97 results about "Clinical decision support system" patented technology

The invention discloses an intelligent psychological intervention system based on multi-modal fusion, which is characterized in that a three-dimensional evaluation system is constructed by integrating speech sentiment analysis, keyboard dynamics monitoring and physiological signal acquisition, and time sequence alignment and feature weighted fusion of multi-source data are realized by adopting a cross-modal Transform model. The core of the system comprises an adaptive intervention engine which defines a multi-dimensional state space based on a hierarchical reinforcement learning architecture, optimizes an intervention strategy through a PPO algorithm, and realizes dynamic emotion interaction in AR and VR scenes in combination with a digital twin training module; according to the clinical decision support system, physiological behavior characteristics and psychological assessment trends are integrated by using a multi-time scale risk prediction model, and a personalized early warning threshold system is constructed, so that the psychological state recognition accuracy is improved, the intervention intensity self-adaptive adjustment response time is shortened, and the high-risk signal early warning timeliness reaches the minute level; and the problems of evaluation hysteresis and strategy stiffness of traditional psychological intervention are obviously improved.

Described are systems and methods for artificial intelligence (AI)-based clinical decision support. Systems can include a platform configured with a user input processing module, a context matching module, a retrieval-augmented generation (RAG) module, and an output generation module. Outputs of the platform can include a differential diagnosis, an assessment and treatment plan, or a clinical reference. The platform can further include an AI-copilot module and an AI-notebook module.

The invention discloses an acute kidney injury risk early warning system, and relates to the technical field of clinical decision support systems. In order to solve the problems that in the prior art, due to the fact that a large amount of data preprocessing and model training need long-time data processing and model operation, the early warning opportunity is not accurate, the real-time performance is low, personalized early warning prompting cannot be carried out for patient groups, and early warning fatigue and early warning desensitization are likely to be caused. Through data preprocessing, model construction and risk analysis, real-time monitoring and accurate prediction of the AKI risk of a patient are realized, the early recognition rate is improved, the early warning decision unit generates specific early warning information according to risk scores and kidney function change rules and provides targeted treatment suggestions, and meanwhile, through an early warning threshold adjustment and approach matching module, the early warning decision unit can perform early warning on the AKI risk of the patient. The early warning strategy is optimized, the response speed of medical staff and the compliance of patients are improved, and the user interaction unit continuously optimizes the early warning system through a feedback mechanism.

The invention provides a hematologic tumor cord blood transplantation treatment prognosis risk assessment method and system based on multi-modal data fusion, a medium and electronic equipment, and relates to the technical field of medical artificial intelligence and clinical decision support systems. The method comprises the following steps: acquiring multi-modal data including unit characteristic data of cord blood of a patient, biomarker data and the like; screening out a simplified feature set from the preprocessed multi-modal data; fusing the simplified features based on three complementary strategies of feature level direct cascade, knowledge graph semantic association and multi-model decision integration; and a prediction model is constructed, and a standardized risk assessment report is generated based on the optimized hierarchical risk levels and is used for assisting clinical decision support. According to the method, accurate layering of the prognosis risk of the hematologic tumor umbilical cord blood transplantation patient is achieved, high-accuracy risk prediction is provided, good interpretability and personalized decision support capacity are achieved, and a scientific basis is provided for clinical practice.

The present invention relates to a cognitive computing-assisted clinical decision support system designed to enable personalized neurological rehabilitation. The system acquires structured user data across clinical, anatomical, radiological, etiological, pathological, and rehabilitation domains to create individualized profiles. These profiles are mapped against a repository of historical cases using analog matching and similarity scoring to generate stratified, evidence-based rehabilitation recommendations. Real-time monitoring of rehabilitation progress is performed using global recovery and function outcome indicators, allowing for dynamic adjustment of treatment plans. Clinician intervention modules ensure safety, interpretability, and context-aware customization. The system incorporates a continuous feedback mechanism to refine future predictions and recommendations, making it increasingly adaptive over time. The invention improves rehabilitation outcome prediction accuracy, reduces recovery variability, and optimizes functional outcomes by transforming static rehabilitation models into intelligent, responsive, and personalized care pathways.

The invention provides a medical information retrieval enhancement method and device based on a large model and related equipment, and relates to the technical field of artificial intelligence. The method comprises the following steps: in response to a medical information query request, executing double-path joint retrieval of keywords and semantics in a pre-constructed medical knowledge base to obtain a candidate data set containing at least one medical information query result, the medical knowledge base is a database which analyzes the multi-modal medical data based on a multi-modal medical data deep analysis large model and is constructed according to the analyzed medical text data; and adopting a plurality of sorting tools to resort the medical information query results in the candidate data set, and performing fusion processing on a plurality of resorting results obtained by resorting to obtain a medical information query result after retrieval enhancement. According to the method and the device, the precision improvement and response efficiency optimization of medical knowledge retrieval can be realized, a high-credibility knowledge enhancement service is provided for a clinical decision support system, and the method and the device have important application value.

A novel clinical decision support system (CDS) helps the clinician set up, maintain, and interpret an esophageal pressure measurement. The esophageal pressure CDS (Pes CDS) would remind the clinician to do an occlusion test whenever the balloon is first inserted or changes dramatically. It could monitor the occlusion test and provide feedback on the performance and success of the occlusion test. Changes in the patient or monitored data can be tracked by looking for changes in the balloon baseline pressure, changes in the amplitude of the pressure waveform, or changes in the pattern of the Pes waveform. Having information from the ventilator will further increase the ability of the system to determine when Pes is changing unexpectedly.

A clinical decision support system and method for patients with pulmonary arterial hypertension is disclosed herein. The system may comprise a processor to process instructions to execute one or more pulmonary arterial hypertension risk algorithms configured to generate a risk score value associated with a patient surviving within a given time period. The system may comprise a means for input and output, wherein input variable data may be received and a set of risk score values may be displayed. A method for operating the clinical decision support system is also disclosed.

The invention relates to a sperm cell analysis and diagnosis system based on a multi-modal large language model, which comprises a multi-modal data co-processing unit, a cross-modal semantic alignment module, a dynamic diagnosis decision engine and a self-adaptive evolution system, the four-dimensional data processing module is used for synchronously processing microscopic images, motion trail videos, biochemical detection data and four-dimensional input data of medical record texts and comprises a feature selector based on a gating attention mechanism. According to the sperm cell analysis and diagnosis system based on the multi-modal large language model, quantitative analysis of sperm movement chaos features is realized for the first time, a nonlinear dynamic evaluation standard is established, a cross-modal knowledge distillation and meta-learning migration framework is developed, a data annotation dependence bottleneck is broken through, and an interpretable clinical decision support system is constructed; dynamic updating and probabilistic suggestion of diagnosis rules are achieved, semantic analysis of single-cell multi-omics data is achieved, and molecular mechanism research results are converted into clinically available knowledge.

The invention provides a liver cancer patient diagnosis data retrieval precise diagnosis and treatment system based on big data, and relates to the technical field of medical diagnosis. The liver cancer patient diagnosis data retrieval precise diagnosis and treatment system based on big data comprises a case database module, a clinical decision support system, a deep learning module, an early warning visualization module, a data analysis and processing module, a multi-modal data fusion module, a data collection and storage module, a feature selection and selection module and a data acquisition module. Image data, genome data, pathological data and electronic medical record data are integrated through multi-modal data fusion, comprehensive analysis is carried out, the accuracy of risk prediction is improved, treatment schemes are dynamically adjusted according to specific conditions and treatment responses of patients, personalized risk prediction and treatment scheme recommendation are achieved, and the risk prediction and treatment scheme recommendation efficiency is improved by combining gene data. Medicine selection and dosage are optimized, the treatment risk is reduced, the treatment effect is improved, doctors can understand the basis of system decision making, and the credibility of clinical application is improved.

The invention provides a cerebral hemorrhage hematoma enlargement prediction system and method based on deep learning, and the system comprises a data obtaining module which is used for obtaining a baseline CT image and clinical data of a patient; the image preprocessing module is used for carrying out standardization processing on the CT image; the feature extraction module is used for extracting image features from the CT image by adopting a deep convolutional neural network; the feature fusion module is used for fusing the image features, the clinical features and the radiology features; and the prediction module is used for predicting the hematoma expansion risk based on the fused features. The system captures 3D space information to the maximum extent by extracting the maximum lesion level, shows high reliability, high interpretability and clinical availability in the aspect of predicting hematoma expansion, is remarkably superior to prediction of clinicians, achieves reasonable consistency of prediction probability and actual probability on multiple data sets, and has good application prospects. The system can be used as a clinical decision support system and has potential to improve patient prognosis.

The invention discloses an interpretable clinical decision support system based on label generation and a knowledge graph. The method comprises the following steps: based on a breast cancer domain knowledge enhanced version Qwen-BrCaAdapt of a general large language model Qwen, analyzing an unstructured medical record text of a patient, and generating a structured result containing tags, values, evidences and explanations; calculating a reasoning label through a path matching engine by utilizing an editable structured path rule table, and matching a candidate treatment scheme according to the reasoning label; and taking the matched treatment scheme as a central node, calling a medical knowledge graph to bind entity information including clinical evidence, recommendation levels, medical insurance information, medication risks, usage and dosage, and generating a traceable JSON structure and a visual report. The method has the beneficial effects that the accuracy and efficiency of tag generation in the breast cancer field are improved, the rule maintenance cost is reduced, the interpretability and traceability of a clinical decision scheme are enhanced, and the acceptability of a doctor to a recommendation result is improved.

Various methods and systems are provided for a longitudinal patient history timeline and predictive clinical decision support system. In one example, a computing device comprising a display screen displays a menu listing one or more electronic medical records (EMRs) of one or more patients, and additionally is configured to display a patient timeline graphical user interface (GUI) accessible from the menu while the one or more EMR systems are in an un-launched state. The patient timeline GUI displays patient data as longitudinal medical history event elements and includes elements representing predicted outcomes generated by one or more artificial intelligence (AI) algorithms based on the patient data and one or more activity items generated from the patient data. The event elements and the predicted outcome elements are selectable to launch respective pop-up windows with additional information relating to the selected element.

Systems, methods, and computer-readable storage media are provided for determining and ascribing clinical conditions or diagnoses to patients and provide them to a caregiver, such as attending clinicians or other appropriate health services personnel. In particular, embodiments of the disclosure determine likely phenotypic findings that are salient to the decision-making context for a current human patient, based on anticipative sequence-mining and trajectory-mining. A sequential pattern mining and sequence itemset matching system is provided for determining likely, temporally-relevant concepts that are manifested in the information that is produced during the course of a patient's care. A clinician or caregiver may be provided the sequence itemset matching by generating a list or notice. In addition or alternatively, the results may be stored in an EHR associated with the patient.

The invention provides an AI doctor assistant agent medical information processing method based on voice interaction and large model driving, and relates to the technical field of intelligent medical treatment, and the method comprises the steps: obtaining the audio information of a doctor-patient conversation and a voice instruction through a voice collection module, and converting the audio information into a text in real time based on a real-time voice conversion module, the intelligent transfer service module is used for carrying out intention classification on texts and distinguishing doctor-patient conversations and voice instructions, the AI medical big model intelligent agent service module is used for generating structured medical information for the doctor-patient conversations, the structured medical information is automatically recorded into the electronic medical record system, and the voice instructions are automatically recorded into the electronic medical record system. And analyzing and executing corresponding desktop operation through the instruction response module and the desktop analysis large model service module. The method can consider performance, cost and data security, improves the accuracy and credibility of medical information, is suitable for electronic medical record input, clinical decision support, system operation automation and other actual scenes, and can help medical institutions improve the diagnosis and treatment efficiency and quality.

A method can involve receiving input data relevant to a patient encounter such that the input data may be compliant with a standard for exchanging healthcare information. The method may involve determining a subset of functions from a plurality of functions based on parameters associated with the patient encounter, conditions met by the input data, or a consuming system that received a customized report and for each function in the subset of functions, the method may perform at least one transformation on the input data such that performing the at least one transforming comprises producing an assigned dynamic value from among the plurality of dynamic values associated with the function. The method may involve automatically generating the customized report for the patient encounter based on the assigned dynamic values from each function in the subset of functions and transmitting an order request to order a lab test.

The invention discloses a traditional Chinese and western medicine combined clinical decision support system and method, and relates to the technical field of medical equipment, the system comprises a user interface module, a data management module, a knowledge base module, a rule engine module and a decision analysis module, the user interface module is used for obtaining patient information input by a clinician; the data management module is used for performing data cleaning and Chinese and western medicine medical term mapping processing on the patient information; the knowledge base module is used for tracking and updating medical knowledge and diagnosis and treatment guidelines in real time; the rule engine module is used for establishing a medical diagnosis and treatment rule base according to the medical knowledge and the diagnosis and treatment guide, calling a corresponding medical diagnosis and treatment rule from the medical diagnosis and treatment rule base according to the processed patient information, and generating a preliminary diagnosis result and a treatment scheme; and the decision analysis module is used for further performing decision analysis and optimization to obtain a final diagnosis result and a treatment scheme. The accuracy and efficiency of medical decision making of the clinical decision support system can be improved.

There is provided a processor configured for executing code external to a ELM and interfacing with the ELM for: grounding the ELM to predefined disorder specific structured flows, determining a differential diagnosis of mental disorders, for a first mental disorder, generating prompts for guiding the ELM for conducting a structured interview with the subject via a user interface according to a first disorder specific structured flow, dynamically analyzing a response(s) for determining inconsistency with the first mental disorder and determining consistency with a second mental disorder, dynamically switching from the first disorder specific structured flow defined for the first mental disorder to a second disorder specific structured flow defined for the second mental disorder for guiding the ELM, and iterating the generating prompts for guiding the ELM, the dynamically analyzing, and the dynamically switching, for obtaining the differential diagnosis of mental disorders that the individual is most likely suffering from.

The application discloses a similar case recommendation method and system, belongs to the technical field of clinical decision support system, and aims to solve the technical problem that the implementation difficulty of similar case recommendation is extremely great and the feasibility is not high by using a deep learning algorithm and a natural language processing technology.The technical scheme is as follows:the method is specifically as follows: data preprocessing: extracting the symptom and diagnosis information of the case admission record electronic medical record; and processing the case into a standard symptom and standard diagnosis list, and extracting the treatment information of the case for storage in a dictionary to assist in subsequent acquisition of the similarity degree of the case; wherein, the treatment information comprises age and department; obtaining the symptom weight: based on the knowledge graph of the symptom and disease related knowledge, extracting the weight of the symptom in the disease diagnosis; obtaining the similarity degree of the case: according to the symptom and diagnosis list of the target case, and fusing the weight to obtain the similarity degree of other cases and the target case; similar case recommendation.

The invention discloses a clinical decision support system fusing structured knowledge and generative intelligence. A guide map generation module is used for converting an unstructured authoritative clinical guide into a machine-readable and executable structured knowledge map; the medical record information extraction module is used for processing an unstructured medical record original text written by a clinician and extracting a clinical event sequence with a timestamp from the unstructured medical record original text; the decision reasoning module is used for receiving the structured knowledge graph and the clinical event sequence and driving a large language model to perform multi-step logical reasoning under the guidance of the structured knowledge graph so as to plan a diagnosis and treatment path; and the interactive display module is used for displaying the diagnosis and treatment path and the decision basis corresponding to each decision node on the diagnosis and treatment path in a visual mode. According to the method, the unstructured medical record information and the structured guide knowledge can be deeply fused, and normative and highly personalized decision support is provided for clinicians.

The invention relates to the technical field of medical artificial intelligence, in particular to a one-question multi-certificate task-oriented special retrieval embedding method, which comprises the following steps of S1, constructing a one-question multi-certificate training data set: systematically collecting authoritative medical guide document data from a plurality of authoritative channels, and establishing a corresponding document meta-information index table based on the collected data; analyzing a document chapter structure by adopting an OCR (Optical Character Recognition) algorithm in combination with font characteristics, a serial number structure and paragraph indentation and line feed rules, and constructing a chapter tree; inputting each structured TXT guide into a GPT-4 or other language model, adopting a customized prompt word Prompt to guide the structured TXT guide to complete multiple rounds of question and answer generation, and forming structured question and answer pairs Qamp in a uniform form; structure A; an automatic sampling mechanism is established, each time N documents are processed, the method can be seamlessly connected with an existing RAG framework, and rapid deployment and iteration in scenes such as electronic medical records and clinical decision support systems are facilitated.

Systems, methods, and computer-readable storage media are provided for determining and ascribing clinical conditions or diagnoses to patients and provide them to a caregiver, such as attending clinicians or other appropriate health services personnel. In particular, embodiments of the disclosure determine likely phenotypic findings that are salient to the decision-making context for a current human patient, based on anticipative sequence-mining and trajectory-mining. A sequential pattern mining and sequence itemset matching system is provided for determining likely, temporally-relevant concepts that are manifested in the information that is produced during the course of a patient's care. A clinician or caregiver may be provided the sequence itemset matching by generating a list or notice. In addition or alternatively, the results may be stored in an EHR associated with the patient.

The invention relates to the technical field of anaesthesia machines, in particular to an anaesthesia machine control parameter optimization method and a clinical decision support system, and the method comprises the steps: setting a safety interval of each control parameter of an anaesthesia machine; observation parameters and action space are determined, a reward function is designed, and a reinforcement learning model is constructed and trained; inputting observation parameters at each moment in the operation of the patient into the strategy network of the trained reinforcement learning model to obtain probability distribution of control parameters at each moment, and determining the value of each control parameter according to the safety interval; and sending the obtained values of the control parameters to a microcontroller of the anaesthesia machine so as to complete updating of the control parameters. By automatically optimizing the control parameters of the anesthesia machine, the workload of an anesthetist is reduced, and the physiological parameters of a patient are maintained within an expected range.

The present disclosure describes a system that automatically detects patient-ventilator asynchrony and trends in patient-ventilator asynchrony. The present disclosure describes a framework that uses pressure, flow, and volume waveforms to detect patient-ventilator asynchrony and the presence of secretions in the ventilator circuit.

A system and method for clinical decision support include a processor configured to receive a clinical service request; parse the clinical service request into a set of semantic tokens, classify the clinical service request according to the type, severity, and urgency of the clinical service request; execute a preliminary triage operation with the semantic tokens to identify a relevant clinical rule and a primary workflow for responding to the clinical service request based on a classification and / or label, the relevant clinical rule and primary workflow are each unique and tailored for a specific medical service provider organization; generate a decision support recommendation based on the relevant clinical rule and primary workflow; and transfer the decision support recommendation to an organizational processing subsystem for the specific medical service provider organization based on the primary workflow.

The present disclosure provides an AI-based clinical decision support system comprising an input module configured to receive clinical information comprising brain scan data, an analysis module configured to parse the clinical information using statistical measures from functional connectivity analysis with counterfactual explanations to identify brain connectivity patterns associated with brain disorders, and an output module configured to present a recommended diagnosis and explanation comprising attribution information identifying connectivity features contributing to the diagnosis. The brain scan data comprises functional magnetic resonance imaging, electroencephalography, and magnetoencephalography data. The statistical measures comprise functional connectivity analysis and graph theory metrics including degree metrics, betweenness centrality measures, and clustering coefficients. The analysis module comprises a functional connectivity engine configured to process brain scan data and generate connectivity features, a feature bank configured to store connectivity features, and modeling backbones configured to analyze connectivity features using machine learning techniques.

The invention discloses a clinical decision support system for treating pelvic adhesive infertility through combination of traditional Chinese medicine and western medicine, relates to the technical field of medical health and artificial intelligence crossing, and assists doctors in formulating personalized schemes, covering screening, diagnosis, scheme generation, curative effect evaluation, adaptation to gynecology of hospitals at the second level and above, acquisition of western medicine images, laboratory and gynecology data and diagnosis and treatment of pelvic adhesive infertility. Performing preprocessing filling missing values, marking abnormal values, western medicine disease differentiation, traditional Chinese medicine syndrome differentiation and correlation to form a combined diagnosis report, and marking a basis; matching traditional Chinese and western medicine schemes, and combining individual difference adjustment to generate a scheme containing a period and review time; and review data grading evaluation is carried out, reasons of invalid patients are analyzed, and a scheme is optimized. The system integrates traditional Chinese and western medicine diagnosis and treatment, improves diagnosis accuracy and scheme suitability, identifies drug interaction in advance to guarantee medication safety, dynamically evaluates the curative effect and adjusts the scheme, realizes full-period management, provides reference for doctors, adapts to different medical scenes, and improves the treatment effect.

High cost of biotherapy drug makes it unaffordable for patients to seek treatments. Further, access to information related to new low-cost alternatives like Biosimilars and Interchangeable may not be available with the physician at the time of consultation. Most of the conventional approaches aims to select an alternative biosimilar for a reference drug without considering patient's information. The present disclosure recommends a list of low-cost alternatives to high-cost reference drugs thereby enabling the physician to get timely and updated information on development of Biosimilars. The solution leverages Natural Language Processing (NLP) technology to extract known adverse events for a reference drug and a relative scoring based technique to identify and optimum alternative to prescribed biologics. The capability of the solution is further extended to identify secondary adverse events due to multiple drugs, thereby providing a clinical decision support system to help physicians take an informed decision.

A computer-implemented method of differentiating between lymphoid blast cells and myeloid blast cells comprises: receiving a digital image containing one or more blast cells; applying a parametric model classifier to one or more portions of the digital image each containing a respective blast cell, the parametric model configured to generate an output indicative of whether each blast cell is a lymphoid blast cell or a myeloid blast cell. Computer-implemented methods of training a parametric model are also provided, as well as a clinical decision support system relying on the computer-implemented method of classifying blast cells.

Embodiments of the present disclosure pertain to methods and clinical decision support systems for distinguishing between a first and a second condition in a subject. Additional embodiments of the present disclosure pertain to computer-implemented methods of distinguishing between the first and the second condition in a subject. Further embodiments of the present disclosure pertain to computing devices that are operable to distinguish between the first and the second condition in a subject.