3723results about "Electronic clinical trials" patented technology

The invention discloses a chronic disease early detection method and system based on a multi-modal large model, and relates to the technical field of intelligent medical treatment and artificial intelligence, and the method comprises the steps: obtaining a multi-modal data stream of a target user in a target time window from a pathology database, and generating an original multi-modal data set; performing timestamp unification and numerical value standardization processing on the original multi-modal data set to obtain a time sequence feature sequence; inputting the time sequence feature sequence to the multi-modal large model to obtain an abnormal symptom feature; calculating the similarity between the abnormal symptom features and feature vectors of marked cases in a historical case library, and determining matched cases; a diagnosis result and a development process of the matched case are extracted, a disease risk level and a development trend corresponding to the original multi-modal data set are determined in combination with the medical knowledge graph, and a pathology assessment result is obtained; and generating an early warning signal containing the risk type and the intervention suggestion according to the pathological assessment result. By implementing the application, the accuracy of early detection of chronic diseases can be improved.

Provided is a method including obtaining a prompt, determining a prompt embedding vector representing the prompt in an embedding space, modifying the prompt embedding vector using a trained model configured to adjust prompt embedding vectors to decrease proximity to vectors of blocks in a data set from which data is retrieved to augment generation by the generative AI model, determining that the modified prompt embedding vector is within a threshold distance to vectors in the embedding space corresponding to one or more blocks in the data set, selecting the one or more blocks in the data set, generating a response using the generative AI model based on the selected one or more blocks in the data set, quantifying an amount of influence of the respective block on corresponding text in the generated response, and providing the response and a representation of the quantified amount of influence as an output.

Provided is a method, including obtaining data associated with clinical trials, storing the obtained data into a repository by preprocessing the data to standardize diverse input formats into unified data model and organizing the stored data into a schema designed to integrate data of diverse input formats, indexing the stored data and analyses performed on the stored data, selecting one or more visualizations responsive to the query by selecting one or more visualizations as being responsive to the query based on metadata associated with each of the one or more visualizations, determining whether the stored data is associated with a plurality of metadata requirements of each of the one or more visualizations, dynamically generating executable code configured to generate the one or more visualizations responsive to the query, executing the generated executable code, and providing a response to the query.

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.

The invention relates to the technical field of medical information, in particular to an intelligent prediction model and method for postoperative complications of anesthetized patients, and the method comprises the steps: collecting preoperative to postoperative complete-cycle clinical data of a patient through a medical data interface; analyzing operation codes to generate risk features, extracting vital sign dynamic features, and establishing a complication probability mapping relation through a multi-modal fusion network; combining the complication probability and pharmacokinetic parameters to construct an optimization model, and solving an individualized anesthetic dosage interval by using a gradient descent algorithm; vital signs are dynamically monitored in the operation, a dose re-optimization mechanism is triggered, the infusion rate is adjusted, and a closed-loop control link is formed; and generating a visual decision report. According to the method, through deep integration of complete-cycle clinical data and multi-modal feature modeling, preoperative physiological parameters, operation coding semantic information and intraoperative vital sign dynamic modes are subjected to fusion analysis, a nonlinear mapping relation between dosage and complication probability is constructed, and the risk prediction precision and individualized adaptability are remarkably improved.

The invention provides a multi-mode prostate cancer biochemical recurrence risk layering prediction system based on artificial intelligence. Based on an Xgboost framework, a postoperative patient pathological panoramic pathological section scanning image is analyzed through end-to-end, multi-scale, multi-center and large-sample analysis, pathological information is utilized to the maximum extent, meanwhile, the prognosis risk of a patient can be evaluated more comprehensively in combination with clinical indexes such as CAPRA-S scores, and the method has obvious advantages compared with a traditional model. The method aims at better fitting the use scene of a hospital, the risk of prostate cancer recurrence of a patient is more efficiently and accurately predicted by fusing pathological section features and clinical features after a radical operation, and the risk of recurrence of the patient within 3 years and longer time after the radical operation can be accurately predicted. And an interpretable module is further combined to assist a doctor to interpret a result, so that precise layering and personalized follow-up visit of the BCR risk of the prostatic cancer patient are realized, the risk of excessive treatment and missed diagnosis is reduced, and the method has a good application prospect.

This specification describes systems and methods for performing endoscopy, obtaining medical images for inflammatory bowel disease (IBD) and scoring severity of IBD in patients. The methods and systems are configured for using machine learning to determine measurements of various characteristics related to IBD. The methods and systems may also obtain and incorporate electronic health data of patients along with endoscopic data to use for scoring purposes.

Disclosed are methods of improving treatment adherence and compliance after discontinuation of incretin-based therapies, such as GLP-1 and / or GIP agonists, using pharmacotherapies comprising the disclosed therapeutic compounds and combinations. In some aspects, methods include treatment of psychological factors affecting other medical conditions (PFAOMC) following the administration of incretin-based therapies to treat obesity or hyperglycemia, and related disorders. In some aspects, disclosed pharmacotherapies for use in the methods include agents that modulate monoaminergic neurotransmission, administered alone or together, such as in disclosed combinations having advantageous synergistic effects.

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 provides a patient pathological data analysis and evaluation method for clinical nursing, and relates to the technical field of nursing informatization. Comprising the steps of collecting biochemical and pathological data of a patient and standardizing units and reference values, analyzing the dynamic trend of detection values and classifying change directions and amplitudes, matching pathological labels and establishing association with nursing items, comparing differences between nursing records and the pathological data and establishing a transverse index relationship, and judging whether the nursing task difference exceeds a threshold value or not and adjusting a nursing path generation scheme. By integrating multi-source pathological data and standardizing units and reference intervals, data consistency and comparability are ensured, the index fluctuation trend is dynamically tracked, the change direction and amplitude are quantitatively analyzed, disease course evolution is accurately captured, direct mapping between pathological categories and nursing items is established, logic binding is clear, intervention precision is improved, and the method is suitable for clinical application. Differences between nursing records and pathological data are transversely compared, nursing dynamic adaptation is optimized, a path scheme is rapidly adjusted through threshold judgment, and the lagging or misjudgment risk is reduced.

The invention discloses a depression risk screening optimization method based on large and small model linkage. The method comprises the following steps: selecting depression related indexes, obtaining interviewee questionnaire data, and preprocessing the data to obtain a scale source database; a depression risk prediction model is constructed, and PHQ-9 measurement results are compared for model training and verification; training a dialogue strategy module of a semantic analysis enhanced fine-tuning training large language model, constructing answer mapping through dynamic question generation and dialogue flow control, and converting a natural language of a user into standardized data required by a small model; training a man-machine interaction reinforcement learning model, generating a depression risk screening result based on a small model, inviting a user to carry out recognition degree evaluation, and dividing feedback into two types of recognition and question; for different feedbacks, strengthening or correcting the current interaction strategy and prediction logic, and storing the audited data as high-quality data to a training database by the system for subsequent large model fine tuning; and outputting a result and performing result interpretation and suggestion by using the semantic analysis reinforced fine-tuning large language model. Hierarchical early screening of depression risks is carried out based on large and small model linkage.

The invention discloses a gestational diabetes auxiliary analysis system and method based on placenta ultrasonic textures, and the system comprises a multi-modal database which is used for storing placenta two-dimensional ultrasonic image data and clinical comprehensive data; the preprocessing module is used for screening the clinical comprehensive data and carrying out standardization processing and labeling on the image data; the image feature extraction module is used for constructing an image segmentation model based on a CNN network, performing image processing on the input placenta two-dimensional ultrasonic image through the image segmentation model, and obtaining an image feature data set; the risk prediction module is used for constructing a GDM risk prediction model; and the judgment module is used for carrying out risk classification on the GDM risk of the current pregnant woman by utilizing the image segmentation model and the GDM risk prediction model. By introducing a deep learning technology, intelligent analysis is performed on placenta ultrasonic images, clinical comprehensive data and other multi-modal data, and a reliable tool is provided for auxiliary analysis of gestational diabetes mellitus.

The invention discloses an intelligent system and method for fusing mental health evaluation in an interest scene based on an education robot. The system realizes non-intrusive mental health assessment by constructing a technical architecture of'multi-modal biological feature anonymization-quantum hybrid encryption-federated learning privacy protection-dynamic risk assessment '. The method is characterized by comprising the following steps of: (1) generating irreversible biological characteristic codes by adopting a chaos random number generator, and realizing anonymized association by combining a double hash chain technology; (2) designing a quantum enhanced hybrid encryption system, fusing an SM4 algorithm, NTRU post quantum cryptography and quantum key distribution; (3) developing a dynamic scene generator, and naturally fusing psychological test questions into interest topics by using a generative adversarial network (GAN) and a curiosity engine; (4) constructing a multi-modal emotion analysis system, and combining an LSTM + CNN hybrid model to realize physiology-behavior-voice data fusion analysis; and (5) deploying a federated learning framework, and protecting model training data through differential privacy noise injection (epsilon = 0.5).

An AI-powered system is disclosed for conducting medical interviews, generating comprehensive medical history reports, and providing clinical insights. The system leverages a large language model (LLM) fine-tuned on domain-specific medical datasets to generate interview prompts, iteratively refine responses based on natural language processing (NLP), and identify incomplete or inconsistent information. The system provides patients with education specific to their situation and tailored to varying levels of health literacy. The system ensures compliance with data protection regulations such as HIPAA and GDPR by employing advanced encryption techniques, multi-factor authentication, and secure data storage. The generated medical history report includes a structured clinical summary, and may include provisional diagnoses with associated ICD-10 codes, clinical analysis, suggestions for diagnostic evaluation and treatment, and other pertinent information. Additional features include EHR integration, customizability, scalability, multilingual support, bias mitigation techniques, and adaptability to diverse demographic groups, ensuring equitable and inclusive healthcare delivery.

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 present application provides techniques for implementing a skill component, configured to perform an assessment of a user, as part of a speech processing system. The system may receive a natural language user input requesting assistance. The skill component may, using one or more machine learning models, determine at least one characteristic of the natural language input (e.g., lexical embedding, acoustic embedding, topic, tone, etc.). The skill component may determine state data for a present session, where the state data indicates a topic of the natural language user input and / or a user state associated with the natural language user input. The skill component may determine past state data of one or more past sessions, and generate a question to the user based on the state data for the natural language user input and the past state data.

The invention relates to a medical decision-making method and device based on a neural symbol hybrid model, and a storage medium, and relates to the technical field of medical information processing. The method comprises the following steps: firstly, fusing multi-modal clinical data of a target object through an attention weighting mechanism to obtain multi-modal fusion representation; then, on the basis of a medical mask enhancement mechanism of a high-risk index, associating the high-risk index of the structure perception type cross-modal attention network constructed on the basis of multi-modal fusion representation with a medical index mask to obtain a joint feature vector of the target object; and inputting the joint feature vector into a neural network decision layer to obtain a neural network recommendation vector, and inputting the joint feature vector into a medical knowledge graph to obtain a symbol rule recommendation vector. And finally, inputting the neural network recommendation vector, the symbol rule recommendation vector and the joint feature vector into a three-layer neural symbol fusion network to obtain a target decision suggestion. Therefore, the accuracy, interpretability and clinical suitability of medical intelligent decision making are 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.

An exemplary system and method are disclosed that is configured to detect cognitive impairment (e.g., early cognitive impairment) or assess cognitive function by analyzing, via machine learning and artificial intelligence analysis, behavioral metadata collected from a smart app during the course when a subject is using a cognitive test instrument for cognitive tests that incorporate motor activity (e.g., drawing or writing). The machine learning and artificial intelligence analysis can execute features associated with the test taker's metadata (e.g., time spent on task or questions, changing answers, referring back to the previous question), drawing qualities (e.g., line straightness, completeness), among others.

The invention relates to the technical field of anesthesia patient physiological monitoring, in particular to an anesthesia preoperative risk assessment method and system, which continuously acquire physiological parameters of an anesthesia patient in a resting state, monitor physiological parameter changes in a dynamic physiological load state, screen physiological parameters exceeding a steady state range and generate a preoperative physiological monitoring data set. According to the method, the individual physiological steady state range is set through data comparison between the resting state and the dynamic physiological load state, accurate screening of parameters such as the heart rate, the blood oxygen saturation degree and the blood pressure is achieved, and the monitoring accuracy is improved. In combination with a load state fluctuation value, a resting mean value and a weight parameter, an individual physiological regulation range is calculated, so that evaluation is more personalized. And the preoperative risk adjustment coefficient is dynamically calculated, so that the risk assessment has the real-time adjustment capability. The heart rate recovery time, the blood oxygen fluctuation amplitude and the blood pressure recovery time are introduced, the physiological load ratio and dynamic load index classification are combined, and the risk assessment fineness is improved.

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.

A method for providing on demand verifiability of a medical metric for a patient using a distributed ledger is disclosed. The method may include receiving, by a node that is part of a network of nodes with access to the distributed ledger, a request to verify the medical metric of the patient, wherein the request comprises an identification of the patient. The method may include querying, using the identification of the patient, the distributed ledger to determine the medical metric of the patient, wherein the distributed ledger comprises at least one block including medical information pertaining to the patient, and the medical information is endorsed by a medical personnel associated with causing the medical information to be stored in the block. The method may include providing the medical metric to the computing device to cause a computing device to perform a responsive action based on the medical metric.