2472results about "Electronic clinical trials" patented technology

An apparatus and method for real-time assessment and mapping of quality of life indicators includes: selecting a set of quality of life dimensions, receiving objective quality of life indicators based on the quality of life dimensions, receiving subjective quality of life indicators based on the quality of life dimensions, performing a statistical analysis on the objective quality of life indicators and the subjective quality of life indicators, storing the objective quality of life indicators, the subjective quality of life indicators, and the results of the statistical analysis in a database, and outputting the objective quality of life indicators, the subjective quality of life indicators, and the results of the statistical analysis.

A computer implemented method for governing risk actions by an artificial intelligence (AI) browser, by classifying a proposed action by the AI browser based on a large language model (LLM) as safe or risky based on AI weights or based on policy rules; initiating a step up authentication flow for a risk action; presenting an action summary and required capabilities to the user for approval; and enforcing user configured spend or scope limits on the risk action.

A programmatically generated AI avatar includes a customizable personality module, acting as the embodied interface for a powerful AI “mind” that delivers personalized coaching to improve user health, well-being, and longevity. The system uses machine learning, large language models, and biometric modeling to synthesize real-time, multi-modal health data—including sleep, nutrition, glucose, mood, and activity—and generate forward-prescribed KHAs. Unlike human coaches, it continuously adapts based on context and behavior, targeting the root cause: metabolic dysfunction—namely by restoring healthy, sustainable body composition through the preservation or building of lean muscle mass and reduction of excess fat. KHAs can also be shared with friends or programmatically generated AI avatars, allowing for coordinated action, emotional support, and accountability through social connection—further reinforcing positive behavior and adherence. The system's reinforcement learning engine incorporates both individual response data and anonymized population-level insights to optimize recommendations over time, learning which interventions are most effective for users with similar physiological and behavioral profiles. First validated with Olympic athletes—resulting in measurable improvements and medal-winning outcomes—this system offers a scalable, emotionally intelligent coaching engine that exceeds human capability, designed for the ultimate purpose of supporting sustainable health, resilience, and human thriving.

The invention relates to the technical field of computers, and discloses an integrated information system for clinical test archives and a data processing method thereof, and the method comprises the steps: constructing a standardized data model covering a whole process; multi-source data dynamic structured intake and integrity verification are carried out; performing multi-dimensional semantic association analysis based on a Bayesian network and a correlation coefficient; block chain type version tracing and difference comparison are carried out; performing role-sensitivity-operation-context four-dimensional access control; and task-driven collaborative sharing and auditing log records. The system comprises a unified modeling module, a data intake module, a semantic analysis module, a version tracing module, an access control module and a collaborative auditing module. The problems of data islands, semantic segmentation, poor dynamic adaptability, weak safety protection and the like in the prior art are solved, intelligent management, semantic interconnection, safety controllability and efficient collaboration of the whole life cycle of clinical test files are achieved, and data quality, audit compliance and multi-role collaboration efficiency are remarkably improved.

The invention provides a tumor patient clinical test matching system and method based on a large language model and an OCR technology, and is applied to the field of medical data processing. The method comprises the following steps: analyzing clinical data and test information, processing an unstructured text, and generating structured clinical feature data through context association analysis; key data is extracted and subjected to double verification correction, and structured data supplementary information is generated; enhancing the structured clinical feature data and supplementary information based on a multi-modal processing assembly line module, extracting an image quantitative index, analyzing an immunohistochemical result, and generating a comprehensive matching score; through a rule engine and semantic similarity calculation, item-by-item comparison of patient features and entry and exhaust conditions is realized, and a preliminary matching result is generated; edge case misjudgment is corrected through context-aware multi-round reasoning, sorting is adjusted in combination with clinical test priority weights, and an optimized clinical test matching list is generated; and generating a clinical test matching report based on the data.

The invention relates to a motor imagery ability evaluation and training enhancement system and method based on electroencephalogram and a medium, and belongs to the technical field of brain-computer interfaces. The system comprises an electroencephalogram acquisition device, a processing terminal and a display device. By collecting and analyzing electroencephalogram signals of a subject, the system extracts time-domain, frequency-domain and space-domain features by using a multi-feature fusion technology, so that accurate quantitative evaluation of motor imagery ability is realized. The evaluation core index is a lateral index. A built-in self-adaptive training module dynamically adjusts training difficulty and comprises a basic mode, a middle-level mode and a high-level mode, and personalized efficient training is ensured. The method comprises pre-training guidance, data acquisition and processing in formal training, and adaptive training adjustment based on an evaluation result. By combining multi-feature fusion and an adaptive training mechanism, an efficient and personalized solution is provided for evaluation and enhancement of motor imagery ability, so that the rehabilitation training effect of a motor imagery brain-computer interface system is more effectively improved.

The invention relates to the technical field of data processing, and discloses an old people health state assessment data processing system based on multi-modal data, and the system comprises a medical data integration module which obtains electronic medical record data and a medical examination report through an FHIR interface, and extracts a structured health index; the cross-modal causal fusion processing module is used for fusing the monitoring data and the medical text through an image, text and image interlayer architecture; the health state evolution modeling module is used for mapping the health feature vectors into physiological function, cognitive level and athletic ability three-dimensional state indexes; an evaluation report backtracking module; and a decision output module. Through an image, text and image interlayer architecture, deep semantic fusion of multi-modal features is realized under the constraint of medical pathology rules, feature weight adaptive distribution is dynamically guided based on a medical causal atlas, a high-dimensional fusion vector retaining key pathology information is generated, the semantic integration ability of health data is remarkably improved, and the health data fusion efficiency is improved. And the reliability of discrimination and decision making is improved.

The invention discloses an acute pancreatitis complication assessment method based on artificial intelligence, and belongs to the technical field of medical information, and the method specifically comprises the steps: firstly, obtaining a clinical data set containing real-time clinical indexes and basic disease historical records; performing time sequence analysis on the historical records of the basic diseases, extracting long-term influence characteristics of the historical records of the basic diseases and generating basic disease influence factors; carrying out standardization processing on the real-time clinical indexes and the basic disease influence factors, and generating a comprehensive feature vector after calculating association weights among features; inputting the vector into a trained artificial intelligence evaluation model, and outputting a complication risk probability through layer-by-layer nonlinear transformation; and finally, mapping the risk probability value into a risk level, and integrating patient information to generate a structured risk assessment report. And an acute pancreatitis complication evaluation scheme integrating the acute stage index and the chronic basic disease influence is established.

The invention provides an abnormal blood sampling inspection data evaluation processing method and system of a blood sampling system. Wherein the diffusion track dynamic state of an anticoagulant-blood contact surface in a blood sampling tube is tracked in real time, dielectric response waveform distortion characteristics on the two sides are synchronously collected through a capacitance sensor array, and diffusion form parameters are generated; curvature sudden change is analyzed to trigger an acoustic fluid device to modulate audio frequency and flow velocity, and a composite disturbance field is formed; performing capacitance axial scanning on the disturbed mixed fluid, extracting phase angle offset and gradient abrupt change point coordinates, and constructing a dielectric anomaly three-dimensional map; matching a standard dielectric model, positioning a coordinate set of a concentration gradient imbalance area and a coordinate set of an eddy current attenuation area, and generating an anticoagulant abnormal index and a mixed defect matrix in combination with phase angle offset difference; and establishing a data anomaly degree evaluation function based on the anomaly index and the defect matrix, dynamically associating a clinical error threshold, and outputting a test evaluation result. According to the invention, anticoagulant diffusion can be monitored in real time, and the mixing quality error can be accurately evaluated.

Described herein are a system, method, and device for configuring a generative machine learning model using a syntactic interface. A system may include a user interface, a memory, and a processor configured to, using a syntactic interface displayed using the user interface, receive a syntactic interface input from a user; identify an electronic medical record (EMR) by generating an EMR database query as a function of the syntactic interface input, querying an EMR database using the EMR database query, and receiving, from the EMR database, an EMR database response; generate a prompt as a function of the syntactic interface input, generate a first generative model output as a function of the prompt and the EMR using a trained generative machine learning model and using a conversational interface displayed using the user interface, display the first generative model output to the user.

The invention discloses a multi-modal data fusion and intelligent analysis high-precision sleep monitoring system and method. The system comprises a data acquisition layer, a transmission layer, an analysis layer and an application layer, the data acquisition layer transmits data acquired by a sensor to the transmission layer step by step through BLE or Wi-Fi, the transmission layer divides the data acquired by the sensor into two paths through a Wi-Fi module and transmits the data to a main control device and a cloud server in the analysis layer, and the main control device and the cloud server process the data. The data collection layer comprises a data collection module, the data collection module comprises a physiological data collection unit, an environment data collection unit and a subjective data collection unit, the data collection layer is used for collecting data of multiple sources such as physiological data, environment data and subjective data, and the data collection layer is used for collecting data of multiple sources such as the physiological data, the environment data and the subjective data. The deep learning and big data analysis technology is applied, high-precision recognition of the sleep state and precise evaluation of health risks are achieved, and a personalized sleep improvement scheme is provided.

The invention discloses an interpretability analysis and decision sharing verification method and system for a rectal cancer prognosis model, and relates to the field of medical artificial intelligence interpretability. The method comprises the following steps: carrying out gradient weighting class activation mapping analysis on a prognosis model to generate an image thermodynamic diagram; calculating the contribution degree of the multi-modal features by using an SHAP interpreter; an integrated visual interface is constructed, and patient data, model prediction and the explanation result are presented to a doctor together; the doctor performs independent risk assessment based on the interface information; finally, decisions of doctors and the model are compared, and model auxiliary efficiency is evaluated. Through a doctor-model decision sharing verification mechanism which is explained and innovated in a multi-level mode, the transparency and clinical credibility of the complex AI prognosis model are remarkably improved, the value of time sequence data in dynamic risk assessment can be verified, and clinical landing application of the AI model is powerfully promoted.

The invention discloses a medical diagnosis report generation method based on a large model and interaction and related equipment, and belongs to the technical field of natural language processing and medical informatization. According to the invention, through multi-modal data acquisition and dynamic inquiry path adjustment, collection of comprehensive clinical information is ensured. And a symptom-examination result consistency verification and diagnosis basis sufficiency evaluation mechanism is introduced, so that the logic preciseness of medical judgment is ensured. By constructing the medical knowledge graph, related diagnosis and treatment guidelines are automatically provided, and omissions and wrong references caused by manual consultation are avoided. A first draft of differential diagnosis and treatment suggestions is automatically generated by means of a large language model, and the professionalism and safety of report content are improved in combination with examination and verification of a clinical rule engine. A doctor participates in modification through multiple rounds of interaction in the whole process, the main right of professional judgment is reserved, intelligent support provided by AI is fully utilized, and therefore the finally generated diagnosis report conforms to the actual clinical situation and meets medical specifications and specialized requirements.

This application describes, amongst other things, methods and systems for building and deploying agents. An example method includes obtaining orchestration data about a set of task-specific components selected to provide a response to the prompt, where each respective task-specific components in the set of task-specific components is configured to assist with a respective clinical task of the one or more clinical tasks. The method further includes, determining an order in which each respective task-specific components of the set of task-specific components should be utilized to prepare a complete response to the prompt that address the one or more clinical tasks based on the obtained orchestration data about the set of task-specific components. The method also includes, in accordance with the determined order, providing first data related to the prompt to a first task-specific component and receiving a first response from the first task-specific component.

A hospital quality abstraction is automatically generated from health records. In some examples, a large language model (LLM) is queried with prompts selected to elicit data to generate a hospital quality abstraction report. The LLM outputs are combined with patient data from health records to improve the accuracy of the responses to the LLM queries. These methods and systems may improve the operation of the LLM, which is deeply needed particularly for healthcare.

Systems and methods for providing an intelligent network to onboard patients to specialty medications are described herein.

The invention relates to the technical field of medical data processing and human-computer interaction, and particularly discloses a rehabilitation training feedback adjusting system based on a brain-computer interface and myoelectricity sensing. The system comprises a multi-modal physiological signal acquisition module, a signal fusion and intention decoding module, a rehabilitation state dynamic evaluation module, a self-adaptive feedback strategy generation module and a multi-channel feedback execution module. By synchronously collecting and fusing the electroencephalogram signals and the electromyographic signals to decode the motion intention, dynamically evaluate the rehabilitation state and predict the trend, a multi-sensory collaborative feedback strategy is adaptively generated, personalized and dynamic precise adjustment of rehabilitation training is achieved, and the training effect and the patient participation degree are improved. The system can autonomously switch between a reinforcement learning strategy and an auxiliary guiding strategy according to the real-time performance and the long-term trend of a patient, and dynamically optimize various feedback parameters.

The invention is applicable to the technical field of clinical medicine, provides a clinical multi-modal cancer drug response prediction method based on feature reconstruction, constructs a clinical multi-modal model for drug response prediction of diffuse large B-cell lymphoma, and aims to predict the drug response of diffuse large B-cell lymphoma by integrating gene sequencing and clinical multi-modal data. And accurate drug reaction prediction is realized. The model adopts an end-to-end multi-stage processing flow: firstly, extracting gene features through TransP-Net, and processing multi-modal clinical data by using a clinical information encoder; then, pseudo-gene features are generated through a clinical-genome filling module to deal with the data missing problem; and finally, multi-modal deep fusion is realized through a clinical information decoder, and a prediction result is output. According to the method, data characteristics and working processes in a real clinical environment are fully considered, two conditions of complete gene data and missing gene data can be processed at the same time, and the method has a good clinical transformation prospect and application value.

The invention relates to the technical field of modularized laboratory project management, and discloses a modularization-based laboratory project management method, which comprises the following steps: S1, system architecture design: adopting a micro-service distributed architecture, and cooperatively working through a plurality of business modules; s2, module management and project creation; the invention also provides a laboratory project management system based on modularization, which comprises a project management engine module, an intelligent key node reminding system, a sample full life cycle tracking management module and a multi-party collaborative feedback management module, the project management engine module comprises three core business modules, namely, a project management module, a sample management module and a contract management module, standardized management and efficient operation of laboratory projects are achieved through a project module engine, an intelligent node reminding mechanism and a full-process tracking algorithm, the project management efficiency can be remarkably improved, and the project management efficiency is improved. And the time cost of a project preparation stage is saved.

The invention relates to the field of clinical diagnostics, and discloses an elderly NSC emergency risk layering method and device and a medium, and the method comprises the steps: S1, obtaining multi-dimensional evaluation data of an elderly NSC patient; s2, adopting a preset risk mapping rule to convert the risk values into single risk values of a unified scale; s3, dividing the single risk value into a plurality of risk sub-models, and calculating a dimension risk score of each risk sub-model; s4, based on a preset fusion strategy, integrating the risk sub-model and the single risk value interaction effect, and calculating to obtain a total risk score of the patient; and S5, determining the risk level of the patient according to the total risk score, and outputting a clinical diagnosis and treatment suggestion. According to the method, multiple biomarkers and key clinical parameters are integrated, a multi-dimensional combined risk assessment model is constructed, and the potential pathological state of a patient can be reflected more comprehensively.

The invention discloses a child autism adaptive brain-computer fusion intervention system based on a large model, and the system comprises a multi-modal data collection module which is used for collecting the neural data, behavior data and clinical scale data of a user; the feature extraction and user portrait construction module is used for performing feature extraction and subtype recognition on the multi-modal data to generate a personalized portrait; the multi-defect intervention normal form generation module is used for dynamically generating a personalized intervention task on the basis of a large language model in combination with the personalized portrait and the historical state of the task; the self-adaptive regulation and control module is used for dynamically adjusting an intervention strategy and task difficulty according to the feedback of the real-time neural data and the dynamic change of the behavior data; the user interaction interface module is used for providing a multi-modal human-computer interaction interface; and the effect evaluation and long-term tracking module is used for generating an individual and group intervention effect report. By utilizing the system and the method, accurate, personalized, multi-dimensional and long-term intervention on the autism children can be realized.

The invention discloses a subject monitoring method for a clinical recruitment project and a related device. The method comprises the following steps: acquiring a state data set corresponding to each subject in the current clinical recruitment project; according to the state data set corresponding to each subject, probability calculation is carried out on the tested state of each subject in the current recruitment period, and a wastage rate prediction result corresponding to the current clinical recruitment item is obtained; and comparing the wastage rate prediction result with the state data set corresponding to each subject to obtain a subject monitoring result corresponding to the current clinical recruitment item. Based on this, the efficiency and accuracy of monitoring the subject in the execution process of the clinical recruitment project are improved.

The invention relates to a personalized traditional Chinese medicine health-care intelligent recommendation system combined with an acupuncture theory. The system comprises a multi-modal data acquisition module, an AI diagnosis module and an acupuncture scheme generation module. According to the invention, the health information of the user is acquired through the multi-modal data acquisition module, the deep learning model fused with the acupuncture meridian theory is utilized to realize physique identification and disease diagnosis, and the personalized acupuncture health care scheme is generated based on the established acupuncture point, physique and disease three-dimensional association database. A reinforcement learning algorithm is combined with a user real-time feedback dynamic optimization scheme, the limitation that traditional acupuncture depends on physician experience is broken through, intelligentization, individuation and dynamics of acupuncture health care are achieved, the accuracy and operability of the health care scheme are improved, and the method is suitable for various scenes such as family health care and community medical treatment and has a wide application prospect. The method has remarkable innovativeness and practicability, the physique and the disease of the user are accurately identified based on multi-modal data, the acupuncture scheme adaptive to personal characteristics is generated, and the problem that a traditional universal scheme is insufficient in adaptability is solved.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for medical condition identification. One of the methods includes obtaining visual data representing at least one body part of a subject; obtaining non-visual data that corresponds to one or more biological characteristics of the subject; providing data representing (i) the visual data and (ii) the non-visual data to one or more machine learning models, wherein the one or more machine learning models are trained to predict presence of a medical condition; obtaining an output of the one or more machine learning models that is generated based on the one or more machine learning models processing the visual data and the non-visual data; and determining presence of the medical condition for the subject using the output of the one or more machine learning models.

The invention provides a prostate cancer multi-modal data federal learning method and system, and relates to the technical field of intelligent medical treatment and distributed artificial intelligence cross, and the method comprises the steps: selecting a plurality of key feature regions in a feature space as a reference basis based on updated global feature parameters, and constructing a feature distribution topological structure; performing partition division on the feature distribution topological structure to generate a plurality of feature subspaces; calculating a spatial characteristic index according to the characteristic density and the distribution characteristic of each characteristic subspace, and generating a weight adjustment coefficient based on the spatial characteristic index; and dynamically optimizing the updated global feature parameters by using the weight adjustment coefficient to obtain optimized federal learning model parameters. Through multi-modal data feature alignment, gradient compression transmission, global gradient aggregation and parameter dynamic optimization, a complete prostate cancer multi-modal data federated learning framework is constructed, and model performance and cross-mechanism cooperation efficiency are improved.

The invention discloses a postoperative infection monitoring and early warning method and system, and relates to the technical field of clinical monitoring, and the method comprises the steps: collecting a postoperative infection multi-source alignment data set, building an individual reference model, dynamically extracting postoperative change features, building a behavioral physiological coupling model, recognizing abnormal interaction, and quantifying the infection risk. According to the method, multi-source data are fused, an individualized dynamic baseline model and a behavior-physiological coupling model are established, intelligent recognition and grading early warning of postoperative infection risks are achieved, and the method is high in practicability and easy to popularize. Model parameters are automatically corrected by utilizing a self-learning and feedback recharge mechanism, a closed-loop process of monitoring, prediction, intervention and optimization is formed, infection symptoms can be recognized in advance, manual dependence is reduced, the real-time performance and accuracy of postoperative infection prevention and control are improved, and intelligent decision support is provided for clinic.

It is possible to extract hidden problems of the user from the quantitative information and subjective information of the user, and to provide countermeasures for the extracted problems. [Solution] The support server 10 causes a generation AI (Artificial Intelligence) to extract the user's issues and / or worries for each user based on the user's quantitative information and the user's subjective information, which is the user's dialogue history regarding the user's past quantitative information. The support server 10 causes the generation AI to classify the issues and / or worries of female employees from integrated data that integrates the issues and / or worries of multiple users into one data set, by fitting them into a matrix diagram combining two items, and outputs countermeasures for the user's issues and / or worries for at least one combination of the combinations.

The invention provides a clinical test data security sharing method and device based on a block chain. The method is applied to the technical field of data processing, and comprises the following steps: extracting feature dimensions corresponding to various types of data, and calculating corresponding data sensitivity coefficients; determining a data sensitivity level of the corresponding data category; extracting corresponding associated metadata; selecting a corresponding encryption algorithm to encrypt the data, and generating corresponding encrypted data and a data abstract; uploading the associated metadata, the encrypted data and the data abstract to a block chain node, constructing a clinical test data sharing account book, and recording a timestamp and a node signature of data operation; extracting identity authentication information and historical access records of visitors, and calculating access credibility; judging whether the visitor is allowed to acquire the decryption key or not; after the visitor obtains the encrypted data, the integrity in the data transmission process is verified based on the non-tampering property of the block chain. Therefore, the security of clinical test data sharing based on the block chain is improved.

The invention discloses a teenager psychological sub-health intelligent early warning system based on multi-source heterogeneous data fusion, and the system comprises a data collection layer which collects the behavior, physiological and social three-dimensional data of teenagers through the multi-source channels of campus cards, wearable devices, social media and questionnaires, and builds an original data pool through cleaning, denoising and standardization; the data fusion layer is used for integrating multi-source data by adopting weighted average and Kalman filtering, mining psychological sub-health key indexes in combination with a feature selection and extraction technology, and forming a three-dimensional psychological portrait; the deep learning layer is used for constructing a multi-modal fusion early warning model based on a Transform architecture, and carrying out real-time prediction and dynamic tracking of psychological sub-health risks through large-scale data training and cross validation optimization; and the intelligent early warning layer is used for visually displaying an early warning result, integrating three-party linkage of a management end, a teacher end and a parent end, providing 24-hour online intervention by a built-in AI psychological counseling module, and automatically transferring high-risk cases to professional psychological consultants.

The invention discloses a children obesity early warning method and system based on obesity risk factor analysis, and the method comprises the steps: carrying out the feature screening of collected children multi-dimensional health related data containing parental information, birth history and lifestyle, and recognizing the important risk factors and weights of children obesity; important risk factors are supplemented through literature review; the method comprises the following steps: formulating scale questions including parental information, birth history and lifestyle based on important risk factors and weights in combination with clinical expert opinions, assigning scores to the scale questions, dividing according to a scale score result to form children obesity risk prediction levels, and providing personalized health management schemes for subjects with different risk levels. The method is low in cost, easy to implement, high in compliance and based on non-invasive characteristics, and is particularly suitable for early obesity screening and early warning of 3-6-year-old children.