56 results about "Clinical decision support system" patented technology

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 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 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.

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.

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.

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 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.

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.

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.

A clinical decision support system and method for predicting a clinical response of a target tumor to an immune checkpoint blocking therapy (ICB) by: obtaining a training dataset, the training dataset comprising training transcriptome records of tumor tissue samples of a plurality of known responders and a plurality of known non-responders to the ICB; deconvolution is carried out on the training data set to identify differential expression genes DEG in the training data set, the DEG is regarded as features of the training data set, and related responder and non-responder states are regarded as labels recorded in the training data set; performing regression analysis on the features to select a feature subset which can predict the responder tag or the non-responder tag and related feature weights; incorporating the feature subset and the associated feature weight into a reaction estimation model; receiving transcriptome data of the target tumor tissue sample; the transcriptome data is processed using the generated response estimation model to generate an estimated response indicator indicative of a possible response of the target tumor to the ICB.

The invention discloses a primary osteoporosis traditional Chinese medicine clinical decision support system, and relates to the field of traditional Chinese medicine artificial intelligence, and the system comprises a data processing module which is used for carrying out the term standardization processing and syndrome element extraction of original traditional Chinese medicine literature data, and constructing a structured traditional Chinese medicine syndrome and treatment database; the dialectical reasoning module is constructed based on a machine learning model and is used for outputting a traditional Chinese medicine syndrome element diagnosis result according to the input four diagnosis information; and the prescription recommendation module is constructed based on a knowledge graph and is used for recommending corresponding treatment methods and rules and traditional Chinese medicine prescriptions according to the traditional Chinese medicine syndrome element diagnosis results and / or the specific clinical symptoms. According to the scheme, objectivity and consistency of syndrome element differentiation are remarkably enhanced, the defect that a traditional method is insufficient in composite syndrome type processing capacity is overcome, and the self-adaptive capacity of prescription recommendation along with dynamic evolution of clinical practice is achieved.

The invention relates to the field of migraine auxiliary analysis, and discloses a migraine patient risk assessment and clinical decision auxiliary system and method, and the method comprises the steps: querying biomarker detection data of a migraine patient, carrying out the data quantification, obtaining the quantified biological data, and recognizing the brain blood vessel region of the migraine patient; analyzing brain features of the migraine patient, performing feature quantization to obtain quantified brain features, and constructing a nerve-pathological model of the migraine patient; performing brain abnormality analysis on the migraine patient to obtain a brain abnormality analysis result, and performing risk assessment on the migraine patient to obtain a migraine state of the patient; the method comprises the following steps: constructing an auxiliary scheme for the migraine patient, collecting pathology-related data and life data, classifying a first auxiliary effect and a second auxiliary effect for the migraine patient to analyze the auxiliary improvement level of the migraine patient, and constructing a risk assessment-clinical auxiliary report. According to the invention, the reliability of risk assessment and clinical decision assistance of migraine patients can be improved.

The invention relates to the technical field related to medical artificial intelligence and clinical decision support systems, in particular to a critical patient artificial intelligence diagnosis and treatment scheme recommendation method, device and equipment and a medium. The method comprises: acquiring patient information; inputting the patient information into a preset diagnosis and treatment scheme recommendation model to obtain a first diagnosis and treatment scheme; wherein the diagnosis and treatment scheme recommendation model is a pre-trained deep learning model; comparing the patient information with each case in a preset case library, determining the case most matched with the patient information in the case library as a similar case, and determining a diagnosis and treatment scheme adopted by the similar case as a second diagnosis and treatment scheme; wherein the preset case library is used for performing case expansion based on actual cases of a hospital; if the first diagnosis and treatment scheme is the same as the second diagnosis and treatment scheme, outputting the first diagnosis and treatment scheme or the second diagnosis and treatment scheme; and if the first diagnosis and treatment scheme is different from the second diagnosis and treatment scheme, simultaneously outputting the first diagnosis and treatment scheme and the second diagnosis and treatment scheme for reference of doctors.

This invention discloses a method and system for predicting the risk of malignant cerebral edema after mechanical thrombectomy in ischemic stroke, aiming to address the pain points of existing prediction tools, such as insufficient accuracy, lack of clinical interpretability, and difficulty in directly supporting decision-making. The core solution involves: stably extracting key predictive variables from multi-source clinical and laboratory data through a multi-algorithm consensus feature selection mechanism (LASSO, Boruta algorithm); employing eight machine learning algorithms to select the best high-performance prediction model, and using repeated cross-validation and grid search for robust optimization; innovatively and deeply integrating the SHAP interpretability framework to achieve global, local, and individualized interpretation of the model predictions; and finally deploying it as an integrated clinical decision support system, outputting a visual report that combines risk probability, risk classification, and decision-making basis. This method significantly improves the early risk identification capability of malignant cerebral edema, and the model exhibits excellent discrimination and calibration.

The present invention is characterized in that it comprises: an initial evaluation unit that calculates the severity and risk of an infectious disease using patient information and determines a treatment type among home treatment, inpatient treatment, and ICU (Intensive Care Unit) by inputting the calculated severity and risk of the infectious disease into an artificial intelligence model or comparing it with preset criteria; a treatment guide unit that determines monitoring guidelines and treatment guidelines for a patient determined to receive home treatment based on patient information, the calculated severity and risk of the infectious disease, and the results of a medical history interview; and a monitoring unit that performs monitoring of the patient receiving home treatment according to the monitoring guidelines determined by the treatment guide unit.

Techniques for evaluating user interactions with a clinical decision support (CDS) system are disclosed. User interaction data is received associated with a user response to an advice item generated by the CDS system. The user interaction data is evaluated, such as to determine a user trust level associated with the advice item and / or the CDS system. A corrective action is generated based on the evaluation of the user interaction data. In some examples, the disclosed techniques identify and correct for user under-trust or over-trust in artificial intelligence (Al)-based CDS systems.

The invention discloses a multi-dimensional prognosis risk assessment method for hematopoietic stem cell transplantation, a clinical decision support system and a storage medium. The multi-dimensional prognostic risk assessment method for hematopoietic stem cell transplantation comprises the following steps: acquiring test data including microorganism data, immune data, medication data and time synchronization data; and according to the test data, carrying out multi-dimensional prognostic risk assessment solution through a pre-trained multi-dimensional prognostic risk assessment model to obtain multi-dimensional prognostic risk assessment. The invention discloses a multi-dimensional prognosis risk assessment method for hematopoietic stem cell transplantation, a clinical decision support system and a storage medium. The hematopoietic stem cell transplantation patient prognosis risk assessment or individualized intervention guidance can be realized by integrating multi-source heterogeneous medical data and adopting an intelligent prediction method of dynamic feature screening, time sequence effect modeling and interpretable output.

A clinical decision support system for retinal diseases processes OCT retinal scan images to enhance certain aspects of speckle, which has been found to include relevant information that can be used to enhance the diagnosis and classification of the severity and progression of any disease found in the retina scan image. A copy of the original OCT retinal scan image is processed using a shifted window transformer block to enhance the relevant speckle information, and combine it with the original image, which is then evaluated by a trained system to classify the retinal features, including the speckle.

The present application belongs to the field of pneumonia prognosis prediction, and in particular to a severe pneumonia post-pulmonary fibrosis prognosis prediction method and system based on multi-modal artificial intelligence. The present application constructs a database for storing clinical medical record texts, laboratory examination results and image CT data. Data acquisition is realized by connecting with a hospital information system, data preprocessing is carried out by using natural language processing, standardization processing and feature extraction, important features are selected after feature integration, a model is constructed and trained by selecting a suitable algorithm, and the model is verified and evaluated. Finally, the prediction results are integrated into a clinical decision support system, a personalized prediction report is generated and associated with clinical data, and a friendly front-end interface is provided for doctors to query, thereby providing a more accurate, efficient and clinically valuable solution for severe pneumonia post-pulmonary fibrosis prognosis prediction.

The present invention relates to a clinical decision support system comprising a processing unit in communication with a memory. The processing unit is configured to receive and process clinical data associated with a patient, which comprises medical records, biometric or physiological data, and / or currently prescribed medication information. The system computes personalized target lipid profile ranges based on the clinical data and medical guidelines stored in the memory. Preferably, the system receives pharmacogenetic analysis data and measured lipid profile values from patient blood tests. The processing unit compares the measured lipid values with the personalized target ranges and, if deviations are found, computes a list of potential medication treatments. This list is sorted based on the estimated effects of each treatment on the lipid profile values, the different between measured and objective lipid profile values, on the clinical data and / or on the pharmacogenetic analysis. The system further outputs data related to the personalized lipid ranges, the comparison of measured values, and / or the suggested medication treatments.