96 results about "Patient profile" patented technology

The invention discloses a patient clinical information management system and method, and relates to the technical field of medical information management and data security, and the system comprises a data collection module which is used for receiving patient basic information and diagnosis and treatment records; the encryption storage module is used for establishing a patient file identification number according to the basic information of the patient and the diagnosis and treatment record, encrypting the basic information of the patient and the diagnosis and treatment record and then storing the encrypted basic information and the encrypted diagnosis and treatment record into a corresponding electronic medical record database; the semantic analysis module is used for performing semantic analysis on the diagnosis and treatment records and extracting clinical feature tags; the clinical feature construction module is used for constructing a patient similarity matrix based on the clinical feature tags obtained by the semantic analysis module, and performing clustering analysis to obtain a clinical feature group; and the treatment template generation module is used for constructing a corresponding treatment scheme template for each clinical feature group. According to the invention, the safety, accuracy and intelligent level of patient information management are significantly improved.

An example digital therapy platform is disclosed that provides personalized recommendations. Patient data is collected from a plurality of data sources associated with a patient profile of a patient in the digital therapy platform. At least a subset of the patient data is processed to detect a patient event. In response to detecting the patient event, a personalized recommendation is generated. The personalized recommendation is associated with the patient profile and generated using at least one machine learning model. An action is invoked in the digital therapy platform based on the personalized recommendation, and the patient profile is adjusted to reflect the action. The digital therapy platform causes presentation of at least one of a first indication of the action at a first device associated with a therapist assigned to the patient profile or a second indication of the action at a second device associated with the patient.

The present invention discloses an adaptive remote medical monitoring method and system based on the Internet of Things, which relates to the field of medical monitoring technology, including creating patient files and performing equipment allocation and configuration; collecting patients' real-time health data and transmitting it to a cloud server for data preprocessing; performing abnormality detection on the preprocessed data and performing preliminary analysis on the abnormal data; and evaluating the patient's health status and formulating response measures. The present invention creates personalized patient files, performs intelligent equipment allocation and configuration, constructs an abnormality detection algorithm, and performs preliminary analysis on abnormal data, ensuring that the monitoring equipment can accurately meet the patient's personalized monitoring needs, which not only improves the efficiency and accuracy of remote medical monitoring, but also can timely detect abnormal changes in the patient's health status, providing a scientific decision-making basis for medical staff.

An apparatus for determining a patient survival profile using artificial intelligence-enabled electrocardiogram (ECG), the apparatus includes a processor and a memory containing instructions configuring the processor to receive a plurality of patient profiles, wherein each patient profile includes ECG data, define a plurality of cohort labels, wherein defining the plurality of cohort labels includes generating a condition score for each patient profile of the plurality of patient profiles and defining the plurality of cohort labels as a function of the condition score, assign the plurality of cohort labels to the plurality of patient profiles, generate condition training data by correlating the plurality of patient profiles with a plurality of condition identifiers, generate a condition evaluation model and a machine-learning algorithm using the condition training data, determine a patient survival profile for a user-inputted patient profile using the condition evaluation model, and display the patient survival profile at a display device.

The invention relates to the technical field of medical information, in particular to a tumor patient adverse drug reaction intelligent follow-up visit system based on an internet hospital, which comprises a patient file module for acquiring medical data, identifying bad data and recording the bad data in a medical record file to form an abnormal symptom report; the intelligent analysis module is used for comprehensively analyzing the abnormal symptom report by utilizing a trained machine learning model and outputting analysis data; according to the system and the method, continuous monitoring and active early warning of extrahospital adverse reactions of tumor patients are realized, the timeliness of finding the adverse reactions is remarkably improved, intervention measures can be taken before symptoms of the patients deteriorate, and the treatment safety of the patients is improved. And secondly, through intelligent analysis and grading, the burden of manually screening the patient condition by medical staff is greatly reduced, the system automatically finds abnormity from massive follow-up visit data, and the efficiency and accuracy of follow-up visit management are improved.

The present invention discloses an artificial intelligence-based (or AI-based) system and method for providing personalized treatment plans to individuals suffering from mental health disorders. By leveraging EEG measurements, patient data, and AI algorithms, this system addresses the unique needs of each patient and enhances the overall quality of mental health care. The AI-based personalized treatment system has the potential to revolutionize mental health care. By amalgamating EEG measurements and an extensive range of patient-specific factors, it enables a tailored, dynamic approach to mental health treatment that takes into account the complete patient profile. The integration of this system into existing electronic health record (EHR) systems or patient monitoring platforms creates a patient-centered experience that enhances treatment outcomes, patient satisfaction, and reduces the strain on healthcare professionals.

The invention discloses a chronic disease management system for hospitals, which relates to the technical field of chronic disease management systems and comprises a patient file module, a risk assessment module, an intervention scheme module, a follow-up management module, a monitoring and early warning module and a decision support module. The patient archive module is used for collecting and sorting personal information and medical history information of patients by adopting an automatic data integration method to obtain a complete patient health archive; the risk assessment module is used for analyzing the health information in the patient health record by adopting an intelligent assessment algorithm to obtain a chronic disease risk assessment result; the intervention scheme module is used for formulating a personalized health intervention scheme according to the risk assessment result, and the personalized health intervention scheme comprises diet, exercise and medication suggestions; and the follow-up management module is used for executing the follow-up plan in the intervention scheme through multiple communication modes, tracking the rehabilitation condition of the patient, recording and feeding back, and generating a follow-up report.

A system for real-time generation of therapeutic plans based on predictive analytics of patient profile data including a processor of a therapeutic plan server (TPS) node configured to host a machine learning (ML) module and connected to at least one patient-entity node over a network and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: receive the patient profile data including patient nutrients intake data and medications intake data from the at least one patient-entity node; parse the patient profile data to derive a plurality of key classifying features; query a local database to retrieve local historical patients-related data based on the plurality of key classifying features; generate at least one classifier feature vector based on the plurality of key classifying features and the local historical patients-related data; provide the at least one feature vector to the ML module coupled to an Artificial Neural Network (ANN); receive a plurality of nutrients-medications correlation parameters from a therapeutic plan predictive model generated by the ML module using outputs of the ANN based on the at least one feature vector; and generate a therapeutic plan for the at least one patient-entity node based on the nutrients-medications correlation parameters.

System (100) for personalized condition-based matching of health networks using artificial intelligence and population health data, comprising: (a) a patient profiling module configured to collect and analyse individual health data from electronic health records, wearable devices and patient inputs to identify current medical conditions and generate dynamic health profiles; (b) a population health intelligence engine configured to aggregate and analyze population-level health data to identify regional disease trends, treatment outcomes, and provider performance; c) an AI-based network matching algorithm configured to compare individual health conditions with provider capabilities, historical outcomes, availability, and other contextual parameters to generate rated recommendations for healthcare providers; (d) a provider profiling and network indexing module configured to store, index and update provider and network information, including specialization, certifications, outcomes, availability and treatment capacities; (e) a decision support interface configured to present patients or clinicians with personalized, explainable recommendations for healthcare providers based on clinical relevance, patient preferences and system intelligence; (f) a feedback and learning optimization module configured to continuously collect patient outcomes, satisfaction, and treatment effectiveness to refine and retrain AI models; and g) a data security and compliance layer configured to ensure the secure handling of patient data, enforce access control, and ensure compliance with data protection standards such as HIPAA and GDPR.

A machine learning-based real-time patient monitoring and health risk prediction system (100), comprising: (a) a data acquisition module configured to receive real-time physiological data from one or more health monitoring devices; (b) a data preprocessing and normalisation module configured to clean, format and standardise the collected data; (c) a patient profile and context information module for maintaining dynamic, personalized health records, including medical history and lifestyle data; (d) a machine learning engine configured to analyse the pre-processed data in conjunction with contextual information to generate predictive health risk scores; (e) an alert and notification module configured to provide real-time alerts upon detection of abnormal health patterns; (f) a dashboard interface module for visualising patient vital signs, risk predictions and historical trends; g) a feedback and continuous learning module that refines the machine learning models using clinical results and feedback; h) and a security, compliance and audit module that ensures encrypted data processing and compliance with healthcare regulations.

A system for monitoring the movements or other activities of patient. Aspects include a monitoring device with one or more sensors such as a pressure or motion sensors that may be positioned on or near a patient. Alerts may be generated by the monitoring device if the sensor readings fall outside predetermined limits set in a patient profile specific to a particular patient. Sensor readings and / or alerts may be sent by the monitoring device to the central server which may notify nearby caregivers that a patient needs assistance. The server may be configured to analyze sensor readings and alert information to refine patient profiles to reduce or eliminate false alarms.

An example digital therapy platform is disclosed that provides personalized recommendations. Patient data is collected from a plurality of data sources associated with a patient profile of a patient in the digital therapy platform. At least a subset of the patient data is processed to detect a patient event. In response to detecting the patient event, a personalized recommendation is generated. The personalized recommendation is associated with the patient profile and generated using at least one machine learning model. An action is invoked in the digital therapy platform based on the personalized recommendation, and the patient profile is adjusted to reflect the action. The digital therapy platform causes presentation of at least one of a first indication of the action at a first device associated with a therapist assigned to the patient profile or a second indication of the action at a second device associated with the patient.

The invention relates to a computer-implemented method and system for automatically providing disease information relating to verification of a suspected diagnosis, comprising the following steps: providing at least one disease database, which contains a plurality of disease patterns relating to one or more diseases, and at least one patient database, the patient database comprises a plurality of electronic patient files with patient information; extracting a disease pattern for diagnosing a disease of the patient from at least one provided disease database based on the suspected diagnosis; matching the extracted sick pattern with patient information of an electronic patient profile of the patient loaded from a patient database by using a mathematical model for automatically determining at least one piece of disease information for suspected diagnosis; and outputting the determined disease information via the first user interface.

The invention provides a DTP medical advice automatic notification method and system based on short message triggering, and belongs to the technical field of DTP medical advice automatic notification. The method comprises the following steps: verifying and synchronizing subject information according to subject data, monitoring transition of a prescription state and generating a trigger event; calling a preset message template library, and matching to obtain a corresponding first short message content and an acceptance role; the subject information is processed based on a predefined label rule base, a patient portrait label is constructed, and a doctor advice notification risk score of a patient is calculated; and according to the patient portrait label and the patient medical advice notification risk score, adjusting the first short message content to obtain a second short message content, and according to the determined short message notification mode, touching the second short message content to the accepting role to complete DTP medical advice automatic notification. According to the invention, through a state driving mechanism, mode change from passive waiting to active triggering is realized. The optimal notification strategy is intelligently matched by fusing patient portraits, testee ages and the like.

The invention, which relates to the technical field of clinic management business, discloses a clinic treatment auxiliary system comprising a medicine management module, a member management module, a patient management module, an outpatient service management module, a pharmacy management module, an execution management module, a reservation management module, an examination management module and a number calling large screen module. The medicine management module is used for counting according to the consumption state of each type of medicine; the member management module is used for managing member assets accumulated by the patient, such as points, recharge and balance, and associated family members enjoy common rights and interests; the patient management module is used for the system to automatically record files for each patient seeing a doctor in a clinic; the outpatient service management module comprises a patient file, doctor seeing information, doctor seeing information and a viewing report; the pharmacy management module is used for providing basic data, such as medicine names, specifications, usage and quantity information states, required by doctors during medicine preparation and also realizing online diagnosis and treatment and online medicine preparation functions; the execution management module is used for displaying patient archives and execution prescriptions; the appointment management module is used for enabling the patient to do not need to queue up and wait offline after online appointment time; the inspection management module displays the inspection analysis report according to the inspection; according to the clinic treatment assisting system, doctor-patient communication is facilitated, appointment and registration of the patient are facilitated, the queuing time for seeing a doctor is saved, and the efficiency is improved.

The invention discloses a patient file generation method and device applied to clinical tests and a medium, and relates to the technical field of data processing. The method comprises the steps that field metadata and text description information are typeset and combined through a visual editor, a patient file structure module is constructed, visual self-defined editing is achieved, and different medical fields and scene requirements can be rapidly met. By constructing the converted patient data, integration of multi-source data is realized, so that when the structured template is modified, traversing and searching of each data source are not needed, the data integration rate is improved, batch patient archive generation of multi-target patient IDs is supported, the patient archive generation efficiency is improved, the structured template can be stored in a template library after being designed, and the generation efficiency of the patient archives is improved. And when other application scenes need, the information can be directly called, so that the time consumption and the error probability of newly creating the patient file are reduced while the unified management of the structured template is realized. Therefore, the generation efficiency and quality of the patient file are improved.

A handheld electronic drug-requesting device includes a handle configured to be grasped by a patient's hand, a display embedded into the handle, and a touch-activated control integrated in the handle or the display. The handheld device performs operations that include determining the device is electronically coupled to a drug-delivery device for delivery of a drug to the patient and, in response to determining the device is electronically coupled to the drug-delivery device, receiving information related to a patient profile associated with the patient, presenting a user interface element on the display to the patient including the information related to the patient profile associated with the patient, receiving a patient request to deliver a dose of the drug to the patient via the touch-activated control, and, responsive to the patient request, causing the dose of the drug to be delivered.

A drug administration management method according to one embodiment disclosed herein may comprise the steps of: receiving identification information of a first drug from a pharmacist terminal; transmitting permission information corresponding to the identification information of the first drug to the pharmacist terminal; receiving customized medication guidance, in which the permission information is personalized according to patient profile information of a patient, from the pharmacist terminal, and storing the customized medication guidance in a patient information database; receiving identification information of a drug from a patient terminal and searching for the identification information of the drug in an internal database; and generating a customized administration schedule for the patient according to administration schedule information of the first drug included in the customized medication guidance if the searched identification information of the drug is the identification information of the first drug.

An example embodiment may involve receiving, by way of a user interface of a computing system, an indication of a healthcare patient profile and further input; based on the indication of the healthcare patient profile, obtaining, by the computing system, health records and genetic data of a patient associated with the healthcare patient profile; based on the health records and the genetic data of the patient and the further input, generating, by the computing system, a natural language prompt, wherein the natural language prompt contains instructions to generate a healthcare document for the patient; providing, by the computing system, the natural language prompt to a generative natural language model; receiving, by the computing system, the healthcare document from the generative natural language model; and providing, by way of the user interface, a version of the healthcare document.

Systems and / or methods for assessing the sleep quality of a patient in a sleep session are provided. Data is collected from the patient and / or physician including, for example, sleep session data in the form of one or more physiological parameters of the patient indicative of the patient's sleep quality during the sleep session, a subjective evaluation of sleep quality, etc.; patient profile data; etc. A sleep quality index algorithm, which optionally may be an adaptive algorithm, is applied, taking into account some or all of the collected data. Sleep quality data may be presented to at least the patient, and it may be displayed in any suitable format (e.g., a format useful for the patient to be appraised on the progress of the treatment, a format useful for a sleep clinician to monitor progress and / or assess the effectiveness of differing treatment regimens, etc).

The invention provides an epileptic information management method and system, and relates to the technical field of medical information management. The method comprises the following steps: firstly, performing standardization processing on multi-source heterogeneous data of a patient, and constructing a multi-dimensional patient portrait containing static, dynamic and time sequence features; secondly, performing multi-level clustering on historical patients based on treatment response and disease course evolution similarity; generating a management suggestion according to the association degree of the to-be-managed patient and the clustering cluster and the current management scene; and finally, dynamically updating the portrait and the clustering affiliation based on treatment feedback. According to the invention, by mining time sequence features and establishing a closed loop feedback mechanism, precise classification and dynamic personalized management of epileptics are realized, and the accuracy of clinical treatment recommendation is significantly improved.

The invention discloses a patient disease data management system, and relates to the technical field of medical information, the patient disease data management system comprises a data management module, a patient symptom module and a communication sharing module, the data management module is in communication connection with the patient symptom module, and the patient symptom module is in communication connection with the communication sharing module; the data management module is used for managing a database and completing normal database operation, the patient symptom module is used for storing and updating symptom data in a patient database, and the communication sharing module is used for managing a mode of communicating a treatment scheme and a conditioning means for a patient. The data management module comprises a database transmission module and a patient self-recording module, the patient self-recording module is in communication connection with the database transmission module, and the patient file module is in communication connection with the symptom filing module; and the database transmission module is used for registering a newly registered patient in the database in the medical system. The system and the method have the characteristic of accurate matching.

Disclosed embodiments may include a method for matching patient data to clinical study sponsors. The system may receive a first data package comprising one or more first patient profiles comprising a plurality of data elements each associated with a respective patient. A data tag indicating a data type for each data element may be assigned. The first data package and the data tags are stored. Study criteria comprising inclusion and exclusion criteria for a first study are received. Each of the inclusion and exclusion criteria of the first study are mapped to a respective data tag. Based on the mapping and the data tags assigned to each data element, one or more second patient profiles from the first data package are identified that are compatible with the inclusion and exclusion criteria. Each data element associated with the identified one or more second patient profiles is shared with the user device.

A patient centered social platform according to systems, methods, and apparatuses of the present disclosure may include a patient database module configured to interpret at least one of a plurality of patient profile values corresponding to a user of the patient centered social platform, a stakeholder database module configured to interpret a plurality of stakeholder service values, and a patient interface module configured to implement a user social interface and to provide a patient engagement communication to the user on the user social interface in response to the corresponding at least one of the patient profile values and the plurality of stakeholder service values.

This invention discloses a method, system, and medium for dynamic prediction of pregnancy complications in SLE patients, belonging to the field of medical information technology. It aims to solve the technical problems of existing risk assessment methods, such as the inability to effectively integrate multi-dimensional time-series data and the lack of causal mechanism analysis of the dynamic evolution of risk, resulting in biased, delayed, and clinically inoperable prediction results. The method includes the following steps: S1: Acquiring multimodal health data of the target patient during pregnancy as a time series; S2: Based on the multimodal health data, constructing and updating a dynamic patient profile reflecting the patient's psychosocial state, immune inflammatory activity, and target organ function; S3: Inputting the dynamic patient profile into a trained risk prediction model. This invention, by constructing a dynamic patient profile and employing a time-series deep learning model for risk prediction, achieves a comprehensive, dynamic, and mechanistically transparent risk assessment of pregnancy complications in SLE patients.

A system for real-time generation of therapeutic meal plans based on predictive analytics of patient profile data including a processor of a therapeutic plan server (TPS) node configured to host a machine learning (ML) module and connected to at least one patient-entity node over a network and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: receive patient profile data from the at least one patient-entity node; parse the patient profile data to derive a plurality of key classifying features; query a local database to retrieve local historical patients-related data based on the plurality of key classifying features; generate at least one classifier feature vector based on the plurality of key classifying features and the local historical patients-related data; provide the at least one feature vector to the ML module coupled to an Artificial Neural Network (ANN); receive a plurality of meal recommendation parameters from a meal plan predictive model generated by the ML module using outputs of the ANN based on the at least one feature vector; and generate a meal plan for the at least one patient-entity node based on the plurality of meal recommendation parameters.

The invention discloses an electronic medical record analysis and personalized rehabilitation recommendation method and system based on a large model, and relates to the technical field of medical information processing and artificial intelligence. Comprising the steps of collecting multi-source medical data of a patient from a hospital information system; preprocessing the multi-source medical data; fusing the preprocessed multi-source medical data, and constructing a multi-modal patient portrait; inputting the constructed patient portrait into a pre-trained artificial intelligence analysis model, and performing multi-dimensional analysis to obtain an analysis result; constructing a personalized rehabilitation recommendation engine; based on the analysis result, generating a rehabilitation recommendation scheme through a personalized rehabilitation recommendation engine; in the execution process of the rehabilitation recommendation scheme, continuously collecting feedback data of the patient, and performing incremental learning and retraining on the artificial intelligence analysis model. According to the method, the electronic medical record can be automatically and accurately analyzed, and the personalized rehabilitation recommendation scheme is generated based on the analysis result.

A device, system and method is provided for predicting risk of hypoglycemia and adjusting an insulin medicament dose size for a subject being on pen based multi-daily injections. A first algorithm is provided for computing a first insulin dose size based on a plurality of timestamped blood glucose (BG) measurements and insulin injected dose sizes. A risk prediction module is adapted to, based on machine learning algorithms, predict risk of hypoglycemia based on the timestamped BG measurements and calculate a second insulin dose size resulting in a non-hypoglycemic state. In case the second dose size is lower than the first dose size, the first dose size is automatically adjusted down to the second dose size and communicated as the recommended dose size to the subject.

Disclosed herein are methods, kits, and devices for improving cognitive function and memory through olfactory stimulation. In some embodiments, olfactory stimulation is performed by releasing one or more scents according to an olfactory stimulation regimen or schedule. The scents can include scents that are familiar to a patient as derived from a patient profile. The methods, kits and devices described herein can provide a large impact on cognition with minimal effort and cost. They can be used widely and effectively among older adults, children, and other populations in need of improved cognitive performance.

The invention discloses a traditional Chinese medicine expert remote medical assistance system based on AI, and the method comprises the following steps: filing a doctor seeing patient at a patient server, and supplementing the original medical history information into a patient file; selecting information collection at an outdoor end or a hospital end according to the actual condition of the patient; the collected data is subjected to auxiliary analysis through a MobileNetV3 algorithm, and an existing patient is subjected to pre-diagnosis; and matching traditional Chinese medicine experts with appropriate qualification, inquiry direction and inquiry experience according to a pre-diagnosis result, and performing doctor-patient butt joint to complete inquiry. According to the method, multiple traditional Chinese medicine signs are collected and pre-diagnosed through the MobileNetV3 model, the corresponding traditional Chinese medicine specialist is matched according to the pre-diagnosis result, and after the traditional Chinese medicine specialist and the patient are successfully matched, medical record data of the patient and related information about inspection diagnosis, smelling diagnosis and cut diagnosis are directly obtained, so that the diagnosis efficiency is improved. And pre-diagnosis and drug recommendation given by the AI system can be obtained.