Artificial intelligence system and method for prognostic assessment of autoimmune disease

Abstract

Artificial intelligence systems and methods for prognostic assessment of autoimmune disease. Certain purposes and advantages of the present disclosure include methods and systems configured to aggregate and analyze clinical dialog data, patient biometric data, medical record data, blood biomarker test data, and patient biometric data, the present invention relates to a method and apparatus for adjusting increasingly patient-specific metrics when creating dynamic patient phenotypes, thereby enhancing clinical understanding of one or more factors specific to patient health conditions. One or more AI frameworks and engines may facilitate efficient integration of communication-related insights into diagnostic and prognostic digital health resources. Exemplary systems, methods, and apparatus according to the principles herein may include machine learning and deep learning techniques for developing one or more quantitative metrics derived from clinical dialog data.

Description

[0001] Cross - Reference to Related Applications

[0002] This application claims the benefit of priority of U.S. Provisional Application Serial No. 63 / 407,584, filed on September 16, 2022, entitled "Novel Methods and Systems for Classification of Undiagnosed Persons at Risk of Autoimmune Conditions Using Innovative Virtual and Digital Models", the entire content of which is incorporated herein by reference at least in part. Technical Field

[0003] The present disclosure relates to the field of software and digital health systems as medical devices, and more particularly to artificial intelligence systems and methods for prognostic assessment of autoimmune diseases. Background Art

[0004] According to current estimates from the National Institutes of Health, up to 23.5 million people in the United States may have at least one autoimmune disease. However, according to the American Autoimmune Related Diseases Association (AARDA), the actual size of the affected population in the United States is now estimated to be as high as 50 million people. The economic burden caused by autoimmune diseases in the U.S. healthcare system is estimated to be as high as $100 billion annually.

[0005] For many of these patients, diagnosis remains a challenge. In the case of systemic lupus erythematosus (SLE), the average time to diagnosis is well over 7 years. Getting a diagnosis can be a long and challenging process. Although autoimmune diseases have unique characteristics, many observable symptoms (such as fatigue and pain) overlap with more common diseases. Thus, clinicians will strive to rule out other health conditions before considering an autoimmune disease diagnosis. Additionally, laboratory tests for specific autoimmune diseases are still under development. Current best practices, including antinuclear antibody (ANA) tests, measure the presence of general types of antibodies. However, they cannot confirm the presence of an autoimmune disease. Autoimmune diseases follow a relapsing - remitting disease course, which means that symptoms will appear and disappear over time as the underlying disease activity level changes. It is not uncommon for symptoms to have subsided by the time a patient sees a clinician.

[0006] The challenges posed by chronic diseases, while a heavy burden globally, are almost universally exacerbated by poor communication. In particular, those with chronic diseases or at risk of developing them almost always achieve better outcomes if they can communicate their health status better and in a timely manner, while health providers are disadvantaged by limited detailed and timely knowledge of the patient's health progress, knowledge that can dictate timely responses and proactive measures. Summary of the Invention

[0007] A simplified summary of some embodiments of the invention is presented below to provide a basic understanding of the invention. This summary is not an extensive overview of the invention. It is not intended to identify key / important elements of the invention or to delineate the scope of the invention. Its sole purpose is to present some embodiments of the invention in a simplified form as a prelude to the more detailed description that is presented later.

[0008] Certain aspects of the present disclosure provide an artificial intelligence method and system for prognostic assessment of autoimmune diseases. According to certain aspects of the present disclosure, the method and system may include one or more of the following steps or system operations: the one or more steps or system operations for presenting (e.g., at one or more time points, using a dialogue AI agent) multiple clinical dialogue prompts to a patient user. The method and system may also include one or more of the following steps or system operations: the one or more steps or system operations for receiving (e.g., at one or more time points, using a dialogue AI agent) multiple dialogue responses from the patient user in response to the multiple clinical dialogue prompts. The method and system may also include one or more of the following steps or system operations: the one or more steps or system operations for receiving (e.g., using at least one server) a first set of text data including the multiple dialogue responses from the patient user. The method and system may also include one or more of the following steps or system operations: the one or more steps or system operations for receiving (e.g., via at least one application programming interface, using at least one server) a first set of medical record data of the patient user. In certain embodiments, the first set of medical record data of the patient user includes at least one of blood biomarker test data and biometric measurement data. The method and system may also include one or more of the following steps or system operations: the one or more steps or system operations for processing (e.g., using at least one server) the first set of text data and the first set of medical record data of the patient user according to a natural language processing model. In certain embodiments, the natural language processing model is configured to extract one or more features from the first set of text data and the first set of medical record data. In certain embodiments, the natural language processing model is configured to cluster one or more text segments from the first set of text data and the first set of medical record data according to the one or more features. The method and system may also include one or more of the following steps or system operations: the one or more steps or system operations for analyzing, according to the natural language processing model, one or more text segments to determine one or more temporal or contextual associations between the one or more text segments and the blood biomarker test data and / or the biometric measurement data. The method and system may also include one or more of the following steps or system operations: the one or more steps or system operations for assigning one or more prognostic values to the one or more text segments according to at least one output of the natural language processing model. The method and system may also include one or more of the following steps or system operations: the one or more steps or system operations for analyzing (e.g., using at least one server) the one or more prognostic values of the one or more text segments to generate a prognostic assessment for at least one autoimmune disease of the patient user.The method and system may further include one or more of the following steps or system operations: the one or more steps or system operations for providing (e.g., using at least one server) a prognosis assessment to a practitioner user via a graphical user interface of a client device.

[0009] According to certain aspects of the present disclosure, the method and system may further include one or more of the following steps or system operations: the one or more steps or system operations for configuring (e.g., using at least one server) a dialogue AI model according to one or more prognosis values of one or more text segments. In certain embodiments, the dialogue AI model may include a large language model. In certain embodiments, the plurality of clinical dialogue prompts may include a plurality of generative prompts according to the dialogue AI model. In certain embodiments, the prognosis assessment includes a diagnostic assessment for at least one autoimmune disease. In certain embodiments, the prognosis assessment includes a predictive assessment of at least one pathophysiological event associated with at least one autoimmune disease. In certain embodiments, the prognosis assessment includes a clinical recommendation for at least one drug intervention for a patient user. In certain embodiments, the prognosis assessment includes a clinical recommendation for at least one blood biomarker test for a patient user. In certain embodiments, the biometric measurement data includes data from at least one body-worn sensor of a patient user. According to certain aspects of the present disclosure, the method and system may further include one or more of the following steps or system operations: the one or more steps or system operations for modifying the plurality of clinical dialogue prompts via the dialogue AI model according to one or more prognosis values of one or more text segments.

[0010] Other aspects of the present disclosure provide an artificial intelligence method and system for patient phenotyping in autoimmune diseases. According to certain aspects of the present disclosure, the method and system may include one or more of the following steps or system operations: the one or more steps or system operations for presenting (e.g., using a dialogue AI agent communicatively coupled to a first server) a first set of clinical dialogue prompts to a patient user according to a generative AI model. The method and system may include one or more of the following steps or system operations: the one or more steps or system operations for receiving (e.g., using the dialogue AI agent) a first set of responses to the first set of clinical dialogue prompts from the patient user. The method and system may include one or more of the following steps or system operations: the one or more steps or system operations for receiving (e.g., using the first server) a first set of medical record data of the patient, wherein the first set of medical record data includes at least one of blood biomarker test data and biometric measurement data of the patient user. The method and system may include one or more of the following steps or system operations: the one or more steps or system operations for receiving (e.g., using at least one server) a first set of text data, the first set of text data including the first set of responses from the patient user to the first set of clinical dialogue prompts. The method and system may include one or more of the following steps or system operations: the one or more steps or system operations for processing (e.g., using at least one server) the first set of text data and the first set of medical record data according to a natural language processing model. According to certain aspects of the present disclosure, the natural language processing model is configured to extract one or more features from the first set of text data and the first set of medical record data. According to the aspect, the natural language processing model is configured to cluster one or more text segments from the first set of text data and the first set of medical record data according to one or more features. The method and system may include one or more of the following steps or system operations: the one or more steps or system operations for analyzing one or more text segments according to the natural language processing model to determine one or more temporal or contextual associations between the one or more text segments and the blood biomarker test data and / or biometric measurement data. The method and system may include one or more of the following steps or system operations: the one or more steps or system operations for configuring a patient phenotype for the patient user according to at least one output of the natural language processing model according to one or more temporal or contextual associations between the one or more text segments and the blood biomarker test data and / or biometric measurement data. According to certain aspects of the present disclosure, the patient phenotype includes one or more symptoms, markers, and pathological triggers of the autoimmune disease of the patient user.The method and system can include one or more of the following steps or system operations: the one or more steps or system operations are for providing a patient phenotype of a patient user to a practitioner user via a graphical user interface of a client device using at least one server.

[0011] In accordance with certain aspects of the present disclosure, the method and system may further include one or more of the following steps or system operations: the one or more steps or system operations for configuring (e.g., using at least one server) a generative AI model based on a patient phenotype. The method and system may further include one or more of the following steps or system operations: the one or more steps or system operations for presenting (e.g., using a conversational AI agent) a second set of clinical conversation prompts to a patient user based on the generative AI model. In certain embodiments, the second set of clinical conversation prompts is configured based on the patient phenotype (e.g., based on the generative AI model). In accordance with certain aspects of the present disclosure, at least one clinical prompt in the second set of clinical conversation prompts is different from the first set of clinical conversation prompts. The method and system may further include one or more of the following steps or system operations: the one or more steps or system operations for receiving (e.g., using a conversational AI agent) a second set of responses from the patient user to the second set of clinical conversation prompts. The method and system may further include one or more of the following steps or system operations: the one or more steps or system operations for receiving (e.g., using a first server) a second set of medical record data of the patient, wherein the second set of medical record data includes a second set of blood biomarker test data and / or a second set of biometric measurement data of the patient user. The method and system may further include one or more of the following steps or system operations: the one or more steps or system operations for receiving (e.g., using at least one server) a second set of text data, the second set of text data including a second set of responses from the patient user to the second set of clinical conversation prompts. The method and system may further include one or more of the following steps or system operations: the one or more steps or system operations for processing (e.g., using at least one server) the second set of text data according to a natural language processing model. In certain embodiments, the natural language processing model is configured to extract one or more features from the second set of text data according to the patient phenotype. In certain embodiments, the natural language processing model is configured to cluster one or more text segments from the second set of text data according to one or more features. The method and system may further include one or more of the following steps or system operations: the one or more steps or system operations for processing (e.g., using at least one server) the second set of text data and the second set of medical record data according to a natural language processing model. In accordance with certain aspects of the present disclosure, the natural language processing model is configured to extract one or more features from the second set of text data and the second set of medical record data according to the patient phenotype. In certain embodiments, the natural language processing model is configured to cluster one or more text segments from the second set of text data and the second set of medical record data according to one or more features.The method and system may further include one or more of the following steps or system operations: the one or more steps or system operations are for analyzing (e.g., according to a machine learning model) one or more text segments to generate a prognosis evaluation for an autoimmune disease of a patient user. The method and system may further include one or more of the following steps or system operations: the one or more steps or system operations are for providing (e.g., using at least one server) the prognosis evaluation to the patient user via a graphical user interface of an end-user device associated with the patient user, and / or providing the prognosis evaluation to a practitioner user via a graphical user interface of a client device. The method and system may further include one or more of the following steps or system operations: the one or more steps or system operations are for updating or modifying a patient phenotype according to at least one output of a natural language processing model. In certain embodiments, the prognosis evaluation includes one or more recommended actions for managing the autoimmune disease of the patient user.

[0012] Other aspects of the present disclosure provide an artificial intelligence method and system for identifying diagnostic triggers for autoimmune diseases from clinical conversation data. According to certain aspects of the present disclosure, the method and system may include one or more of the following steps or system operations: The one or more steps or system operations are for presenting (e.g., using at least one server communicatively coupled to the first client device) a plurality of clinical conversation prompts to a patient user on a user interface of the first client device. The method and system may further include one or more of the following steps or system operations: The one or more steps or system operations are for receiving (e.g., via the first client device, using at least one server) a plurality of user-generated responses from the patient user to the plurality of clinical conversation prompts, the plurality of user-generated responses to the plurality of clinical conversation prompts including a first set of clinical conversation data. The method and system may further include one or more of the following steps or system operations: The one or more steps or system operations are for receiving (e.g., using at least one server) a first set of medical record data of the patient user, wherein the first set of medical record data includes at least one of blood biomarker test data and biometric measurement data of the patient user. The method and system may further include one or more of the following steps or system operations: The one or more steps or system operations are for processing (e.g., using at least one server) the first set of clinical conversation data and the first set of medical record data according to a natural language processing model. In certain embodiments, the natural language processing model is configured to extract one or more features from the first set of clinical conversation data and the first set of medical record data. The natural language processing model may be configured to cluster one or more text segments from the first set of clinical conversation data and the first set of medical record data according to the one or more features. The method and system may further include one or more of the following steps or system operations: The one or more steps or system operations are for analyzing one or more text segments according to at least one output of the natural language processing model to determine one or more temporal or contextual associations between the one or more text segments and the blood biomarker test data and / or the biometric measurement data. The method and system may further include one or more of the following steps or system operations: The one or more steps or system operations are for configuring, according to at least one output of the natural language processing model and according to one or more temporal or contextual associations between the one or more text segments and the blood biomarker test data and / or the biometric measurement data, one or more diagnostic triggers for the patient user. The method and system may further include one or more of the following steps or system operations: The one or more steps or system operations are for transmitting (e.g., via a network interface, using at least one server) one or more diagnostic triggers of the patient user to a practitioner user via a graphical user interface of a second client device.

[0013] According to certain aspects of the present disclosure, the method and system may further include one or more of the following steps or system operations: the one or more steps or system operations for configuring (e.g., using at least one server) a natural language processing model according to one or more diagnostic triggers. In certain embodiments, a plurality of clinical dialogue prompts are configured according to a generative AI model. In such embodiments, the generative AI model may include a large language model. The method and system may further include one or more of the following steps or system operations: the one or more steps or system operations for configuring (e.g., using at least one server) the generative AI model according to one or more diagnostic triggers. The method and system may further include one or more of the following steps or system operations: the one or more steps or system operations for presenting (e.g., using at least one server communicatively coupled to the first client device) a second or subsequent plurality of clinical dialogue prompts to a patient user on a user interface of the first client device. The method and system may further include one or more of the following steps or system operations: the one or more steps or system operations for receiving (e.g., via the first client device, using at least one server) user-generated second or subsequent plurality of responses from the patient user to the second or subsequent plurality of clinical dialogue prompts, the user-generated second or subsequent plurality of responses to the second or subsequent plurality of clinical dialogue prompts including a second or subsequent set of clinical dialogue data. The method and system may further include one or more of the following steps or system operations: the one or more steps or system operations for processing (e.g., using at least one server) the second or subsequent set of clinical dialogue data according to the natural language processing model to extract one or more features from the second or subsequent set of clinical dialogue data according to one or more diagnostic triggers. The method and system may further include one or more of the following steps or system operations: the one or more steps or system operations for analyzing at least one output of the natural language processing model according to a machine learning model to identify at least one diagnostic trigger from the second or subsequent set of clinical dialogue data. The method and system may further include one or more of the following steps or system operations: the one or more steps or system operations for transmitting (e.g., using at least one server) at least one diagnostic trigger from the second or subsequent set of clinical dialogue data to a practitioner user via a graphical user interface of the second client device. The method and system may further include one or more of the following steps or system operations: the one or more steps or system operations for transmitting (e.g., using at least one server) at least one diagnostic trigger from the second or subsequent set of clinical dialogue data to the patient user at a user interface of the first client device.The method and system may also include one or more of the following steps or system operations: the one or more steps or system operations are for generating at least one clinical recommendation for managing an autoimmune disease based on a machine learning model and at least one diagnostic trigger factor from a second or subsequent plurality of responses generated by a user. The method and system may also include one or more of the following steps or system operations: the one or more steps or system operations are for transmitting (e.g., using at least one server) at least one clinical recommendation for managing an autoimmune disease to a practitioner user via a graphical user interface of a second client device. The method and system may also include one or more of the following steps or system operations: the one or more steps or system operations are for transmitting (e.g., using at least one server) at least one clinical recommendation for managing an autoimmune disease to a patient user at a user interface of a first client device.

[0014] The more relevant and important features of the present invention have been outlined rather broadly above so that the detailed description of the present invention that follows may be better understood and so that the contribution of the present invention to the art may be more fully appreciated. Additional features of the present invention that form the subject matter of the claims of the present invention will be described hereinafter. Those skilled in the art should recognize that the inventive concept and the specific methods and structures disclosed herein can be readily utilized as a basis for modifying or designing other structures for carrying out the same purposes of the present invention. Those skilled in the art should recognize that such equivalent structures do not depart from the spirit and scope of the present invention as set forth in the appended claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Those skilled in the art will understand that the drawings described herein are for illustrative purposes only. It should be understood that in some instances, aspects of the implementations may be exaggerated or enlarged to facilitate understanding of the implementations. In the drawings, like reference numerals generally denote like features, functionally similar and / or structurally similar elements in the various figures. The drawings are not necessarily drawn to scale, but rather focus on illustrating the principles of the teachings. The drawings are not intended to limit the scope of the teachings in any way. The systems, methods, and computer program products of the present disclosure may be better understood from the following illustrative description with reference to the drawings, in which:

[0016] Figure 1 is an illustrative implementation of a computing system through which one or more aspects of the present disclosure may be implemented;

[0017] Figure 2 is an architecture diagram of an artificial intelligence system for prognostic assessment of autoimmune diseases according to certain aspects of the present disclosure;

[0018] Figure 3Flowchart of an artificial intelligence system and method for prognostic assessment of autoimmune diseases according to certain aspects of the present disclosure;

[0019] Figure 4 Functional block diagram of an artificial intelligence system and method for prognostic assessment of autoimmune diseases according to certain aspects of the present disclosure;

[0020] Figure 5 Processing flowchart of an artificial intelligence system and method for prognostic assessment of autoimmune diseases according to certain aspects of the present disclosure;

[0021] Figure 6 Processing flowchart of a routine of an artificial intelligence system and method for prognostic assessment of autoimmune diseases according to certain aspects of the present disclosure;

[0022] Figure 7 Processing flowchart of a routine of an artificial intelligence system and method for prognostic assessment of autoimmune diseases according to certain aspects of the present disclosure;

[0023] Figure 8 Processing flowchart of a routine of an artificial intelligence system and method for prognostic assessment of autoimmune diseases according to certain aspects of the present disclosure;

[0024] Figure 9 Processing flowchart of an artificial intelligence method for prognostic assessment of autoimmune diseases according to certain aspects of the present disclosure;

[0025] Figure 10 Processing flowchart of an artificial intelligence method for prognostic assessment of autoimmune diseases according to certain aspects of the present disclosure; and

[0026] Figure 11 Processing flowchart of an artificial intelligence method for prognostic assessment of autoimmune diseases according to certain aspects of the present disclosure. Detailed Description

[0027] It should be understood that all combinations of the concepts discussed in more detail below (assuming such concepts are not mutually contradictory) are considered to be part of the inventive subject matter disclosed herein. It should also be understood that the terms explicitly employed herein may also appear in any disclosure incorporated by reference, and such terms should be accorded the meaning that is most consistent with the particular concepts disclosed herein.

[0028] The following is a more detailed description of various concepts related to inventive methods, apparatuses, and systems, as well as embodiments of the inventive methods, apparatuses, and systems, which are configured to facilitate the acquisition, management, and practical application of health information obtained from a) medical records, b) biometric analysis, and c) medical communications of the following persons (users or patients): the health conditions of the persons (users or patients) indicate that they would benefit from proactive health monitoring and medical promotion.

[0029] It should be understood that the various concepts introduced above and discussed in more detail below can be implemented in any of a number of ways, as the disclosed concepts are not limited to any particular implementation. Examples of specific implementations and applications are provided primarily for illustrative purposes. The present disclosure should in no way be limited to the exemplary implementations and techniques illustrated in the figures and described below.

[0030] Before describing the present invention and specific exemplary embodiments thereof, it should be understood that the present invention is not limited to the particular embodiments described and may thus vary. It should also be understood that the terms used herein are for the purpose of describing particular embodiments only and are not intended to be limiting, as the scope of the present invention will be limited only by the appended claims.

[0031] When a range of values is provided, it is to be understood that, unless the context clearly dictates otherwise, each intermediate value between the upper and lower limits of that range, down to one-tenth of the lower limit unit, as well as any other stated value or intermediate value within the stated range, is covered by the present invention. The upper and lower limits of these smaller ranges may independently be included in the smaller ranges and are also covered by the present invention, subject to any explicit exclusionary limits within the stated range. Where the stated range includes one or both of the endpoint limits, ranges excluding one or both of those included endpoint limits are also included in the scope of the present invention.

[0032] As used herein, "exemplary" means serving as an example or illustration and does not necessarily denote ideal or optimal.

[0033] As used herein, the terms "computer", "processor", and "computer processor" include personal computers, workstation computers, tablet computers, smart phones, microcontrollers, microprocessors, field programmable object arrays (FPOAs), digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), or any other digital processing engine, device, or equivalent device capable of executing software code, including associated memory devices, transmission devices, indicating devices, input / output devices, displays, and equivalent devices.

[0034] As used herein, the terms "conversation agent" or "conversational AI agent" or "agent" refer to any device, system, and / or program configured to autonomously perform one or more target functions in response to one or more inputs. The terms may be used interchangeably. The one or more inputs may include one or more user-generated inputs, sensor-based inputs, internal system inputs, external system inputs, environmental awareness, etc. Examples of conversation agents may include, but are not limited to, one or more virtual assistants, personal assistants, or chatbots.

[0035] As used herein, the term "mobile device" includes any portable electronic device capable of performing one or more digital functions or operations; including but not limited to smart phones, tablet computers, personal digital assistants, wearable activity trackers, smart watches, smart speakers, etc.

[0036] As used herein, the terms "provider" and "practitioner" refer to healthcare professionals or healthcare providers responsible for one or more aspects of patient care; including but not limited to doctors, nurses, physician assistants, pharmacists, technicians, etc. The terms "provider" and "practitioner" may be used interchangeably throughout the present disclosure. As used herein, the term "practitioner user" refers to a provider / practitioner who is also a user of the systems and methods described herein.

[0037] As used herein, the term "patient" refers to any recipient of healthcare services performed or assisted by a practitioner; including but not limited to individuals suffering from autoimmune diseases. As used herein, the term "patient user" refers to a patient who is also a user of the systems and methods described herein.

[0038] As used herein, the term "comprising" means including but not limited to, and the term "including" means including but not limited to. The term "based on" means "at least partially based on".

[0039] As used herein, the term "interface" refers to any shared boundary across which two or more independent components of a computer system can exchange information. The exchange may occur between software, computer hardware, peripherals, humans, and combinations thereof.

[0040] As used herein, the terms "transmit" or "communicate" and variations thereof mean to transmit digital and / or analog signal information via electronic transmission, Wi-Fi, Bluetooth technology, wireless, wired, or other known transmission technologies including transmission to an Internet website.

[0041] As used herein, the term "biometric" refers to any measurable biological (i.e., anatomical and / or physiological) and / or behavioral characteristic of a human (i.e., a patient). According to certain aspects of the present disclosure, examples of biometric measurements can include, but are not limited to, heart rate and cardiac activity (e.g., pulse and electrocardiogram), sleep data, brain wave data (e.g., MRI and fMRI), activity data (i.e., motion / telemetry), body temperature, blood pressure, and the like.

[0042] The term "program" or "software" is used herein in a general sense to refer to any type of computer code or set of computer-executable instructions that can be used to program a computer or other processor to implement various aspects of the present technology as described above. Additionally, it should be recognized that, according to one aspect of this embodiment, one or more computer programs that execute the methods of the present technology when executed do not need to reside on a single computer or processor, but can be distributed in a modular manner among a plurality of different computers or processors to implement various aspects of the present technology. Computer-executable instructions can take many forms, such as program modules executed by one or more computers or other devices. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Generally, in various embodiments, the functions of program modules can be combined or distributed as desired.

[0043] Certain benefits and advantages of the present disclosure include artificial intelligence methods and systems configured to identify and train diagnosis-related triggers to alert healthcare providers about pathological risks or drug intervention opportunities that arise in the treatment and management of autoimmune diseases.

[0044] Certain objectives and advantages of the present disclosure include artificial intelligence methods and systems for analyzing (e.g., according to one or more machine learning frameworks) the written and oral content of interpersonal communication that contains subjective information about an individual's health status. Embodiments of the present disclosure include methods and systems for acquiring and managing clinical communication data in an easily analyzable manner for diagnostic and prognostic purposes. Embodiments of the present disclosure include methods and systems for systematically leveraging existing information with rigorously quantified medical data (e.g., laboratory tests and other standardized diagnoses).

[0045] Certain objectives and advantages of the present disclosure include artificial intelligence methods and systems for effectively integrating communication-related insights into diagnostic and prognostic digital health resources. Exemplary systems, methods, and apparatuses according to the principles herein may include machine learning and deep learning techniques for developing one or more quantitative metrics derived from clinical dialogue data. Such techniques may include natural language processing (NLP) and generative artificial intelligence (gAI). According to certain aspects of the present disclosure, NLP may include one or more computer-implemented machine learning frameworks for analytically deconvolving free text into informational granules and macro trends. According to certain aspects of the present disclosure, gAI may include one or more artificial neural networks that include the ability to simulate normal human communication. According to certain aspects of the present disclosure, gAI includes a clinical dialogue framework configured as an artificial intelligence vehicle through which productive interactions between medical patients and digital health resources are facilitated, where the digital health resources are designed to improve the passive and active healthcare of such patients.

[0046] Certain objectives and advantages of the present disclosure include the following methods and systems: The methods and systems are configured to converge and analyze clinical dialogue data, patient biometric data, medical record data, blood biomarker test data, and patient biometric data to adjust increasingly patient-specific metrics when creating a dynamic patient phenotype, thereby enhancing the clinical understanding of one or more factors specific to the patient's health condition. According to certain aspects of the present disclosure, the patient phenotype is used to configure and / or modify one or more gAI models to drive one or more gAI communications customized for the patient.

[0047] Certain objectives and advantages of the present disclosure include one or more systems, methods, apparatuses, and digital platform products that include one or more NLP engines and frameworks, and gAI engines and frameworks, for presenting to patients an ideal (i.e., useful and not burdensome) communication resource that facilitates regular information exchange that does not overly burden healthcare providers, while also encouraging patient-specific and context-appropriate inquiries in a manner that one might share with someone they know and trust. According to certain embodiments, data derived from these information exchanges is transmitted (e.g., according to one or more communication protocols) to alert healthcare providers of the need to respond to medical-specific questions or requests from patients, as well as scenarios where NLP-driven AI models detect a possible convergence of multiple situations (e.g., a set of concerning statements may be present in the record and combined with specific recent test results) that may signal adverse events and situations indicating the need for one or more specific treatment interventions.

[0048] Certain objects and advantages of the present disclosure include one or more systems, methods, devices, and digital platform products for facilitating proactive assessment and care of people having an assessed risk of an autoimmune disease (including chronic connective tissue diseases). Although the present disclosure discusses autoimmune diseases with a certain degree of specificity, the systems, methods, devices, and digital platform products may also be applicable to many other disorders, diseases, and impairments.

[0049] Turning now to the drawings, in which like reference numerals represent like elements in several views, Figure 1 an exemplary computing system is depicted in which certain illustrated embodiments of the invention may be implemented.

[0050] Now refer to Figure 1, a processor-implemented computing device is shown in which one or more aspects of the present disclosure may be implemented. According to an embodiment, the processing system 100 generally may include at least one processor 102, or processing unit or multiple processors, a memory 104, at least one input device 106, and at least one output device 108, which are coupled together via a bus or bus group 110. In some embodiments, the input device 106 and the output device 108 may be the same device. An interface 112 may also be provided for coupling the processing system 100 to one or more peripheral devices. For example, the interface 112 may be a PCI card or a PC card. At least one storage device 114 that houses at least one database 116 may also be provided. The memory 104 may be any form of memory device, such as volatile or non-volatile memory, solid-state storage devices, magnetic devices, etc. The processor 102 may include more than one different processing device, for example, to handle different functions within the processing system 100. The input device 106 receives input data 118 and may include, for example, a keyboard, a pointing device such as a pen device or a mouse, an audio receiving device for voice control activation such as a microphone, a data receiver or antenna such as a modem or a wireless data adapter, a data acquisition card, etc. The input data 118 may come from different sources, such as keyboard instructions and data received via a network. The output device 108 generates or produces output data 120 and may include, for example, a display device or a monitor (in which case the output data 120 is visual), a printer (in which case the output data 120 is printed), a port such as a USB port, a peripheral component adapter, a data transmitter or antenna such as a modem or a wireless network adapter, etc. The output data 120 may be different and derived from different output devices, such as a visual display on a monitor and data sent to a network. A user may view the data output or an interpretation of the data output, for example, on a monitor or using a printer. The storage device 114 may be any form of data or information storage means, such as volatile or non-volatile memory, solid-state storage devices, magnetic devices, etc.

[0051] In use, the processing system 100 is adapted to allow data or information to be stored in at least one database 116 via a wired or wireless communication device, and / or to allow retrieval of data or information from the at least one database 116. The interface 112 may allow wired and / or wireless communication between a processing unit 102 for a specific purpose and peripheral components. Generally, the processor 102 may receive instructions as input data 118 via the input device 106 and may display the processing result or other output to a user by utilizing the output device 108. More than one input device 106 and / or output device 108 may be provided. It should be understood that the processing system 100 may be any form of terminal, server, dedicated hardware, etc.

[0052] It should be understood that the processing system 100 can be part of a networked communication system. The processing system 100 can be connected to a network such as the Internet or a WAN. The input data 118 and the output data 120 can be transmitted via the network to other devices. Transmitting information and / or data over the network can be achieved using wired communication devices or wireless communication devices. A server can facilitate the data transfer between the network and one or more databases. The server and one or more databases provide examples of information sources.

[0053] Therefore, Figure 1 The illustrated processing computing system environment 100 can operate in a networked environment using logical connections to one or more remote computers. The remote computers can be personal computers, servers, routers, network PCs, peer devices, or other common network nodes, and typically include many or all of the elements described above.

[0054] It should also be understood that Figure 1 the logical connections depicted in include local area networks (LANs) and wide area networks (WANs), but can also include other networks such as personal area networks (PANs). Such networked environments are common in offices, enterprise-wide computer networks, intranets, and the Internet. For example, when used in a LAN networked environment, the computing system environment 100 is connected to the LAN via a network interface or adapter. When used in a WAN networked environment, the computing system environment typically includes a modem or other device for establishing communication over a WAN such as the Internet. A modem, which can be internal or external, can be connected to the system bus via a user input interface or via other appropriate mechanisms. In a networked environment, program modules depicted relative to the computing system environment 100 or portions thereof can be stored in a remote memory storage device. It should be understood that Figure 1 the illustrated network connections are exemplary, and other devices for establishing communication links between multiple computers can be used.

[0055] Figure 1 It is intended to provide a brief general description of an illustrative and / or suitable exemplary environment in which embodiments of the present invention described below can be implemented. Figure 1 is an example of a suitable environment and is not intended to impose any limitation on the structure, scope of use, or functionality of embodiments of the present invention. A particular environment should not be construed as having any dependency or requirement related to any one or combination of the components shown in the exemplary operating environment. For example, in some cases, one or more elements in the environment may be considered unnecessary and omitted. In other cases, one or more other elements may be considered necessary and added.

[0056] In the following description, reference may be made to the acts and symbolic representations of operations performed by one or more computing devices (e.g., Figure 1 computing system environment 100) to describe certain embodiments. Accordingly, it should be understood that such acts and operations are sometimes referred to as computer-executed, which includes operations by a processor of a computer on electrical signals representing data in a structured form. The operations transform the data or maintain the data at locations in the computer's memory system, which reconfigures or otherwise changes the operation of the computer in a manner understood by those skilled in the art. The data structures in which the data is maintained are physical locations in memory having specific properties defined by the data format. However, while embodiments are described in the foregoing context, this is not meant to be limiting, as those skilled in the art will understand that the acts and operations described hereinafter can also be implemented in hardware.

[0057] Embodiments can be implemented in many other general or special purpose computing devices and computing system environments or configurations. Examples of well-known computing systems, environments, and configurations suitable for use with embodiments include, but are not limited to, personal computers, hand-held or laptop devices, personal digital assistants, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, networks, minicomputers, server computers, game server computers, web server computers, mainframe computers, and distributed computing environments including any of the above systems or devices.

[0058] Embodiments can be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Embodiments can also be practiced in a distributed computing environment where tasks are performed by remote processing devices linked through a communications network. In a distributed computing environment, program modules can be located in both local and remote computer storage media including memory storage devices.

[0059] The exemplary computing system environment 100 has been generally shown and discussed above. Now, turning to the embodiments of the present invention, the embodiments of the present invention generally relate to systems and methods for presenting and analyzing clinical dialogue data to assist in the prognosis assessment and personalized management of autoimmune diseases. Figure 1

[0060] Now refer to Figure 2 ​, which shows an architecture diagram of an artificial intelligence system 200 for prognostic assessment of autoimmune diseases. According to certain aspects of the present disclosure, system 200 includes a computing architecture 204, which includes an artificial intelligence analysis framework for acquiring, managing, and incrementally learning information and associations in patient profiles, communications, and medical records. According to certain embodiments, system 200 may include a patient client device 234, a practitioner client device 238, and one or more electronic health record (EHR) servers 236 communicatively coupled to one or more components of computing architecture 204 via a network communication interface 232. According to certain aspects of the present disclosure, system 200 enables one or more practitioner users 23 to proactively evaluate various data points to improve diagnostic time and personalized management of one or more autoimmune diseases of patient user 21. One or more autoimmune diseases may include, but are not limited to, SLE, rheumatoid arthritis, scleroderma, polymyositis, Sjogren's syndrome, Raynaud's syndrome, and mixed connective tissue disease. According to certain aspects of the present disclosure, patient client device 234 may facilitate the exchange of patient-specific data to / from computing architecture 204 via network communication interface 232. Patient client device 234 may be communicatively coupled to one or more of a physiological measurement device 240 and a body-worn sensor device 242 via a wireless (e.g., Bluetooth) or wired (e.g., USB) interface. Physiological measurement device 240 and body-worn sensor device 242 may be configured to collect multiple biometric data from patient user 21. According to certain aspects of the present disclosure, biometric data may include heart rate data, heart rate variability data, ECG data, sleep data, activity (i.e., movement / telemetry) data, blood pressure, body temperature, pulse oximetry, etc. In certain embodiments, physiological measurement device 240 and body-worn sensor device 242 may be communicatively coupled directly to computing architecture 204 via network communication interface 232 (e.g., without a communication interface and patient client device 234). According to certain embodiments, patient client device 234 may send biometric data from physiological measurement device 240 and body-worn sensor device 242 to practitioner client device 238 via network communication interface 232 (e.g., according to one or more data transfer protocols). According to certain aspects of the present disclosure, EHR server 236 may include electronic health record (EHR) data 248 and / or laboratory test data 246 of patient user 21 stored thereon. EHR data 248 may include multiple longitudinal health data of patient user 21, as well as multiple EHR data related to the diagnosis, management, and treatment of one or more autoimmune diseases specific to user 21. Laboratory test data 246 may include blood biomarker test data related to the diagnosis, management, and treatment of one or more autoimmune diseases of user 21.The EHR server 236 may transmit (e.g., according to one or more data transfer protocols) the EHR data 248 and / or the laboratory test data 246 to the computing architecture 204 via the network communication interface 232. According to certain embodiments, the EHR server 236 may send the EHR data 248 and / or the laboratory test data 246 to the practitioner client device 238 via the network communication interface 232, and receive the EHR data 248 and / or the laboratory test data 246 from the practitioner client device 238 (e.g., according to one or more data transfer protocols).

[0061] According to certain aspects of the present disclosure, the computing architecture 204 may include an Application Programming Interface (API) gateway 205 configured to facilitate data transfer between the computing architecture 204 and one or more other elements of the system 200. In certain embodiments, the API gateway 205 may include a lambda function 206 and an integration service 208. The lambda function 206 includes event-driven functions for managing the computing resources of the computing architecture 204. In various embodiments, the lambda function 206 (or an equivalent function) is configured to scale the runtime environment, execute one or more functions (e.g., process medical record uploads), and scale down the runtime capabilities as needed to efficiently manage computing resources. Examples of the lambda function 206 include AWS LAMBDA available from AMAZON WEB SERVICES of Seattle, Washington. The integration service 208 may include a two-way data transfer interface for managing data streams to / from the computing architecture 204 and one or more elements of the system 200. Examples of the integration service 208 may include AMAZON APPFLOW available from AMAZON WEB SERVICES of Seattle, Washington. According to certain aspects of the present disclosure, the computing architecture 204 may further include one or more network service functions 210 to 214. In certain embodiments, the first network service function 210 may include a simple storage service function configured to provide object storage for the computing architecture 204. The first network service function 210 may include a bucket storage architecture and may be configured to manage the storage and routing of multiple object files (e.g., medical record data and laboratory test data). Examples of the first network service function 210 may include AMAZONS3 available from AMAZON WEB SERVICES of Seattle, Washington. In certain embodiments, the second network service function 212 may include a real-time data processing function. The second network service function 212 may include a scalable and persistent real-time data streaming service for real-time capturing and processing data from multiple sources. The second network service function 212 may implement a real-time data transfer interface between patient client devices 234, physiological measurement devices 240, body-worn sensor devices 242, etc. Examples of the second network service function 212 may include AMAZON KINESIS available from AMAZON WEB SERVICES of Seattle, Washington. In certain embodiments, the third network service function 214 may include a relational database function for setting up, operating, and scaling one or more relational databases for use in the care management application 230 (as described in more detail below). Examples of the third network service function 214 may include AMAZON RDS available from AMAZON WEB SERVICES of Seattle, Washington.

[0062] According to certain aspects of the present disclosure, the computing architecture 204 may include an Optical Character Recognition (OCR) engine 216. The OCR engine 216 may be communicatively coupled to the first network service function 210 to receive one or more medical record files or laboratory test data files. The one or more medical record files or laboratory test data files may include scanned PDF documents and / or image file formats. The OCR engine 216 may be operatively configured to process the one or more medical record files or laboratory test data files according to multiple pattern matching algorithms to enable extraction of data from printed or handwritten text in the scanned documents or image files. The OCR engine 216 may be operatively configured to convert the text into a machine-readable form for further data processing (e.g., by the AI engine 218). According to certain aspects of the present disclosure, the computing architecture 204 may include an Artificial Intelligence (AI) engine 218. The AI engine 218 may be communicatively coupled to receive multiple data streams / inputs from the first network service function 210, the second network service function 212, the third network service function 214, and / or the OCR engine 216. The AI engine 218 may include one or more AI frameworks (i.e., models) for acquiring, managing, and incrementally learning information and relationships in patient profiles, communications, and medical records according to one or more aspects of the system 200. According to certain embodiments, the AI engine 218 may include one or more sub-engines, including a Generative AI (gAI) engine 220, a Natural Language Processing (NLP) engine 222, and a Machine Learning (ML) engine 224. According to certain aspects of the present disclosure, the gAI engine 220 includes a neural network architecture configured to identify patterns and structures within existing data to generate new and original content (e.g., clinical dialogue interactions). In certain embodiments, the gAI engine 220 includes a large language model. In certain embodiments, the gAI engine 220 is configured to generate clinical dialogue content to facilitate multi-turn dialogue interactions between the dialogue AI agent 228 and the patient user 21. According to certain aspects of the present disclosure, the gAI engine 220 encompasses a class of computing techniques that aim to authentically simulate human expression (e.g., communication) by assimilating a given context (e.g., a communication prompt such as "I felt dizzy when I woke up this morning. Should I be worried?") with a prescribed context (e.g., the response should be commensurate with that of a medical professional trained in immuno-metabolic diseases), and should adhere to specific semantic rules (e.g., the response should be friendly and caring, and should seek supportive information while avoiding aggressive or intrusive questioning behavior).The way to train this simulation behavior requires applying deep learning algorithms to process a large number of previously compiled examples of human expressions, and the examples of human expressions should address both context (i.e., a large number of formal and informal literatures of many examples of terms and concepts in which immunometabolic physiology and pathology are conveyed) and semantics (i.e., specific examples of effective communication and interrogation to be emulated, and other examples of undesirable communication to be avoided). According to certain aspects of the present disclosure, the gAI engine 220 includes a gAI model that has been rigorously trained (by exposure to examples of best practice medical communication) to encourage patient input and its tendency to interact with the normal "bedside manner" interaction mode of the patient user 21, and this interaction mode combines medical-related topics with non-technical "chat", where the non-technical "chat" can provide additional topic clues about the mood or health of the participant / patient. According to certain aspects of the present disclosure, the gAI model is configured to direct the conversation towards evaluating specific medical-related interrogations while avoiding any suggestive bias towards any specific answer.

[0063] In accordance with certain aspects of the present disclosure, the NLP engine 222 includes one or more NLP models, the one or more NLP models including one or more NLP algorithms configured to process text data derived from medical record data (e.g., received from the OCR engine 216) and patient dialogue data (e.g., in response to multiple generative outputs from the gAI engine 220) to derive one or more data subsets that are directly (or statistically likely) related to guidelines for reporting the disease-related health status of the patient user 21. According to certain embodiments, the NLP engine 222 includes one or more NLP models, the one or more NLP models including one or more NLP algorithms configured to parse the text data such that, according to context-sensitive guidelines (e.g., interested in an "expression", but in a specific context of elevated or suppressed expression of a given protein marker, rather than in all other contexts where the word might be used), text granules (phrases, sentences, paragraphs, etc.) can be targeted for extraction. According to certain embodiments, the NLP engine 222 includes one or more NLP models, the one or more NLP models including one or more NLP algorithms configured to analyze the text data to generate one or more quantitative metrics, where the quantification can evaluate functional similarity to the original query, temporal or spatial proximity to other queries within the document, or the corresponding sentiment context to be suggested, to, for example, assign a prognostic value to a given term for the patient user 21. Such quantification supports systematic parsing of text documents such that text insights granules can be grouped with other forms of data (e.g., test results or biometric measurements) over time or by situation. Such grouping enables associations to be formed such that text granules frequently observed at or near the time of key medical instances (e.g., onset, recurrence, new diagnosis, etc.) can be considered to have a relatively high prognostic value for future monitoring and clinical management of the patient 21.

[0064] According to certain aspects of the present disclosure, the ML engine 224 includes one or more ML models, the one or more ML models including one or more ML algorithms, the one or more ML algorithms being configured to process MR and conversation data and one or more outputs from the NLP engine 222 to generate one or more clinical recommendations, clinical insights, drug interventions, laboratory test recommendations, and / or facilitate one or more interactions between the patient user 21 and the provider user 23. Exemplary ML models that can be incorporated into the ML engine 224 can include, but are not limited to: deep learning models such as deep Boltzmann machines, deep belief networks, recurrent neural networks (RNNs), fully convolutional neural networks (FCNs), dilated residual networks (DRNs), generative adversarial networks (GANs), and deep neural networks (DNNs); ensembles such as random forests, gradient boosting machines, boosting, adaboosting, stacking generalization, and gradient boosting regression trees; neural networks such as perceptrons, backpropagation, Hopfield neural networks, ridge regression, LASSO, and elastic net; rule systems such as cubist, one rule, and zero rule; linear regression such as ordinary least squares regression, stepwise regression, multivariate adaptive regression splines, locally estimated scatterplot smoothing, and logistic regression; Bayesian such as naive Bayes, average one-dependence estimators, Gaussian naive Bayes, and multinomial naive Bayes; decision trees such as classification and regression, iterative dichotomiser 3, and conditional decision; instance-based such as k-nearest neighbor, learning vector quantization, and locally weighted learning; and clustering such as k-means, k-medians, expectation maximization, and hierarchical clustering.

[0065] According to certain aspects of the present disclosure, the computing architecture 204 may further include an application database 226 communicatively coupled to the AI engine 218 to facilitate data transfer and storage between one or more of the first network service function 210, the second network service function 212, the third network service function 214, and the OCR engine 216. The application database 226 may store one or more model configurations for the gAI engine 220, the NLP engine 222, and the ML engine 224, and / or model outputs from the gAI engine 220, the NLP engine 222, and the ML engine 224. According to certain embodiments, the AI engine 218 may be communicatively coupled to a conversational AI agent 228 configured to present one or more generative clinical dialogue prompts to the patient user 21 and facilitate one or more multi-round dialogue interactions with the patient user 21 (e.g., via the patient user instance 228'). The patient user instance 228' of the conversational AI agent 228 may be configured to present generative clinical dialogue prompts to the patient user 21 via the user interface of the patient user device 234 and may be configured to receive multiple patient responses to the generative clinical dialogue prompts via at least one input device of the patient user device 234. The computing architecture 204 may further include a care management application 230. The care management application 230 may include multiple application modules, functions, and processor-executable operations for personalized management of one or more autoimmune diseases of the patient user 21. According to certain aspects of the present disclosure, the care management application 230 may be operatively coupled to the AI engine 218 to provide one or more diagnostic insights, clinical recommendations, clinical insights, medication interventions, laboratory test recommendations to the patient user 21 (e.g., via the patient user application instance 230') and the provider user 23 (e.g., via the practitioner user application instance 230"), and / or facilitate one or more communications or real-time interactions between the patient user 21 and the provider user 23.

[0066] Now referring to Figure 3 , a flowchart of an artificial intelligence system 300 for prognostic assessment of autoimmune diseases is shown. According to certain aspects of the present disclosure, the system 300 is equivalent to as Figure 2The system 200 shown and described. In accordance with certain aspects of the present disclosure, system 300 illustrates the data flow and associated operations from data source 302 to application computing environment 304 to client interface 306. In accordance with certain embodiments, data source 302 includes a conversational AI agent 308 (i.e., chatbot), a wearable sensor device 310, a physiological sensor device 312, patient medical record data 314, and laboratory test data 316. System 300 is configured to converge multiple data types across data source 302 to receive and process multiple patient-specific data within application computing environment 304. Patient-specific data includes patient-reported data from various applications, sensor-based information from wearable devices, medical records, survey and questionnaire data, and other external data. In accordance with certain aspects of the present disclosure, application computing environment 304 is configured to receive and process data via network service 318. Network service 318 may process the data and provide the data to application server 320. Application server 320 may include one or more gAI engines, natural language processing engines, and machine learning engines (e.g., deep learning engines) for analyzing the data and is set in an iterative learning and improvement mode. The natural language processing engine, gAI engine, and machine learning engine are configured to analyze the data to generate intelligent recommendations for patient identification, treatment, and other interventions and track the results. Recommendations for patient identification, treatment, and other interventions may be communicated to one or more client devices 322. Patient outcome data and other patient management data may be presented at one or more graphical user interfaces 324.

[0067] Now referring to Figure 4 , a functional block diagram 400 of an artificial intelligence system and method for prognostic assessment of autoimmune diseases is shown. In accordance with certain aspects of the present disclosure, the artificial intelligence system for prognostic assessment of autoimmune diseases includes the system 200 as Figure 2 shown and described. In accordance with certain aspects of the present disclosure, system computing module 401 may be configured to receive / ingest multiple data inputs including medical records and laboratory test data 402 and clinical dialogue data 404. In accordance with certain aspects of the present disclosure, system computing module 401 may be as Figure 2Execute within the computing architecture 204 shown and described. According to certain aspects of the present disclosure, the system computing module 401 may be configured to receive and process medical records and laboratory test data 402 and clinical dialogue data 404 at the AI engine 406. The AI engine 406 may be configured to execute one or more gAI models, NLP models, and ML models according to blocks 408 to 414. According to certain embodiments, the system 400 may be configured to generate multiple gAI prompts (block 408) according to the gAI model of the AI engine 406. The multiple gAI prompts may drive multiple multi-round dialogue interactions with the patient user to derive clinical dialogue data 404 via the AI dialogue agent. The clinical dialogue data 404 may be processed according to the NLP model (block 410) to identify one or more temporal and / or contextual associations between the medical records and laboratory test data 402 and the clinical dialogue data 404. The NLP model may be continuously improved according to one or more temporal and / or contextual associations between the medical records and laboratory test data 402 and the clinical dialogue data 404 (block 410). One or more outputs of the NLP model may be analyzed by at least one machine learning model or deep learning model to derive one or more quantitative metrics for deriving one or more clinical recommendations, clinical insights, drug interventions, laboratory test recommendations, and / or facilitating one or more patient-practitioner interactions. The output of block 410 may be used to drive the feedback loop at block 414 to further improve / develop one or more AI models (e.g., gAI models, NLP models, and / or ML models).

[0068] According to certain aspects of the present disclosure, the system 400 facilitates adaptive improvement such that an increasing amount of disease-specific information (e.g., medical records, test results, biometric observations, and key text phrases, concepts, and relationships therein) can be regularly subjected to feature selection analysis to identify specific information features or combinations thereof that tend to occur concurrently with or precede important changes in the patient's state. This allows the existing AI models within the system 400 to be gradually improved, which enhances both the specificity and accuracy of these AI models in characterizing the patient's state and flagging new situations that may require medical intervention. Additionally, a set of spurious metrics (measurements, observations, or text instances not considered disease-related) may be maintained in parallel and periodically explored using feature selection to possibly re-determine whether previously unrecognized features should be monitored for potential disease relevance. This facilitates pathophysiological learning and drug learning as a byproduct of continuous monitoring.

[0069] In accordance with certain aspects of the present disclosure, the system computing module 401 may drive one or more system outputs 416-424. The system outputs may enable a plurality of practical application functions for one or more stakeholder users of the care management application for the personalized management of a patient's autoimmune disease. According to certain embodiments, the output of the system computing module 401 may include a plurality of provider management outputs 416, which include one or more clinical recommendations, clinical insights, pharmaceutical interventions, laboratory test recommendations for the provider users of the system 400. According to certain embodiments, the output of the system computing module 401 may include a clinical research / trial output 418 that includes recommendations for one or more participant recruitment / enrollment, such as based on an analysis of medical records and laboratory test data 402 and clinical dialogue data 404. According to certain embodiments, the output of the system computing module 401 may include a prescription and dosage management output 420 that includes recommendations for pharmaceutical intervention or prescription titration for patient users and / or provider users, such as based on an analysis of medical records and laboratory test data 402 and clinical dialogue data 404. According to certain embodiments, the output of the system computing module 401 may include a plurality of patient insights 422, which include one or more activity or behavior recommendations, patient phenotypes, pharmaceutical recommendations, laboratory test recommendations, and pathophysiological insights for the patient users of the system 400. According to certain embodiments, the output of the system computing module 401 may include facilitation of one or more patient-provider interactions 424; for example, automatically scheduling virtual or in-person medical appointments, phone calls, emails, text messages, or other communications.

[0070] Now referring to Figure 5 , a diagram of a processing flow 500 of an artificial intelligence system and method for prognostic assessment of an autoimmune disease is shown. The operations of the processing flow 500 may be performed in the presented order, a different order, or simultaneously. Additionally, in some exemplary embodiments, some of the operations may be omitted, added, modified, skipped, etc. without departing from the scope of the present invention. According to certain aspects of the present disclosure, the operations of the processing flow 500 may be embodied as as Figure 2One or more system routines of the system 200 shown and described. According to certain aspects of the present disclosure, the processing flow 500 may include one or more steps or operations for screening patients according to a plurality of generative prompts generated by a generative AI model (step 502). Step 502 may include a plurality of multi-round interactions between a dialogue AI agent (e.g., a chatbot) and the patient. The dialogue AI agent may be configured to receive (e.g., via voice or text) a plurality of patient-generated responses to the plurality of generative prompts. The processing flow 500 may continue by performing one or more steps or operations for receiving and processing the plurality of patient-generated responses according to a natural language processing model to evaluate the degree of risk that the patient exhibits one or more symptoms or indications of an autoimmune disease (step 504). The processing flow 500 may also include one or more steps or operations for analyzing the plurality of patient-generated responses and / or one or more outputs of the natural language processing model according to at least one machine learning or deep learning framework to improve the patient's risk level and / or provide a recommended diagnosis for the patient (step 506). The processing flow 500 may also include one or more steps or operations for providing the patient with one or more remote management tools for managing the diagnosed autoimmune disease (step 508). Step 508 may include one or more steps or operations for continuously collecting data via one or more modalities, the one or more modalities including sensor data, biometric data, clinical dialogue data (e.g., according to the gAI model), blood biomarker test data, medical record data, etc. The processing flow 500 may also include one or more steps or operations for providing the patient and / or one or more practitioners or stakeholder users with one or more recommended interventions for managing the diagnosed autoimmune disease (step 510). The processing flow 500 may also include one or more steps or operations for continuously monitoring and processing patient data to track the patient's disease state (e.g., progression, improvement, etc.) (step 512). The processing flow 500 may also include one or more steps or operations for temporarily or according to one or more predetermined intervals or milestones, analyzing the system data to evaluate one or more clinical outcomes of the patient (step 514).

[0071] Now refer to Figure 6, shows a processing flow diagram of routine 600 for an artificial intelligence system and method for prognostic assessment of autoimmune diseases. The operations of routine 600 may be performed in the presented order, a different order, or simultaneously. Additionally, in some exemplary embodiments, some of the operations may be omitted, added, modified, skipped, etc. without departing from the scope of the present invention. According to certain aspects of the present disclosure, routine 600 may be embodied within one or more routines or operations of system 200 as shown and described in Figure 2 . Routine 600 may facilitate one or more steps of processing flow 500 as shown in Figure 5 . According to certain aspects of the present disclosure, routine 600 may include one or more steps or operations 602 to 616 for evaluating the risk of undiagnosed autoimmune disease in a patient user.

[0072] According to certain aspects of the present disclosure, routine 600 may include one or more steps or operations for presenting a plurality of clinical conversation prompts to a patient user via a conversational AI agent (e.g., according to a generative AI model) (step 602), and receiving a plurality of conversation responses from the patient user via the conversational AI agent (step 604). In certain embodiments, steps 602 to 604 include a plurality of multi-round (i.e., conversational) interactions between the patient user and the conversational AI agent. Routine 600 may proceed by performing one or more steps or operations for processing the plurality of conversation responses received from the patient user according to an NLP model (step 606). According to certain aspects of the present disclosure, the NLP model is configured to extract one or more features from the plurality of conversation responses and cluster one or more text segments from the plurality of conversation responses according to the one or more features. Routine 600 may proceed by performing one or more steps or operations for evaluating the risk that the patient user has an undiagnosed autoimmune disease according to a machine learning model (step 608). In certain embodiments, routine 600 may evaluate the risk that the patient user has an undiagnosed autoimmune disease according to the output of the NLP model (i.e., without a separate analysis by the machine learning model). According to certain aspects of the present disclosure, routine 600 may include a decision step 610 for determining whether the patient user is at risk (i.e., reaches a specified risk threshold) based on the output of step 608. If no (i.e., based on the plurality of conversation responses, the patient user does not reach the specified risk threshold), then routine 600 proceeds by performing one or more steps or operations for providing a generative response to the patient user indicating that the patient user does not exhibit a risk of autoimmune disease (e.g., via the conversational AI agent) (step 612). If yes (i.e., based on the plurality of conversation responses, the patient user reaches the specified risk threshold), then routine 600 proceeds by performing one or more steps or operations for providing a generative response to the patient user (e.g., via the conversational AI agent) to provide a risk profile or diagnostic analysis to the patient user (step 614). According to certain aspects of the present disclosure, routine 600 may proceed by performing one or more steps or operations for configuring one or more account parameters for the patient user within a care management application such that the patient user can continue one or more subsequent computerized interactions according to an artificial intelligence diagnostic framework (step 616).

[0073] Now referring to Figure 7, a processing flowchart of routine 700 for an artificial intelligence system and method for prognostic assessment of autoimmune diseases is shown. The operations of routine 700 may be performed in the presented order, a different order, or simultaneously. Additionally, in some exemplary embodiments, some of the operations may be omitted, added, modified, skipped, etc. without departing from the scope of the present invention. Routine 700 may be consecutive or sequential with one or more steps or operations of routine 600 (as Figure 6 shown), and / or may include one or more sub-steps or sub-operations of routine 600. According to certain aspects of the present disclosure, routine 700 may be embodied within one or more routines or operations of system 200 as Figure 2 shown and described. Routine 700 may facilitate one or more steps of processing flow 500 as Figure 5 shown. According to certain aspects of the present disclosure, routine 700 may include one or more steps or operations 702 to 714 for analyzing a plurality of medical record data and clinical conversation data to derive a patient phenotype or other patient-specific insights for managing the autoimmune disease of a patient user. According to certain aspects of the present disclosure, one or more steps or operations 702 to 714 are after step 616 in Figure 6 .

[0074] According to certain aspects of the present disclosure, routine 700 may include one or more steps or operations for receiving and aggregating a plurality of medical record data (e.g., including a plurality of medical records of a patient user) at an application server or a distributed computing environment (step 702). Routine 700 may be performed by executing one or more steps or operations for processing the medical records (e.g., one or more scanned PDF or image files) using an OCR engine to extract (i.e., convert) a plurality of text data from the medical records (step 704). Routine 700 may be performed by executing one or more steps or operations for processing the medical record data (i.e., the converted text data) and clinical conversation data according to a natural language processing model (e.g., Figure 6(multiple conversation responses received by the patient and / or one or more additional conversation responses) (step 706). In some embodiments, the natural language processing model is configured to extract one or more features from the clinical conversation data and the medical record data, and cluster one or more text segments from the clinical conversation data and the medical record data according to the one or more features. According to some aspects of the present disclosure, routine 700 may be performed by executing one or more steps or operations for analyzing one or more text segments to determine one or more temporal or contextual associations between the one or more text segments to generate one or more quantitative data metrics (step 708). According to some aspects of the present disclosure, routine 700 may generate one or more quantitative data metrics according to at least one machine learning engine. In some embodiments, the quantitative data metrics include one or more prognostic values associated with one or more text segments. According to some embodiments, step 708 may include one or more steps or operations for analyzing one or more prognostic values of one or more text segments to generate a prognostic assessment for at least one autoimmune disease of the patient user. According to some aspects of the present disclosure, routine 700 may be performed by executing one or more steps or operations for updating and / or modifying (i.e., improving) one or more aspects of the NLP model according to the data metrics (step 710). Routine 700 may also include one or more steps or operations for updating and / or modifying (i.e., improving) one or more aspects of the gAI model according to the data metrics (step 712). According to some aspects of the present disclosure, routine 700 may be performed according to one or more steps or operations of an artificial intelligence patient management framework (step 714).

[0075] Now referring to Figure 8 , a process flow diagram of routine 800 of an artificial intelligence system and method for prognostic assessment of autoimmune diseases is shown. The operations of routine 800 may be performed in the presented order, in a different order, or simultaneously. Additionally, in some exemplary embodiments, some of the operations may be omitted, added, modified, skipped, etc. without departing from the scope of the present invention. Routine 800 may be consecutive or sequential with one or more steps or operations of routine 600 and / or routine 700 (as shown in Figure 6 and Figure 7 respectively), and / or may include one or more sub-steps or sub-operations of routine 600 and / or routine 700. According to some aspects of the present disclosure, routine 800 may be embodied as in Figure 2within one or more routines or operations of the system 200 shown and described. The routine 800 may facilitate one or more steps of the processing flow 500 as Figure 5 shown. In accordance with certain aspects of the present disclosure, the routine 800 may include one or more steps or operations 802 to 824 for analyzing multiple medical record data and clinical conversation data to obtain one or more interventions or clinical recommendations for treating and / or managing an autoimmune disease of a patient user. In accordance with certain aspects of the present disclosure, one or more steps or operations 802 to 824 are after Figure 7 step 714 in

[0076] According to certain aspects of the present disclosure, routine 800 may include one or more steps or operations for presenting a plurality of clinical dialogue prompts (e.g., according to a generative AI model) to a patient user via a dialogue AI agent (step 802), and receiving a plurality of dialogue responses from the patient user via the dialogue AI agent (step 804). The plurality of dialogue responses include a plurality of clinical dialogue data. In certain embodiments, steps 802 to 804 include a plurality of multi-round (i.e., conversational) interactions between the patient user and the dialogue AI agent. Routine 800 may be carried out by performing one or more steps or operations for receiving a plurality of patient medical data via one or more data sources. According to certain embodiments, the patient medical data may include a plurality of patient biometric data and / or physiological sensor data 808, medical record data 810, and / or blood biomarker test data 812. Routine 800 may be carried out by performing one or more steps or operations for processing the patient medical data and the clinical dialogue data according to an NLP model to extract one or more features from the clinical dialogue data and the medical record data, and clustering one or more text segments from the clinical dialogue data and the medical record data according to the one or more features (step 814). In certain embodiments, step 814 may further include processing the medical record data 810 and / or the blood biomarker test data 812 according to an OCR engine to convert or extract a plurality of texts from the data for analysis by an NLP engine. Routine 800 may be carried out by performing one or more steps or operations for analyzing one or more outputs of step 814 according to an ML engine to generate one or more quantitative metrics for the clinical dialogue data based on one or more temporal or contextual associations between the clinical dialogue data and the patient medical data (step 816). According to certain embodiments, routine 800 may include one or more steps or operations for updating or modifying (i.e., improving) the gAI model and / or the NLP model based on the output of step 816 (step 818). Routine 800 may be carried out by performing one or more steps or operations for processing one or more outputs of step 816 to generate one or more clinical recommendations (step 820). According to certain embodiments, one or more clinical recommendations may include one or more prognostic or diagnostic insights for the patient. In certain embodiments, one or more clinical recommendations may include a patient phenotype that includes one or more personalized pathophysiological insights for the patient. In certain embodiments, one or more clinical recommendations may include one or more recommended drug interventions.In some embodiments, one or more clinical recommendations may include a predictive evaluation of at least one pathophysiological event associated with an autoimmune disease. In some embodiments, one or more clinical recommendations may include one or more behavioral or environmental recommendations for a patient user. In some embodiments, one or more clinical recommendations may include a clinical recommendation for at least one blood biomarker test for a patient. Routine 800 may be performed by executing one or more steps or operations for communicating clinical recommendations and interventions to a patient user (step 822) and a practitioner user (step 824).

[0077] Now referring to Figure 9 , a process flow diagram of an artificial intelligence method 900 for prognostic assessment of an autoimmune disease is shown. The steps or operations of method 900 may be performed in the presented order, a different order, or simultaneously. Additionally, in some exemplary embodiments, some of the operations may be omitted, added, modified, skipped, etc., without departing from the scope of the present invention. According to certain aspects of the present disclosure, method 900 may be embodied within one or more routines or operations of system 200 as shown and described in Figure 2 . Method 900 may facilitate one or more steps of process flow 500 as shown in Figure 5 . According to certain aspects of the present disclosure, method 900 may include one or more steps or operations 902 to 918 for analyzing a plurality of clinical dialogue data and medical record data of a patient (e.g., according to one or more gAI, NLP, and / or ML models) to identify one or more temporal or contextual associations within the data in order to generate a personalized prognostic assessment of an autoimmune disease for the patient user.

[0078] According to certain aspects of the present disclosure, method 900 may include one or more steps or operations for presenting (e.g., at one or more time points, using a conversational AI agent) multiple clinical conversation prompts to a patient user according to a gAI model (e.g., via a gAI engine executed on at least one server) (step 902). Method 900 may further include one or more steps or operations for receiving (e.g., at one or more time points, using a conversational AI agent) multiple conversation responses from the patient user in response to the multiple clinical conversation prompts (step 904). Method 900 may further include one or more steps or operations for receiving (e.g., using at least one server) a first set of text data including the multiple conversation responses from the patient user (step 906). Method 900 may further include one or more steps or operations for receiving (e.g., via at least one application programming interface, using at least one server) a first set of medical record data of the patient user (step 908). In certain embodiments, the first set of medical record data of the patient user includes at least one of blood biomarker test data and biometric measurement data. Method 900 may further include one or more steps or operations for processing (e.g., using at least one server) the first set of text data and the first set of medical record data of the patient user according to a natural language processing model (step 910). In certain embodiments, method 900 may include one or more steps or operations for processing the first set of medical record data via an OCR engine to convert one or more scanned files or image files into a text-searchable format. In certain embodiments, the natural language processing model is configured to extract one or more features from the first set of text data and the first set of medical record data. In certain embodiments, the natural language processing model is configured to cluster one or more text segments from the first set of text data and the first set of medical record data according to one or more features. Method 900 may further include one or more steps or operations for analyzing one or more text segments according to the natural language processing model to determine one or more temporal or contextual associations between the one or more text segments and the blood biomarker test data and / or the biometric measurement data (step 912). Method 900 may further include one or more steps or operations for assigning one or more prognostic values to one or more text segments according to at least one output of the natural language processing model (step 914).Method 900 may also include one or more steps or operations for analyzing (e.g., using at least one server) the prognostic value of one or more text segments to generate a prognostic assessment of at least one autoimmune disease for the patient user (step 916). Method 900 may also include one or more steps or operations for providing (e.g., using at least one server) the prognostic assessment to a practitioner user and / or patient user via a graphical user interface of at least one client device (step 918).

[0079] In certain aspects of the present disclosure, method 900 may also include one or more steps or operations for configuring (e.g., using at least one server) a dialogue AI model based on the prognostic value of one or more text segments. In certain embodiments, the dialogue AI model may include a large language model. In certain embodiments, the plurality of clinical dialogue prompts may include a plurality of generative prompts based on the dialogue AI model. In certain embodiments, the prognostic assessment includes a diagnostic assessment of at least one autoimmune disease. In certain embodiments, the prognostic assessment includes a predictive assessment of at least one pathophysiological event associated with at least one autoimmune disease. In certain embodiments, the prognostic assessment includes a clinical recommendation for at least one drug intervention for the patient user. In certain embodiments, the prognostic assessment includes a clinical recommendation for at least one blood biomarker test for the patient user. In certain embodiments, the biometric measurement data includes data from at least one body-worn sensor of the patient user. In certain aspects of the present disclosure, method 900 may also include one or more steps or operations for modifying the dialogue AI model to improve one or more clinical dialogue prompts based on the prognostic value of one or more text segments, so as to increase the relevance or specificity of one or more future dialogue interactions between the patient user and the dialogue AI agent.

[0080] Now referring to Figure 10 , a process flow diagram of an artificial intelligence method 1000 for prognostic assessment of autoimmune diseases is shown. The steps or operations of method 1000 may be performed in the presented order, a different order, or simultaneously. Additionally, in some exemplary embodiments, some of the operations may be omitted, added, modified, skipped, etc. without departing from the scope of the present invention. In certain aspects of the present disclosure, method 1000 may be embodied in one or more routines or operations of system 200 as shown and described in Figure 2 Method 1000 may facilitate as shown in Figure 5One or more steps of the illustrated processing flow 500. According to certain aspects of the present disclosure, method 1000 may include one or more steps or operations 1002 to 1016 for analyzing a plurality of clinical conversation data and medical record data of a patient (e.g., according to one or more gAI, NLP, and / or ML models) to identify one or more temporal or contextual associations within the data in order to generate a personalized patient phenotype for the patient user to derive one or more pathophysiological insights for managing the autoimmune disease of the patient user.

[0081] According to certain aspects of the present disclosure, method 1000 may include one or more steps or operations for presenting (e.g., using a dialogue AI agent communicatively coupled to the first server) a first set of clinical dialogue prompts to a patient user according to a generative AI model (e.g., via a gAI engine executed on the first server) (step 1002). Method 1000 may include one or more steps or operations for receiving (e.g., using the dialogue AI agent) a first set of responses to the first set of clinical dialogue prompts from the patient user (step 1004). Method 1000 may include one or more steps or operations for receiving (e.g., using the first server) a first set of medical record data of the patient, where the first set of medical record data includes at least one of blood biomarker test data and biometric data of the patient user (step 1006). Method 1000 may include one or more steps or operations for receiving (e.g., using at least one server) a first set of text data that includes the first set of responses from the patient user to the first set of clinical dialogue prompts (step 1008). Method 1000 may include one or more steps or operations for processing (e.g., using at least one server) the first set of text data and the first set of medical record data according to a natural language processing model (e.g., via an NLP engine executed on the first server) (step 1010). According to certain aspects of the present disclosure, the natural language processing model is configured to extract one or more features from the first set of text data and the first set of medical record data. According to the aspects, the natural language processing model is configured to cluster one or more text segments from the first set of text data and the first set of medical record data according to the one or more features. Method 1000 may include one or more steps or operations for analyzing the one or more text segments according to the natural language processing model to determine one or more temporal or contextual associations between the one or more text segments and the blood biomarker test data and / or biometric data (step 1012). Method 1000 may include one or more steps or operations for configuring a patient phenotype for the patient user according to at least one output of the natural language processing model according to the one or more temporal or contextual associations between the one or more text segments and the blood biomarker test data and / or biometric data (step 1014). According to certain aspects of the present disclosure, the patient phenotype includes one or more personalized insights for the patient user, including, for example, one or more symptoms, markers, and pathological triggers of an autoimmune disease of the patient user.Method 1000 may include one or more steps or operations for providing a patient phenotype of a patient user to a practitioner user and / or a patient user via a graphical user interface of one or more client devices using at least one server (step 1016).

[0082] According to certain aspects of the present disclosure, method 1000 may further include one or more steps or operations for configuring (e.g., using at least one server) a generative AI model according to a patient phenotype. Method 1000 may further include one or more steps or operations for presenting (e.g., using a conversational AI agent) a second set of clinical conversation prompts to a patient user according to the generative AI model. In certain embodiments, the second set of clinical conversation prompts is configured according to a patient phenotype (e.g., according to the generative AI model). According to certain aspects of the present disclosure, at least one clinical prompt in the second set of clinical conversation prompts is different from the first set of clinical conversation prompts. Method 1000 may further include one or more steps or operations for receiving (e.g., using a conversational AI agent) a second set of responses from the patient user to the second set of clinical conversation prompts. Method 1000 may further include one or more steps or operations for receiving (e.g., using a first server) a second set of medical record data of the patient, wherein the second set of medical record data includes a second set of blood biomarker test data and / or a second set of biometric measurement data of the patient user. Method 1000 may further include one or more steps or operations for receiving (e.g., using at least one server) a second set of text data, which includes the second set of responses from the patient user to the second set of clinical conversation prompts. Method 1000 may further include one or more steps or operations for processing (e.g., using at least one server) the second set of text data according to a natural language processing model. In certain embodiments, the natural language processing model is configured to extract one or more features from the second set of text data according to a patient phenotype. In certain embodiments, the natural language processing model is configured to cluster one or more text segments from the second set of text data according to one or more features. Method 1000 may further include one or more steps or operations for processing (e.g., using at least one server) the second set of text data and the second set of medical record data according to a natural language processing model. According to certain aspects of the present disclosure, the natural language processing model is configured to extract one or more features from the second set of text data and the second set of medical record data according to a patient phenotype. In certain embodiments, the natural language processing model is configured to cluster one or more text segments from the second set of text data and the second set of medical record data according to one or more features. Method 1000 may further include one or more steps or operations for analyzing (e.g., according to a machine learning model) one or more text segments to generate a prognosis evaluation for the patient user's autoimmune disease.Method 1000 may further include one or more steps or operations for providing (e.g., using at least one server) a prognosis assessment to a patient user via a graphical user interface of an end-user device associated with the patient user, and / or for providing a prognosis assessment to a practitioner user via a graphical user interface of a client device. Method 1000 may further include one or more steps or operations for updating or modifying a patient phenotype based on at least one output of a natural language processing model. In some embodiments, the prognosis assessment includes one or more recommended actions for managing the patient user's autoimmune disease.

[0083] Now referring to Figure 11 , a process flow diagram of an artificial intelligence method for prognosis assessment of an autoimmune disease is shown. The steps or operations of method 1100 may be performed in the presented order, a different order, or simultaneously. Additionally, in some exemplary embodiments, some of the operations may be omitted, added, modified, skipped, etc. without departing from the scope of the present invention. According to certain aspects of the present disclosure, method 1100 may be embodied in one or more routines or operations of system 200 as shown and described in Figure 2 . Method 1100 may facilitate one or more steps of process flow 500 as shown in Figure 5 . According to certain aspects of the present disclosure, method 1100 may include one or more steps or operations 1102 to 1114 for analyzing a plurality of clinical conversation data and medical record data of a patient (e.g., according to one or more gAI, NLP, and / or ML models) to identify one or more diagnostic triggers of an autoimmune disease from the clinical conversation data, thereby improving diagnostic accuracy and reducing the time for diagnosing an autoimmune disease.

[0084] According to certain aspects of the present disclosure, method 1100 may include one or more of the following steps or operations: the one or more steps or operations for presenting (e.g., using at least one server communicatively coupled to the first client device) a plurality of clinical conversation prompts to a patient user on a user interface of the first client device according to a generative AI model (e.g., via a gAI engine executed on at least one server) (step 1102). Method 1100 may also include one or more of the following steps or operations: the one or more steps or operations for receiving (e.g., via the first client device, using at least one server) a plurality of user-generated responses from the patient user (e.g., via a dialogue AI agent) to the plurality of clinical conversation prompts, the plurality of user-generated responses to the plurality of clinical conversation prompts including a first set of clinical conversation data (step 1104). Method 1100 may also include one or more of the following steps or operations: the one or more steps or operations for receiving (e.g., using at least one server) a first set of medical record data of the patient user, wherein the first set of medical record data includes at least one of blood biomarker test data and biometric measurement data of the patient user (step 1106). Method 1100 may also include one or more of the following steps or operations: the one or more steps or operations for processing (e.g., using at least one server) the first set of clinical conversation data and the first set of medical record data according to a natural language processing model (e.g., via an NLP engine executed on at least one server) (step 1108). In certain embodiments, the natural language processing model is configured to extract one or more features from the first set of clinical conversation data and the first set of medical record data. The natural language processing model may be configured to cluster one or more text segments from the first set of clinical conversation data and the first set of medical record data according to the one or more features. Method 1100 may also include one or more of the following steps or operations: the one or more steps or operations for analyzing one or more text segments according to at least one output of the natural language processing model to determine one or more temporal or contextual associations between the one or more text segments and the blood biomarker test data and / or the biometric measurement data (step 1110). Method 1100 may also include one or more of the following steps or operations: the one or more steps or operations for configuring one or more diagnostic triggers for the patient user according to at least one output of the natural language processing model and according to one or more temporal or contextual associations between the one or more text segments and the blood biomarker test data and / or the biometric measurement data (step 1112).In some embodiments, step 1112 may include one or more of the following steps or operations: the one or more steps or operations for analyzing at least one output of a natural language processing model according to at least one machine learning model (e.g., via at least one ML engine executed on at least one server). Method 1100 may further include one or more of the following steps or operations: the one or more steps or operations for transmitting (e.g., via a network interface, using at least one server) one or more diagnostic triggers to a patient user via a first client device, and / or transmitting one or more diagnostic triggers to a practitioner user via a second client device (step 1114).

[0085] According to certain aspects of the present disclosure, method 1100 may further include one or more steps or operations for configuring (e.g., using at least one server) a natural language processing model based on one or more diagnostic triggers. In certain embodiments, multiple clinical dialogue prompts are configured according to a generative AI model. In such embodiments, the generative AI model may include a large language model. Method 1100 may further include one or more of the following steps or operations: the one or more steps or operations for configuring (e.g., using at least one server) the generative AI model based on one or more diagnostic triggers. Method 1100 may further include one or more of the following steps or operations: the one or more steps or operations for presenting (e.g., using at least one server communicatively coupled to the first client device) a second or subsequent multiple clinical dialogue prompts to a patient user on a user interface of the first client device. Method 1100 may further include one or more of the following steps or operations: the one or more steps or operations for receiving (e.g., via the first client device, using at least one server) user-generated second or subsequent multiple responses from the patient user to the second or subsequent multiple clinical dialogue prompts, the user-generated second or subsequent multiple responses to the second or subsequent multiple clinical dialogue prompts including a second or subsequent set of clinical dialogue data. Method 1100 may further include one or more of the following steps or operations: the one or more steps or operations for processing (e.g., using at least one server) the second or subsequent set of clinical dialogue data according to the natural language processing model to extract one or more features from the second or subsequent set of clinical dialogue data based on one or more diagnostic triggers. Method 1100 may further include one or more of the following steps or operations: the one or more steps or operations for analyzing at least one output of the natural language processing model according to a machine learning model to identify at least one diagnostic trigger from the second or subsequent set of clinical dialogue data. Method 1100 may further include one or more of the following steps or operations: the one or more steps or operations for transmitting (e.g., using at least one server) at least one diagnostic trigger from the second or subsequent set of clinical dialogue data to a practitioner user via a graphical user interface of a second client device. Method 1100 may further include one or more of the following steps or operations: the one or more steps or operations for transmitting (e.g., using at least one server) at least one diagnostic trigger from the second or subsequent set of clinical dialogue data to the patient user at a user interface of the first client device.Method 1100 may further include one or more of the following steps or operations: the one or more steps or operations for generating, according to a machine learning model, at least one clinical recommendation for managing an autoimmune disease based on at least one diagnostic trigger factor from a second or subsequent plurality of responses generated by a user. Method 1100 may further include one or more of the following steps or operations: the one or more steps or operations for transmitting (e.g., using at least one server) at least one clinical recommendation for managing an autoimmune disease to a practitioner user via a graphical user interface of a second client device. Method 1100 may further include one or more of the following steps or operations: the one or more steps or operations for transmitting (e.g., using at least one server) at least one clinical recommendation for managing an autoimmune disease to a patient user at a user interface of a first client device.

[0086] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although any methods and materials similar to or equivalent to those described herein can also be used in the practice or testing of the present invention, exemplary methods and materials are now described. All publications mentioned herein are incorporated herein by reference to disclose and describe the methods and / or materials in connection with the publications cited.

[0087] It must be noted that, as used herein and in the appended claims, the singular forms "a", "an", and "the" include plural references unless the context clearly dictates otherwise. Thus, for example, reference to "a stimulant" includes a plurality of such stimulants, and reference to "a signal" includes reference to one or more signals known to those skilled in the art and their equivalents, and so on.

[0088] Any publication discussed herein is provided only because it was publicly available prior to the filing date of the present application. By virtue of the prior invention, the content herein should not be construed as admitting that the present invention is not entitled to antedate such publication. In addition, the provided publication date may be different from the actual publication date, and the actual publication date may need to be independently confirmed.

[0089] As those skilled in the art will appreciate, the present invention may be embodied as a method (including, for example, computer-implemented processing, business processes, and / or any other processing), an apparatus (including, for example, a system, a machine, a device, a computer program product, etc.), or a combination of the foregoing. Accordingly, embodiments of the present invention may take the form of: all-hardware embodiments, all-software embodiments (including firmware, resident software, microcode, etc.), or embodiments combining software aspects and hardware aspects, which forms may generally be referred to herein as "systems". In addition, embodiments of the present invention may take the form of a computer program product on a computer-readable medium having computer-executable program code contained therein.

[0090] Any suitable transient or non-transient computer-readable medium may be utilized. The computer-readable medium may be, for example but not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices. More specific examples of the computer-readable medium include, but are not limited to, the following: an electrical connection having one or more wires; a tangible storage medium such as a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a compact disc read-only memory (CD-ROM), or other optical or magnetic storage device.

[0091] In the context of this document, a computer-readable medium may be any medium that can contain, store, transmit, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. The computer-usable program code may be transmitted using any appropriate medium, including but not limited to the Internet, wireline, optical fiber cable, radio frequency (RF) signal, or other medium.

[0092] The computer-executable program code for performing the operations of the embodiments of the present invention may be written in an object-oriented, scripting, or non-scripting programming language such as Java, Perl, Smalltalk, C++, etc. However, the computer program code for performing the operations of the embodiments of the present invention may also be written in a conventional procedural programming language such as the "C" programming language or similar programming languages.

[0093] Embodiments of the present invention have been described above with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products. It will be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-executable program code portions. These computer-executable program code portions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a particular machine, such that the code portions executed via the processor of the computer or other programmable data processing apparatus create a mechanism for implementing the functions / actions specified in the block or blocks of the flowchart and / or block diagram.

[0094] These computer-executable program code portions can also be stored in a computer-readable memory, which can direct a computer or other programmable data processing apparatus to operate in a particular manner, such that the code portions stored in the computer-readable memory produce an article of manufacture including an instruction mechanism for implementing the functions / actions specified in the block of the flowchart and / or block diagram.

[0095] The computer-executable program code can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus, thereby producing a computer-implemented process, such that the code portions executed on the computer or other programmable apparatus provide steps for implementing the functions / actions specified in the block of the flowchart and / or block diagram. Alternatively, the steps or actions implemented by a computer program can be combined with the steps or actions implemented by an operator or a human to perform embodiments of the present invention.

[0096] As used herein, the phrase that a processor can be “configured to” perform a certain function in various ways, including, for example, by causing one or more general-purpose circuits to perform the function by executing specific computer-executable program code contained in a computer-readable medium, and / or by causing one or more dedicated circuits to perform the function.

[0097] The embodiments of the present invention have been described above with reference to the flowcharts and / or block diagrams. It will be understood that the stages of the processes described herein may be executed in an order different from the order shown in the flowcharts. In other words, in some embodiments, the processes represented by the blocks of the flowchart may be executed in an order different from the shown order, may be combined or divided, or may be executed simultaneously. It will also be understood that in some embodiments, the blocks of the block diagrams only show a conceptual description between systems, and one or more systems shown by the blocks in the block diagrams may combine or share hardware and / or software with another or more systems shown by the blocks in the block diagrams. Similarly, a device, system, apparatus, and / or the like may be composed of one or more devices, systems, apparatuses, and / or the like. For example, in the case where a processor is shown or described herein, the processor may be composed of multiple microprocessors or other processing devices, and the microprocessors or other processing devices may be coupled to each other or may not be coupled to each other. Similarly, in the case where a memory is shown or described herein, the memory may be composed of multiple memory devices, and the memory devices may be coupled to each other or may not be coupled to each other.

[0098] In the claims and in the foregoing specification, all transitional phrases such as "comprising," "including," "carrying," "having," "containing," "involving," "holding," "consisting of," etc. should be understood to be open-ended, i.e., meaning including but not limited to. Only the transitional phrases "consisting of" and "consisting essentially of" should be closed or semi-closed transitional phrases, as set forth in Section 2111.03 of the Manual of Patent Examining Procedure of the United States Patent and Trademark Office.

[0099] Although certain exemplary embodiments have been described and shown in the drawings, it should be understood that such embodiments are merely illustrative of the broad invention and not limiting, and the present invention is not limited to the specific constructions and arrangements shown and described, since various other changes, combinations, omissions, modifications, and substitutions are possible in addition to those set forth in the foregoing paragraphs. Those skilled in the art will understand that various adjustments and modifications to the embodiments just described can be made without departing from the scope and spirit of the present invention. Therefore, it should be understood that the present invention may be practiced in a manner different from that specifically described herein within the scope of the appended claims.

Claims

1. A computer-implemented method for prognostic assessment of autoimmune diseases, comprising: at one or more time points, presenting, using a conversational AI agent, a plurality of clinical conversation prompts to a patient user; at the one or more time points, receiving, using the conversational AI agent, a plurality of conversation responses from the patient user in response to the plurality of clinical conversation prompts; receiving, using at least one server, a first set of text data, the first set of text data including the plurality of conversation responses from the patient user; receiving, via at least one application programming interface, using the at least one server, a first set of medical record data of the patient user, wherein the first set of medical record data of the patient user includes at least one of blood biomarker test data and biometric measurement data; processing, using the at least one server, the first set of text data and the first set of medical record data of the patient user according to a natural language processing model, wherein the natural language processing model is configured to extract one or more features from the first set of text data and the first set of medical record data, wherein the natural language processing model is configured to cluster one or more text segments from the first set of text data and the first set of medical record data according to the one or more features; analyzing, according to the natural language processing model, the one or more text segments to determine one or more temporal or contextual associations between the one or more text segments and the blood biomarker test data and / or the biometric measurement data; assigning, according to at least one output of the natural language processing model, one or more prognostic values to the one or more text segments; analyzing, using the at least one server, the one or more prognostic values of the one or more text segments to generate a prognostic assessment for at least one autoimmune disease of the patient user; and providing, using the at least one server, via a graphical user interface of a client device, the prognostic assessment to a practitioner user.

2. The computer-implemented method according to claim 1, further comprising: configuring, using the at least one server, a conversational AI model according to the one or more prognostic values of the one or more text segments.

3. The computer-implemented method according to claim 2, wherein, the conversational AI model includes a large language model.

4. The computer-implemented method according to any one of claims 2 to 3, wherein, the plurality of clinical conversation prompts include a plurality of generative prompts according to the conversational AI model.

5. The computer-implemented method according to any one of claims 1 to 4, wherein, the prognostic assessment includes a diagnostic assessment for the at least one autoimmune disease.

6. The computer-implemented method according to any one of claims 1 to 5, wherein, the prognostic assessment includes a predictive assessment of at least one pathophysiological event associated with the at least one autoimmune disease.

7. The computer-implemented method according to any one of claims 1 to 6, wherein, the prognosis evaluation includes clinical recommendations for at least one drug intervention for the patient user.

8. The computer-implemented method according to any one of claims 1 to 7, wherein, the prognosis evaluation includes clinical recommendations for at least one blood biomarker test for the patient user.

9. The computer-implemented method according to any one of claims 1 to 8, wherein, the biometric measurement data includes data from at least one body-worn sensor of the patient user.

10. The computer-implemented method according to any one of claims 2 to 9, further comprising modifying the plurality of clinical dialogue prompts according to the one or more prognosis values of the one or more text segments via the dialogue AI model.

11. A computer-implemented system for prognosis assessment of an autoimmune disease, comprising a processing unit and a non-transitory computer-readable medium communicatively coupled to the processing unit, the non-transitory computer-readable medium including processor-executable instructions stored thereon, the processor-executable instructions causing the processing unit to perform one or more operations when executed by the processing unit, the one or more operations including the computer-implemented method according to any one of claims 1 to 10.

12. A computer program product embodied in a non-transitory computer-readable medium, the computer program product including processor-executable instructions that cause a processing unit to perform one or more operations when executed by the processing unit, the one or more operations including the computer-implemented method according to any one of claims 1 to 10.

13. A computer-implemented method for patient phenotype analysis of an autoimmune disease, comprising: presenting, by a dialogue AI agent communicatively coupled to a first server, a first set of clinical dialogue prompts to a patient user according to a generative AI model; receiving, by the dialogue AI agent, a first set of responses from the patient user to the first set of clinical dialogue prompts; receiving, by the first server, a first set of medical record data of the patient, wherein the first set of medical record data includes at least one of blood biomarker test data and biometric measurement data of the patient user; receiving, by at least one server, a first set of text data including the first set of responses from the patient user to the first set of clinical dialogue prompts; processing, by the at least one server, the first set of text data and the first set of medical record data according to a natural language processing model, wherein the natural language processing model is configured to extract one or more features from the first set of text data and the first set of medical record data, wherein the natural language processing model is configured to cluster one or more text segments from the first set of text data and the first set of medical record data according to the one or more features. Analyze the one or more text segments according to the natural language processing model to determine one or more temporal or contextual associations between the one or more text segments and the blood biomarker test data and / or the biometric measurement data; Configure a patient phenotype for the patient user according to the one or more temporal or contextual associations between the one or more text segments and the blood biomarker test data and / or the biometric measurement data based on at least one output of the natural language processing model, wherein the patient phenotype includes one or more symptoms, markers, and pathological triggers of an autoimmune disease of the patient user; and Use the at least one server to provide the patient phenotype of the patient user to a practitioner user via a graphical user interface of a client device.

14. The computer-implemented method according to claim 13, further comprising: Configure the generative AI model using the at least one server according to the patient phenotype.

15. The computer-implemented method according to any one of claims 13 to 14, further comprising: Use the dialogue AI agent to present a second set of clinical dialogue prompts to the patient user according to the generative AI model.

16. The computer-implemented method according to claim 15, wherein, The second set of clinical dialogue prompts is configured according to the patient phenotype.

17. The computer-implemented method according to any one of claims 15 to 16, wherein, At least one clinical prompt in the second set of clinical dialogue prompts is different from the first set of clinical dialogue prompts.

18. The computer-implemented method according to any one of claims 15 to 17, further comprising: Use the dialogue AI agent to receive a second set of responses from the patient user to the second set of clinical dialogue prompts.

19. The computer-implemented method according to any one of claims 15 to 18, further comprising: Use the first server to receive a second set of medical record data of the patient, wherein the second set of medical record data includes a second set of blood biomarker test data and / or a second set of biometric measurement data of the patient user.

20. The computer-implemented method according to any one of claims 15 to 19, further comprising: Use the at least one server to receive a second set of text data, the second set of text data including the second set of responses from the patient user to the second set of clinical dialogue prompts.

21. The computer-implemented method according to claim 20, further comprising: Use the at least one server to process the second set of text data according to the natural language processing model.

22. The computer-implemented method according to claim 21, wherein, The natural language processing model is configured to extract one or more features from the second set of text data according to the patient phenotype.

23. The computer-implemented method according to claim 22, wherein, The natural language processing model is configured to cluster one or more text segments from the second set of text data based on the one or more features.

24. The computer-implemented method according to claim 20, further comprising: processing, by the at least one server, the second set of text data and the second set of medical record data according to the natural language processing model.

25. The computer-implemented method according to claim 24, wherein the natural language processing model is configured to extract one or more features from the second set of text data and the second set of medical record data according to the patient phenotype.

26. The computer-implemented method according to claim 25, wherein the natural language processing model is configured to cluster one or more text segments from the second set of text data and the second set of medical record data based on the one or more features.

27. The computer-implemented method according to any one of claims 21 to 23, further comprising: analyzing, according to a machine learning model, the one or more text segments to generate a prognosis evaluation for the autoimmune disease of the patient user.

28. The computer-implemented method according to any one of claims 24 to 26, further comprising: analyzing, according to a machine learning model, the one or more text segments to generate a prognosis evaluation for the autoimmune disease of the patient user.

29. The computer-implemented method according to claim 27 or 28, further comprising: using the at least one server to provide the prognosis evaluation to the patient user via a graphical user interface of a terminal user device associated with the patient user.

30. The computer-implemented method according to claim 27 or 28, further comprising: using the at least one server to provide the prognosis evaluation to the practitioner user via a graphical user interface of the client device.

31. The computer-implemented method according to any one of claims 21 to 23, further comprising: updating or modifying the patient phenotype according to at least one output of the natural language processing model.

32. The computer-implemented method according to any one of claims 24 to 26, further comprising: updating or modifying the patient phenotype according to at least one output of the natural language processing model.

33. The computer-implemented method according to any one of claims 29 or 30, wherein the prognosis evaluation includes one or more recommended actions for managing the autoimmune disease of the patient user.

34. A computer-implemented system for patient phenotype analysis of autoimmune diseases, comprising a processing unit and a non-transitory computer-readable medium communicatively coupled to the processing unit, the non-transitory computer-readable medium including processor-executable instructions stored thereon, the processor-executable instructions, when executed by the processing unit, cause the processing unit to perform one or more operations, the one or more operations including the computer-implemented method according to any one of claims 13 to 33.

35. A computer program product embodied in a non-transitory computer-readable medium, the computer program product including processor-executable instructions that, when executed by a processing unit, cause the processing unit to perform one or more operations, the one or more operations including the computer-implemented method according to any one of claims 13 to 33.

36. A computer-implemented method for identifying diagnostic triggers for autoimmune diseases from clinical dialogue data, comprising: using at least one server communicatively coupled to a first client device to present a plurality of clinical dialogue prompts to a patient user on a user interface of the first client device; receiving, via the first client device and using the at least one server, a plurality of user-generated responses from the patient user to the plurality of clinical dialogue prompts, the plurality of user-generated responses to the plurality of clinical dialogue prompts including a first set of clinical dialogue data; receiving, using the at least one server, a first set of medical record data of the patient user, wherein the first set of medical record data includes at least one of blood biomarker test data and biometric measurement data of the patient user; processing, using the at least one server, the first set of clinical dialogue data and the first set of medical record data according to a natural language processing model, wherein the natural language processing model is configured to extract one or more features from the first set of clinical dialogue data and the first set of medical record data, wherein the natural language processing model is configured to cluster one or more text segments from the first set of clinical dialogue data and the first set of medical record data according to the one or more features; analyzing the one or more text segments according to at least one output of the natural language processing model to determine one or more temporal or contextual associations between the one or more text segments and the blood biomarker test data and / or the biometric measurement data; configuring, according to the at least one output of the natural language processing model and according to the one or more temporal or contextual associations between the one or more text segments and the blood biomarker test data and / or the biometric measurement data, one or more diagnostic triggers for the patient user; and transmitting, via a network interface and using the at least one server, the one or more diagnostic triggers of the patient user to a practitioner user via a graphical user interface of a second client device.

37. The computer-implemented method according to claim 36, wherein, the plurality of clinical dialogue prompts are configured according to a generative AI model.

38. The computer-implemented method according to claim 37, wherein, the generative AI model includes a large language model.

39. The computer-implemented method according to any one of claims 36 to 37, further comprising: configuring, using the at least one server, the natural language processing model according to the one or more diagnostic triggers.

40. The computer-implemented method according to any one of claims 37 to 38, further comprises: configuring the generative AI model by means of the at least one server according to the one or more diagnostic trigger factors.

41. The computer-implemented method according to claim 40, further comprises: presenting, by means of the at least one server communicatively coupled to the first client device, a second or subsequent plurality of clinical dialogue prompts to the patient user on the user interface of the first client device.

42. The computer-implemented method according to claim 41, further comprises: receiving, via the first client device by means of the at least one server, second or subsequent plurality of user-generated responses from the patient user to the second or subsequent plurality of clinical dialogue prompts, the second or subsequent plurality of user-generated responses to the second or subsequent plurality of clinical dialogue prompts comprising a second or subsequent set of clinical dialogue data.

43. The computer-implemented method according to claim 42, further comprises: processing, by means of the at least one server, the second or subsequent set of clinical dialogue data according to the natural language processing model to extract one or more features from the second or subsequent set of clinical dialogue data according to the one or more diagnostic trigger factors.

44. The computer-implemented method according to claim 43, further comprises: analyzing, according to a machine learning model, at least one output of the natural language processing model to identify at least one diagnostic trigger factor from the second or subsequent set of clinical dialogue data.

45. The computer-implemented method according to claim 44, further comprises: transmitting, by means of the at least one server, via the graphical user interface of the second client device, the at least one diagnostic trigger factor from the second or subsequent set of clinical dialogue data to the practitioner user.

46. The computer-implemented method according to claim 44, further comprises: transmitting, by means of the at least one server, at the user interface of the first client device, the at least one diagnostic trigger factor from the second or subsequent set of clinical dialogue data to the patient user.

47. The computer-implemented method according to claim 44, further comprises: generating, according to the machine learning model, at least one clinical recommendation for managing the autoimmune disease according to the at least one diagnostic trigger factor from the user-generated second or subsequent plurality of responses.

48. The computer-implemented method according to claim 47, further comprises: transmitting, by means of the at least one server, via the graphical user interface of the second client device, at least one clinical recommendation for managing the autoimmune disease to the practitioner user.

49. The computer-implemented method according to claim 47, further comprises: transmitting, by means of the at least one server, at the user interface of the first client device, at least one clinical recommendation for managing the autoimmune disease to the patient user.

50. A computer-implemented system for identifying diagnostic triggers for autoimmune diseases, comprising a processing unit and a non-transitory computer-readable medium communicatively coupled to the processing unit, the non-transitory computer-readable medium including processor-executable instructions stored thereon, the processor-executable instructions, when executed by the processing unit, cause the processing unit to perform one or more operations, the one or more operations including the computer-implemented method according to any one of claims 36 to 49.

51. A computer program product embodied in a non-transitory computer-readable medium, the computer program product including processor-executable instructions, the processor-executable instructions, when executed by a processing unit, cause the processing unit to perform one or more operations, the one or more operations including the computer-implemented method according to any one of claims 36 to 49.

52. A system for prognostic assessment of autoimmune diseases, comprising: a first client device associated with a patient user, wherein the first client device includes a first graphical display and a first input / output device; a second client device associated with a practitioner user, wherein the second client device includes a second graphical display and a second input / output device; an application server communicatively coupled to the first client device and the second client device via a network communication interface, wherein the application server includes a generative AI engine and a natural language processing engine, wherein the application server includes at least one processor and a non-transitory computer-readable medium having instructions stored thereon, the instructions, when executed by the at least one processor, cause the processor to perform one or more operations, the one or more operations including: presenting, at the first client device, a plurality of clinical dialogue prompts according to a generative AI model executed on the generative AI engine; receiving, from the patient user, a plurality of dialogue responses in response to the plurality of clinical dialogue prompts, the plurality of dialogue responses including a first set of text data; receiving, via at least one application programming interface, a first set of medical record data of the patient user, wherein the first set of medical record data of the patient user includes at least one of blood biomarker test data and biometric measurement data; processing the first set of text data and the first set of medical record data of the patient user according to a natural language processing model executed on the natural language processing engine, wherein the natural language processing model is configured to extract one or more features from the first set of text data and the first set of medical record data, wherein the natural language processing model is configured to cluster one or more text segments from the first set of text data and the first set of medical record data according to the one or more features; Analyze the one or more text segments according to the natural language processing model to determine one or more temporal or contextual associations between the one or more text segments and the blood biomarker test data and / or the biometric measurement data; Assign one or more prognostic values to the one or more text segments according to the natural language processing model; Analyze the one or more prognostic values of the one or more text segments to generate a prognostic evaluation for at least one autoimmune disease of the patient user; and Provide the prognostic evaluation to the first client device and / or the second client device via the network communication interface.

53. The system according to claim 52, further comprising at least one biometric sensor configured to collect the biometric measurement data of the patient user.

54. The system according to claim 53, wherein, the at least one biometric sensor includes a body-worn sensor configured to continuously collect the biometric measurement data when worn by the patient user.

55. The system according to claim 54, wherein, the at least one biometric sensor is communicatively coupled to the first client device to transmit a plurality of sensor inputs including the biometric measurement data to the first client device in real time.

56. The system according to claim 55, wherein, the first client device is configured to transmit the biometric measurement data to the application server via the network communication interface.

57. The system according to any one of claims 52 to 56, wherein, the generative AI model includes a large language model.

58. The system according to any one of claims 52 to 57, wherein, the biometric measurement data includes at least one data type selected from the group consisting of electrocardiogram data, heart rate data, heart rate variability data, sleep data, and activity data.

59. The system according to any one of claims 52 to 58, wherein, the prognostic evaluation includes a diagnostic evaluation for the at least one autoimmune disease.

60. The system according to any one of claims 52 to 59, wherein, the prognostic evaluation includes a predictive evaluation of at least one pathophysiological event associated with the at least one autoimmune disease.

61. The system according to any one of claims 52 to 60, wherein, the prognostic evaluation includes clinical recommendations for at least one drug intervention for the patient user.

62. The system according to any one of claims 52 to 61, wherein, the prognostic evaluation includes clinical recommendations for at least one blood biomarker test for the patient user.

63. A system for patient phenotype analysis of autoimmune diseases, comprising: a first client device associated with a patient user, wherein the first client device includes a first graphical display and a first input / output device; A second client device associated with a practitioner user, wherein the second client device includes a second graphical display and a second input / output device; An application server communicatively coupled to the first client device and the second client device via a network communication interface, wherein the application server includes a generative AI engine and a natural language processing engine, wherein the application server includes at least one processor and a non-transitory computer-readable medium having instructions stored thereon that, when executed by the at least one processor, cause the processor to perform one or more operations, the one or more operations including: Presenting a first set of clinical conversation prompts at the first client device according to a generative AI model executed on the generative AI engine; Receiving a first set of conversation responses from the patient user in response to the first set of clinical conversation prompts, the first set of conversation responses including a first set of text data; Receiving a first set of medical record data of the patient user, wherein the first set of medical record data includes at least one of the patient user's blood biomarker test data and biometric measurement data; Processing the first set of text data and the first set of medical record data according to a natural language processing model executed on the natural language processing engine, wherein the natural language processing model is configured to extract one or more features from the first set of text data and the first set of medical record data, wherein the natural language processing model is configured to cluster one or more text segments from the first set of text data and the first set of medical record data according to the one or more features; Analyzing the one or more text segments according to a machine learning model to determine one or more temporal or contextual associations between the one or more text segments and the blood biomarker test data and / or the biometric measurement data; Configuring a patient phenotype for the patient user according to the machine learning model based on the one or more temporal or contextual associations between the one or more text segments and the blood biomarker test data and / or the biometric measurement data, wherein the patient phenotype includes one or more symptoms, markers, and pathological triggers of the patient user's autoimmune disease; and Providing the patient phenotype of the patient user to the first client device and / or the second client device via the network communication interface.

64. The system according to claim 63, further comprising at least one biometric sensor configured to collect the biometric measurement data of the patient user.

65. The system according to claim 64, wherein, the at least one biometric sensor includes a body-worn sensor configured to continuously collect the biometric measurement data when worn by the patient user.

66. The system according to claim 65, wherein, The at least one biometric sensor is communicatively coupled to the first client device to transmit, in real time, a plurality of sensor inputs including the biometric measurement data to the first client device.

67. The system according to claim 66, wherein, the first client device is configured to transmit the biometric measurement data to the application server via the network communication interface.

68. The system according to any one of claims 63 to 67, wherein, the generative AI model includes a large language model.

69. The system according to any one of claims 63 to 68, wherein, the biometric measurement data includes at least one data type selected from the group consisting of electrocardiogram data, heart rate data, heart rate variability data, sleep data, and activity data.

70. The system according to any one of claims 63 to 69, wherein, the one or more operations further include configuring the generative AI model according to the patient phenotype.

71. The system according to any one of claims 63 to 70, wherein, the one or more operations further include presenting, at the first client device, a second set of clinical dialogue prompts according to the generative AI model.

72. The system according to claim 71, wherein, the second set of clinical dialogue prompts is configured according to the patient phenotype.

73. The system according to any one of claims 71 to 72, wherein, at least one clinical prompt in the second set of clinical dialogue prompts is different from the first set of clinical dialogue prompts.

74. The system according to any one of claims 71 to 73, wherein, the one or more operations further include receiving a second set of dialogue responses from the patient user in response to the second set of clinical dialogue prompts, the second set of dialogue responses including a second set of text data.

75. The system according to any one of claims 71 to 74, wherein, the one or more operations further include receiving a second set of medical record data of the patient, wherein the second set of medical record data includes a second set of blood biomarker test data and / or a second set of biometric measurement data of the patient user.

76. The system according to any one of claims 71 to 75, wherein, the one or more operations further include processing the second set of text data according to the natural language processing model.

77. The system according to claim 76, wherein, the natural language processing model is configured to extract one or more features from the second set of text data according to the patient phenotype.

78. The system according to claim 77, wherein, the natural language processing model is configured to cluster one or more text segments from the second set of text data according to the one or more features.

79. The system according to any one of claims 76 to 78, wherein, the one or more operations further include updating or modifying the patient phenotype according to at least one output of the natural language processing model.

80. The system according to any one of claims 76 to 79, wherein, the one or more operations further include analyzing the one or more text segments to generate a prognosis evaluation for the autoimmune disease of the patient user.

81. The system according to claim 80, wherein, the one or more operations further include providing the prognosis evaluation to the first client device and / or the second client device via the network communication interface.

82. The system according to claim 81, wherein, the prognosis evaluation includes one or more recommended actions for managing the autoimmune disease of the patient user.

83. A system for identifying diagnosis-related trigger factors of autoimmune diseases from clinical dialogue data, comprising: a first client device associated with a patient user, wherein the first client device includes a first graphical display and a first input / output device; a second client device associated with a practitioner user, wherein the second client device includes a second graphical display and a second input / output device; an application server communicatively coupled to the first client device and the second client device via a network communication interface, wherein the application server includes a generative AI engine and a natural language processing engine, wherein the application server includes at least one processor and a non-transitory computer-readable medium storing instructions that, when executed by the at least one processor, cause the processor to perform one or more operations, the one or more operations including: presenting, at the first client device, a plurality of clinical dialogue prompts for the patient user according to a generative AI model executed on the generative AI engine; receiving a plurality of user-generated responses from the patient user for the plurality of clinical dialogue prompts, the plurality of user-generated responses for the plurality of clinical dialogue prompts including a first set of clinical dialogue data; receiving a first set of medical record data of the patient user, wherein the first set of medical record data includes at least one of blood biomarker test data and biometric measurement data of the patient user; processing the first set of clinical dialogue data and the first set of medical record data according to a natural language processing model executed on the natural language processing engine, wherein the natural language processing model is configured to extract one or more features from the first set of clinical dialogue data and the first set of medical record data, wherein the natural language processing model is configured to cluster one or more text segments from the first set of clinical dialogue data and the first set of medical record data according to the one or more features; analyzing the one or more text segments according to at least one output of the natural language processing model to determine one or more temporal or contextual associations between the one or more text segments and the blood biomarker test data and / or the biometric measurement data. Based on the at least one output of the natural language processing model, configure one or more diagnostic triggers for the patient user according to the one or more temporal or situational associations between the one or more text segments and the blood biomarker test data and / or the biometric measurement data; and Transmit the one or more diagnostic triggers to the first client device and / or the second client device via the network communication interface.

84. The system according to claim 83, further comprising at least one biometric sensor configured to collect the biometric measurement data of the patient user.

85. The system according to claim 84, wherein, the at least one biometric sensor includes a body-worn sensor configured to continuously collect the biometric measurement data when worn by the patient user.

86. The system according to claim 85, wherein, the at least one biometric sensor is communicatively coupled to the first client device to transmit multiple sensor inputs including the biometric measurement data to the first client device in real time.

87. The system according to claim 86, wherein, the first client device is configured to transmit the biometric measurement data to the application server via the network communication interface.

88. The system according to any one of claims 83 to 87, wherein, the generative AI model includes a large language model.

89. The system according to any one of claims 83 to 88, wherein, the biometric measurement data includes at least one data type selected from the group consisting of electrocardiogram data, heart rate data, heart rate variability data, sleep data, and activity data.

90. The system according to any one of claims 83 to 89, wherein, the one or more operations further include configuring or modifying the natural language processing model according to the one or more diagnostic triggers.

91. The system according to any one of claims 83 to 90, wherein, the one or more operations further include configuring or modifying the generative AI model according to the one or more diagnostic triggers.

92. The system according to claim 91, wherein, the one or more operations further include presenting a second or subsequent plurality of clinical dialogue prompts for the patient user at the first client device according to the generative AI model.

93. The system according to claim 92, wherein, the one or more operations further include receiving second or subsequent user-generated responses from the patient user for the second or subsequent plurality of clinical dialogue prompts, and the second or subsequent user-generated responses for the second or subsequent plurality of clinical dialogue prompts include a second or subsequent set of clinical dialogue data.

94. The system according to claim 93, wherein, The one or more operations further include processing the second or subsequent set of clinical conversation data according to the natural language processing model to extract one or more features from the second or subsequent set of clinical conversation data based on the one or more diagnostic triggers.

95. The system according to claim 94, wherein, the one or more operations further include analyzing at least one output of the natural language processing model to identify at least one diagnostic trigger from the second or subsequent set of clinical conversation data.

96. The system according to claim 95, wherein, the one or more operations further include transmitting, via the network communication interface, the at least one diagnostic trigger from the second or subsequent set of clinical conversation data to the first client device and / or the second client device.

97. The system according to any one of claims 94 to 96, wherein, the one or more operations further include generating, according to a machine learning model, at least one clinical recommendation for managing the autoimmune disease based on the at least one diagnostic trigger from the second or subsequent set of clinical conversation data.

98. The system according to claim 97, wherein, the one or more operations further include transmitting, via the network communication interface, at least one clinical recommendation for managing the autoimmune disease to the first client device and / or the second client device.

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