421 results about "Clinical trial" patented technology

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.

The invention relates to the technical field of medical information, in particular to a medical data conjoint analysis system based on medical knowledge graph driving. The system comprises a medical knowledge graph construction module, a medical data quality evaluation module, a medical feature engineering module and a medical knowledge driven analysis module, and heterogeneous knowledge graph modeling can be performed by integrating medical clinical guidelines, biomedical literatures, a drug database and historical medical data so as to generate a medical heterogeneous knowledge graph; obtaining new medical clinical test information and carrying out delayed contradictory learning update to generate a medical dynamic update knowledge graph; the method comprises the following steps: obtaining multi-modal medical data, carrying out quality verification evaluation and medical feature engineering analysis, carrying out knowledge path joint driving analysis at the same time, generating a decision support reasoning path corresponding to medical clinical knowledge, and outputting a corresponding medical knowledge path confidence coefficient. According to the method, fusion reasoning among cross-source data can be realized by constructing the multi-modal medical knowledge graph.

The invention relates to the technical field of clinical experiment data processing, and discloses a clinical experiment data analysis method based on reinforcement learning, and the method comprises the steps: obtaining time sequence physiological parameters and treatment scheme execution state parameters; performing hierarchical feature extraction on the time sequence physiological parameters, constructing a dynamic response feature model, and generating an initial treatment response evaluation model in combination with the execution state parameters; loading risk constraints and curative effect indexes to generate a comprehensive evaluation model, and simulating to obtain strategy regulation and control data; constructing and training a reinforcement learning decision model, and generating a treatment path correction model and initial treatment parameters; obtaining dynamic strategy adjustment information, and constructing a multi-stage optimization model for iterative optimization to obtain optimal treatment path parameters; reasoning a curative effect standard reaching probability based on a real-time model, and adjusting a strategy to realize dynamic curative effect matching. According to the method, the clinical data dynamic analysis and treatment scheme optimization capability is improved, and the method is suitable for precise medical scenes.

The invention relates to the technical field of data analysis, in particular to a diabetes clinical test data intelligent clustering analysis system and method based on federal learning. Comprising a data acquisition and preprocessing unit; a federal privacy protection unit; the dynamic clustering modeling unit is used for constructing a clustering model of self-adaptive diabetes data features, and realizing joint clustering analysis of multi-source heterogeneous data by adopting a hierarchical federal architecture and a dynamic parameter aggregation algorithm and combining a diabetes course time decay factor and a clinical feature weight adjustment strategy; a double-track verification optimization unit; and an intelligent decision support unit. According to the method, the incidence matrix of the diabetes disease course time decay factor and the clinical characteristics is introduced, the dynamic weight vector is constructed and applied to clustering distance calculation, so that the model can adapt to dynamic changes of the clinical characteristics in different disease course stages, the adaptability to multi-center heterogeneous data is improved, and the stability of a clustering result is enhanced.

Systems and methods for predicting a medical trait in an individual are provided. The techniques include obtaining genomic data including information indicative of genetic variants present in a genome of the individual and calculating, using an at least one processor and a trained computational model, a trait score based on the information indicative of the genetic variants, wherein the trained computational model was obtained by training a first computational model using data describing phenotypes, genotypes, and / or phenotype-genotype relationships for a population including admixed individuals of multiple ancestries. The trait score may be used to alter a course of medical treatment of the individual and / or to implement a clinical trial.

The invention relates to a block chain-based clinical trial subject management system and method, and relates to the technical field of clinical trials, and the system comprises a management system constructed based on a block chain network and a plurality of edge computing nodes deployed in a medical institution. The edge computing node processes and encrypts the case information of the subject and then transmits the case information to the block chain network for storage, also can generate a feature query condition to screen a target subject, obtains index data of the target subject to determine test information, follow-up questionnaires and nursing information, and predicts follow-up time to generate a follow-up plan; the block chain network can start the smart contract template to screen the target subject based on the feature query condition. According to the method, the technical effects that the data security of the subjects is guaranteed by utilizing the block chain technology, and the clinical test subjects are efficiently screened, precisely supervised and scientifically managed in a follow-up visit manner are achieved.

An automated clinical trial matching system is disclosed, which may be used by a care provider to generate a shortlist of clinical trials that a patient may participate in, or by a trial coordinator to generate a list of patients eligible for a given clinical trial. The clinical trial matching system may rely on large language models (LLMs), clinical ontologies, reference databases, clinical guidelines, etc., to generate patient data models and clinical trial data models including structured data that may be directly compared using a matching model. Clinical trials that match a patient and / or patients that match a clinical trial may then be ranked, with top ranking results being shortlisted for display to the care provider or trial coordinator. The clinical trial matching system may display the top ranking results in a graphical user interface that indicates elements of a clinical trial and patient data that match.

Aspects of the subject matter described in this specification are embodied in systems and methods that utilize machine-learning techniques to evaluate clinical trial data using one or more learning models trained to identify anomalies representing adverse events associated with a clinical trial investigation. In some implementations, investigation data collected at a clinical trial site is obtained. A set of models corresponding to the clinical trial site is selected. Each model included in the set of models is trained to identify, based on historical investigation data collected at the clinical trial site, a distinct set of one or more indicators that indicate a compliance risk associated with the investigation data. A score for the clinical trial site is determined based on the investigation data relative to the historical investigation data. The score represents a likelihood that the investigation data is associated with at least one indicator representing the compliance risk.

Methods, systems, and apparatus, including computer-readable media encoded with computer program instructions, for a decentralized application ecosystem and data sharing platform. In some implementations, a system stores data for different individuals in different logical data storage areas. The system stores data indicating a set of predetermined data classifications, and for at least some of the data storage areas, the system determines and stores data classifications for data stored in an encrypted form in the data storage area. The system provides an application programming interface (API) that enables multiple different applications to access the data storage areas over a communication network. The system is configured to (i) provide access through the API to the data of data storage areas, conditioned on applications providing authorization tokens, and (ii) provide access through the API to the data classifications in the metadata that is not conditioned on providing authorization tokens.

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 disclosure relates to methods and kits for detecting tau, e.g., tau that is phosphorylated at amino acid position T181 (pTau181), tau that is phosphorylated at amino acid position T217 (pTau217), and / or total tau. The disclosure further provides methods for distinguishing between individuals whose cognitive condition will remain stable and whose cognitive condition will decline during their lifetime. The disclosure also provides methods for determining the eligibility of individuals for participation in clinical trials for Alzheimer's disease treatments. Also provided are methods for distinguishing between individuals with Alzheimer's disease and non-Alzheimer's dementia, and for monitoring response to treatment for Alzheimer's disease.

The invention discloses a clinical test resource scheduling collaborative management system based on risk assessment, and relates to the technical field of clinical test management, the clinical test resource scheduling collaborative management system comprises a clinical test collaborative management platform, and the clinical test collaborative management platform is in communication connection with the following modules: a data integration module, a resource scheduling module and a resource scheduling module. The data collection module is used for collecting clinical test related data from different systems, preprocessing the collected data and integrating the data into a multi-source data set in a unified format. According to the method, the physical world is mapped to the virtual space in real time through the digital twinning technology, and the risk probability is dynamically updated in combination with the association rule of the knowledge graph, so that the system can accurately evaluate the possibility of occurrence of the current risk, a timely and accurate decision basis is provided for risk management of a clinical test, and the risk management efficiency is improved. The problems of hysteresis and subjectivity caused by the fact that a traditional risk assessment method depends on historical data or expert experience are effectively avoided, and therefore the constantly changing risk situation in a clinical test can be better coped with.

The invention belongs to the technical field of clinical test management and artificial intelligence application, and discloses a clinical test subject intelligent recruitment and safety monitoring system and method based on artificial intelligence. Aiming at the problems of low clinical test subject recruitment matching degree, dynamic data quality control lagging and bad event early warning response slowing in the prior art, four invention points of a subject intelligent matching algorithm, a dynamic data quality control model, a bad event real-time early warning mechanism and a global collaborative management framework are provided. The method comprises two innovative algorithm formulas of subject matching degree calculation and data anomaly risk assessment. By constructing a recruitment-quality control-early warning global collaborative closed loop, the testee recruitment matching degree is remarkably improved, the dynamic data quality control timeliness is greatly enhanced, the adverse event early warning response speed is obviously improved, the method is suitable for various medicine and medical instrument clinical test scenes, and the efficiency and safety of clinical tests are comprehensively improved.

The present application discloses a research HIS system for clinical trials, which relates to the technical field of clinical trials, and includes a research participant management end, including a participant registration module, a pre-screening module, a research participant tracking module, a data collection module, an access rights management module, a research participant communication module, and a participant security and privacy protection module; a planning and scheduling management end, including a test plan module, a research center management module, a personnel task allocation module, a test schedule management module, a task tracking progress monitoring module, a resource scheduling management module, and a visit arrangement reminder module; a collaborative communication end, including a project team collaboration module, an email message notification module, a real-time chat discussion module, a file sharing collaborative editing module, a meeting schedule module, a discussion board knowledge base module, and a task allocation tracking module. The present application has the effect of facilitating the reasonable management and monitoring of clinical data in a hospital information management system.

Methods and systems including computer programs encoded on computer storage media, for a method for detecting signals related to subjects participating in a clinical trial. In some implementations, a computer collects clinical data from multiple sources. The computer standardizes and redacts personally identifiable information and determines predictive features that correspond to characteristics of the data that correlate with efficacy and safety signals. The computer obtains training data and trains machine learning models to predict one or both of a predicted efficacy signal and a predicted safety signal for a subject. The computer receives clinical data for a particular clinical trial subject enrolled in an ongoing clinical trial and predicts one or more signals and receives a review of the signals through a user interface. The computer updates the predictive components of the system based on the review.

The invention relates to the technical field of medical information, in particular to a clinical test patient screening and dynamic follow-up visit management method based on artificial intelligence, and the method comprises the following steps: collecting and standardizing admission, examination, medication and image data, mapping test items to form associated health data, comparing the group entering standard, screening qualified patients, and performing dynamic follow-up visit management. And extracting distribution mark difference crowds of medical units and patients, analyzing a physiological data fluctuation trend, and adjusting a follow-up visit sequence to generate a dynamic management plan. According to the method, by collecting structured health information and mapping test items, accurate screening and synchronous rhythm control are achieved, distribution of patients and medical units is analyzed, differential groups are recognized, resource allocation is optimized, a time sequence is constructed through physiological data, the difference value is calculated, and the health fluctuation trend is captured. The follow-up visit node is combined to adjust time arrangement, the execution efficiency is improved, and the patient management response speed and the data processing integrity are enhanced through multi-link cooperation.

The invention belongs to the technical field of drug research and development, and provides a drug research and development knowledge base construction method and system, and the method comprises the following steps: recognizing and collecting multi-source data, and carrying out synonym mapping and entity disambiguation; performing entity labeling on the original text by using a BERT model, inputting an entity labeling result into an RE model, and extracting a relationship between entities to obtain integrated knowledge data; filling the integrated knowledge data into a knowledge graph to form a drug research and development knowledge graph; the similarity between entities is calculated through an embedded model, entity alignment is carried out, a reasoning result is compared with clinical test data, contradiction is corrected through a voting mechanism, and a verified atlas is obtained; a potential entity relationship is predicted through an SWRL rule and GNN reasoning, and the drug research and development knowledge graph is dynamically updated and expanded; according to the method, more reliable and comprehensive knowledge support is provided for medicine research and development through an efficient, accurate and dynamically-updated medicine research and development knowledge base construction mode.

A system (100) for automated data review and discrepancy management in Medidata Rave using advanced visualization, comprising: a) a data integration module configured to extract clinical trial data and discrepancy logs from Medidata Rave in real time; b) a discrepancy detection module capable of automatically identifying data inconsistencies, missing entries, or log deviations based on predefined rules and algorithms; c) an advanced visualization module configured to display the identified discrepancies through interactive dashboards with heat maps, trend charts, and status indicators; d) a workflow management module configured to assign, track, and escalate discrepancies to specific users based on priority and role;e) a reporting and analytics module configured to generate comprehensive reports summarizing discrepancy trends, resolution status, and key performance indicators; and f) a user access and security module that provides role-based access control and ensures data confidentiality in accordance with regulatory standards.

The application relates to the technical field of computers and discloses an integrated information system for clinical trial archives and a data processing method thereof, the method comprising the following steps: constructing a standardized data model covering the whole process; dynamically structuring and inputting multi-source data and checking the integrity; performing multi-dimensional semantic correlation analysis based on a Bayesian network and a correlation coefficient; performing block chain version tracing and difference comparison; performing four-dimensional access control of roles-sensitivity-operations-context; and performing task-driven collaborative sharing and audit logging. The system comprises six modules, namely, unified modeling, data input, semantic analysis, version tracing, access control and collaborative audit. The application solves the problems of data islands, semantic fragmentation, poor dynamic adaptability and weak security protection in the prior art, realizes intelligent management, semantic interconnection, safe controllability and efficient collaboration of the whole life cycle of clinical trial archives, and significantly improves data quality, audit compliance and multi-role collaboration efficiency.

A software system simplifies and expedites the generation of patient-facing documents and other essential documents used in clinical trials. Through the integration of AI models, a biochemistry-oriented knowledge base, and Language Model Learning (LLM) mechanisms, the software system is capable of quickly producing documents that balance technical correctness with readability, thus substantially enhancing the productivity of clinical trial initiation and management processes. The software system operates through a sophisticated pipeline that ingests clinical trial protocol documents as inputs and processes them to generate comprehensive patient-facing documents along with other vital documents needed for clinical trials. This procedure, scalable and adaptive, consists of several nuanced stages.

A site distribution model identifies a set of sites to satisfy operational requirements of a clinical trial. An objective function is defined as: a first element indicating whether a site is included in the clinical trial; and a second element indicating whether a country is included in the clinical trial. There is a set of constraints including that an estimated total enrollment reaches a defined target enrollment. Computer code is generated to implement a site distribution model, using an optimization modeling language, based on the objective function and the primary set of constraints. The site distribution model is solved, if possible, to produce values of the site decision variable and the country decision variable. Otherwise, it is indicated to a user that a solution is not possible. If solving the site distribution model is possible, a list of clinical trial sites is produced from the site decision variable.

Disclosed herein are methods, non-transitory computer readable media, systems, and kits for performing a multiple tiered analysis for identifying individuals with a health condition for monitoring, treating, and / or enrolling the individuals in a clinical trial. Specifically, the multiple tiered analysis involves a first screen, which eliminates a large proportion of individuals who are identified as not at risk for a health condition, and a subsequent second analysis which detects presence of a health condition in the remaining individuals. The second analysis includes an intra-individual analysis, which involves combining sequence information from target nucleic acids and reference nucleic acids obtained from the individual. The target nucleic acids include signatures that may be informative for determining presence or absence of the health condition and the reference nucleic acids include baseline biological signatures of the individual. Altogether, the multiple tiered analysis achieves improved performance and accurate identification of individuals with the health condition.

A software system simplifies and expedites the generation of patient-facing documents and other essential documents used in clinical trials. Through the integration of AI models, a biochemistry-oriented knowledge base, and Language Model Learning (LLM) mechanisms, the software system is capable of quickly producing documents that balance technical correctness with readability, thus substantially enhancing the productivity of clinical trial initiation and management processes. The software system operates through a sophisticated pipeline that ingests clinical trial protocol documents as inputs and processes them to generate comprehensive patient-facing documents along with other vital documents needed for clinical trials. This procedure, scalable and adaptive, consists of several nuanced stages.

According to one aspect of the present invention, there is provided a method for recording clinical trial data, the method comprising the steps of: acquiring clinical trial data included in an area specified by a user; generating structured data using the clinical trial data; and recording key-value data included in the structured data as data on an item corresponding to the key.

The invention relates to the field of quality management optimization, and discloses a vaccine clinical test quality management system optimization method and system, and the method comprises the steps: obtaining vaccine clinical test data of a target test group, building an interaction information network according to the extracted case sharing test information, carrying out the node graph convolution propagation of the interaction information network, and obtaining a target test group; a structural information embedding vector is obtained, a structural outlier center of vaccine clinical test data is identified, abnormal type hierarchical analysis is performed on the structural outlier center to obtain a quality deviation classification result, and a local structural abnormal cluster is identified according to an acquired abnormal propagation path. And generating a local quality control strategy according to the quality deviation classification result and the local structural anomaly cluster, and optimizing the vaccine clinical test quality management system. According to the method, the structural outlier center in the vaccine clinical test data can be effectively captured and the test data interaction diagram structure can be constructed in the vaccine clinical test data lacking a natural interaction network.

According to the intelligent real-time blood detection system based on the intravascular indwelling sensors, through an intravascular indwelling multi-sensor integrated system (electrochemical / optical coaxial layered design), continuous monitoring of one-time puncture for more than or equal to 7 days is achieved, in a clinical test, puncture within 7 days can be reduced from 28 times to 3 times, an improved Kalman filtering algorithm eliminates signal crosstalk, and the detection accuracy is improved. The D-S evidence theory is combined to dynamically fuse multi-modal data, so that the blood glucose detection MARD is reduced to 4.1%, the blood potassium error is less than 0.08 mM, and the motion artifact residual error is 1t; and 0.5%. A real-time data transmission and intelligent alarm system significantly improves clinical response efficiency, self-adaptively calibrates individual differences, and realizes high-precision long-acting monitoring.

Automated systems and methods are presented for retrospectively analyzing clinical trial data. A plurality of image derived from biological samples of patients in a cohort population are accessed. Image features are computed based on the plurality of images. A diagnostic feature metric is derived based on the computed image features. A cut point value is determined by applying a statistical minimization method using the derived diagnostic feature metric and patient outcome data from the cohort population, in which the cut point value identifies a patient in the cohort population as positive or negative for a diagnostic test.

A method of pre-screening a human subject for a clinical trial for treatment of tauopathy or an amyloidogenic disease. The method comprises obtaining a plasma sample from the subject and determining a concentration of p217+tau present in the plasma sample. The method further comprises indicating the subject for further screening for the clinical trial when the concentration of p217+tau present in the plasma sample is greater than or equal to a minimum threshold and less than or equal to a maximum threshold. The minimum threshold corresponds to an amount of p217+tau in plasma over which subjects present with mild cognitive impairment (MCI) and an increased accumulation of tau tangles in the brain as compared to a cognitively normal patient. The maximum threshold corresponds to an amount of p217+tau present in plasma over which subjects present with pathology of widespread accumulation of tau tangles in multiple regions of the brain.

The present disclosure relates to a microbiome composition for improving muscle strength using a heat-treated fermented culture complex of KM2 Lactiplantibacillus plantarum, wherein the composition according to the present disclosure increased synthesis of myoproteins and showed an effect of inhibiting degradation of myoproteins and animal tests showed an effect of improving muscle strength and increasing leg muscle mass through the increase in grip strength, travel distance, and speed, as well as results in which hamstring muscle strength is improved and muscle mass increases even in a clinical trial. In addition, it was found that the beneficial bacteria increased through an increase in the diversity of intestinal microflora that changed with muscle reduction and improvement in the microflora composition, while the harmful bacteria decreased.

A system for aiding in a medical clinical trial involving one or more pediatric patients includes a patient passport, individual achievement stickers that are awarded to pediatric patients and that are placed within the patient passport, and an interactive companion toy having distinctive patterns thereon. A camera of a mobile device scans the individual achievement stickers in the patient passport and / or the distinctive patterns on the interactive companion toy, and an interactive incentivization application logic section of the mobile device unlocks and display reward content to the pediatric patient. The interactive companion toy, the mobile application, and the physical passport can be customized to match particular protocols of the clinical trial. The pediatric patient is thus incentivized to participate and continue the clinical trial. Virtual medical play associated with the clinical trial can be practiced on the interactive companion toy in an augmented reality environment. Parents are assisted through automatic text message reminders, and electronic patient-reported outcomes are collected, transmitted, processed, and summarized for visualization and insights via a cloud-based clinical trial portal.