344 results about "Clinical tests" patented technology

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 provides a recruitment patient determination method and device for a clinical test, a computer program product and electronic equipment, and relates to the technical field of medical big data, and the method comprises the steps: obtaining medical data of a plurality of patients, and converting the medical data into structured data; performing natural language analysis on the group entering standard information of the clinical test according to a large language model to generate a structured screening condition; performing data filtering on the structured data based on the structured screening condition through intelligent agents corresponding to a plurality of accommodating and arranging conditions of a clinical test to determine a recall result, matching the recall result based on the plurality of accommodating and arranging conditions, and determining an accommodating and arranging score of the medical data of each patient according to a plurality of matching results; and determining a target recruitment patient corresponding to the clinical test based on the recruitment score. According to the invention, the patient recruitment efficiency and accuracy can be improved.

The invention provides a thyroid cancer auxiliary diagnosis and metastasis risk prediction method based on deep learning, and relates to the technical field of artificial intelligence auxiliary medical treatment, and the method comprises the steps: extracting ultrasonic image multi-scale features through a self-adaptive neural architecture search network, combining clinical examination data, fusing diagnosis and treatment knowledge through a neural symbol inference device, and carrying out the prediction of the metastasis risk. Generating a knowledge enhancement feature map; constructing a feature propagation field by using a dynamic neural field network, solving a dynamic evolution equation, and generating a spatial-temporal feature field representing the dynamic change of focus features; constructing a tumor diffusion kinetic model by using an implicit neural representation network and a nerve ordinary differential equation network, calculating a transition probability based on an optimal transmission algorithm, solving an optimal control equation, and outputting a metastasis risk prediction result of each organ; the thyroid cancer diagnosis accuracy and metastasis risk prediction reliability can be effectively improved, and doctors can be assisted in accurate diagnosis and treatment.

The invention relates to the technical field of hepatitis diagnosis and treatment management, and discloses a block chain-based hepatitis patient full-period management method. The method comprises the following steps: creating a patient identity chain in a block chain network, and storing patient registration data and initial diagnosis information; full-cycle diagnosis and treatment data of a patient are obtained and divided into clinical examination, iconography, medication records and life sign monitoring data. Performing multi-dimensional cleaning on the clinical examination data to generate a standardized data set; inputting the iconography data into a pre-trained hepatitis feature extraction model, and outputting a liver injury feature set; verifying the integrity of the medication record, and generating a non-tampering medication time sequence chain; comparing the life sign monitoring data with preset health reference parameters to generate a sign deviation coefficient set; according to the method, data safety and traceability are guaranteed through the block chain, diagnosis and treatment data quality is improved through multi-dimensional data processing, patient privacy is protected, and reliable support is provided for full-period management of hepatitis patients.

The invention discloses a multi-role configuration and effective judgment method based on semantic arbitration, and belongs to the technical field of artificial intelligence, and the method comprises the steps: setting a multi-organization role and an arbitration role; receiving and vectorizing a test description text, distributing the test description text as a first clinical test context vector to multiple organizations, randomly assigning a first speaking party and setting an empty discussion pool; the first speaking party generates speaking content based on the clinical test context vector, vectorizes the speaking content and writes the speaking content into a discussion pool, calculates the similarity between a speaking content vector and the clinical test context vector, and calculates an arbitration score in combination with multi-organization attributes; the arbitration role executes three-state judgment according to the arbitration score and the parameters, an arbitration decision is generated, and an effective speaking content vector is determined; fusing, updating and writing the effective vector and the clinical test context vector into a discussion pool; discussion is terminated according to preset conditions, and based on the final vector and the discussion pool, effective judgment is output and a log is recorded; according to the method, a multi-organization discussion scene can be effectively simulated, and the clinical test discussion efficiency and accuracy are improved.

The invention belongs to the technical field of radial artery compression hemostats, and particularly relates to a radial artery hemostasis method based on real-time pulse analysis and a hemostat control system. A slow and continuously changing force is applied to a radial artery puncture opening through the pressure applying module, meanwhile, the pulse detection module transmits a pulse forced feedback signal of the puncture opening to the main control module in real time, and the main control module analyzes and processes the received feedback signal in real time and judges the current hemostatic state of the radial artery. And on this basis, the pressure applying module is controlled to adjust the pressure. The method is characterized in that the relation between pulse forced feedback signals and the forced state of the radial artery is obtained through clinical tests according to the difference of the pulse feedback signals of the radial artery in different pressed states, and therefore the pressure is stabilized in the state that the blood flow of the radial artery is stable and a puncture opening does not bleeding. According to the scheme, the individual difference between different patients can be overcome, the compression hemostasis strength is adjusted in a self-adaptive mode, and the occurrence rate of related complications such as radial artery occlusion is effectively reduced.

The invention discloses a drug clinical trial contract management system, which comprises a contract text structured knowledge base for storing contract structures, field terms and hospital basic data and supporting standardized construction. The contract processing engine module is used for receiving test parameters and driving contract intelligent matching, budget automation and text dynamic splicing; the intelligent auditing module is used for performing automatic comparison, rule checking, difference detection, compliance examination and clause conflict early warning, so that manual errors and workload are remarkably reduced; the electronic signing module is used for providing multi-mode identity verification and block chain evidence storage, and ensuring the signing effectiveness and non-tampering property; a database is recorded and tracked, full-life-cycle management of contracts is achieved, state changes and version history are recorded, and tracing and auditing are facilitated. According to the invention, all the modules cooperate, the contract generation efficiency and accuracy are significantly improved, the customization and compliance are enhanced, budget transparency is realized, safe and efficient signing is provided, and the operation cost and legal risks are effectively reduced.

The present invention provides a method and system for automatically and seamlessly processing clinical trial source data into electronic data capture (EDC) systems. In one embodiment, a file structure is defined for an electronic binder system (eBinder); source data is uploaded to the eBinder; the source data is encrypted, Patient Identifiable Information in the source data is masked; the source data is converted into machine readable plain text in the JavaScript Object Notation (JSON) format using Natural Language Processing (NPL) technologies; the JSON data is converted into tabulated machine readable data in the HyperText Markup Language (HTML) format using NPL technologies; the HTML data is converted into machine understandable data using NPL technologies; the machine understandable data is populated into EDC datasets using NPL technologies; the source data and converted data are displayed side-by-side for source data verification; and a platform is provided for regulatory data verification or auditing.

The invention provides a compound nystatin ointment and a preparation method thereof, and relates to the technical field of medicine preparation, the compound nystatin ointment comprises active ingredients of nystatin, neomycin sulfate, triamcinolone acetonide and permethrin, and the formula comprises liquid paraffin, white vaseline, wool fat, hexadecanol, PEG-30 dipolyhydroxystearate and glyceryl monoisostearate. According to the compound nystatin ointment preparation prescription process disclosed by the invention, the phenomenon of ointment matrix layering in the use process of commercially available products is mainly solved. According to a pharmaceutical research test, the stability of the compound nystatin ointment is superior to that of a commercially available original development agent, the ointment body character and the stability of active ingredients can be kept under the conditions of high temperature, high humidity, low temperature, freeze thawing and illumination, the maximum single impurity content and the total impurity content in the product are not obviously increased, and the quality is controllable. And meanwhile, an in-vitro transdermal test (IVPT) accidentally finds that the retention amount of related active ingredients is higher than that of the original development agent, and the drug effect is obviously improved. Through clinical test research, the compound nystatin ointment disclosed by the invention can be used for effectively treating external otitis caused by bacteria sensitive to neomycin, fungi sensitive to nystatin and ear mites sensitive to permethrin of dogs and cats.

The invention discloses a sample size calculation method and system for quantitative consistency evaluation. The method comprises the following steps: firstly, determining MDL and a clinical consistency boundary value, then collecting pre-test data, and determining a required sample size through iterative calculation. Then, verification samples are extracted from the overall data for consistency analysis, and the robustness of a calculation result of the verification sample size is simulated through Bootstrap; according to the method, MDL and least square regression are fused for the first time, a sample size formula for quantitative consistency evaluation is constructed, the reliability and effectiveness of clinical test data are ensured by providing a new sample size calculation method, more accurate sample size estimation is provided for clinical research, and therefore the quality of clinical decisions is improved.

The invention relates to the technical field of medical data analysis, and discloses a clinical examination result analysis method and system based on a medical knowledge graph. The method comprises the following steps: discretizing continuous test data of a patient into data segments with change modes, mapping a time sequence mode of the data segments to corresponding entity nodes of a knowledge graph, and constructing knowledge graph segments carrying dynamic information; and performing heuristic extension by identifying key mode paths in the analysis paths to generate a candidate analysis path set. And performing synchronous iteration comparison on the core analysis path and real-time inspection data, dynamically correcting the confidence coefficient weight of each path, and finally outputting a structured report according to the optimized main analysis link. According to the method, fusion of inspection data time sequence features and the knowledge graph is realized, and the depth of result analysis and the adaptive capacity to illness state evolution are improved through dynamic interaction optimization of paths and data.

The invention discloses an intelligent processing method and system for clinical test data, and relates to the field of data processing, and the method comprises the steps: recognizing a high flow disturbance stage, and starting an uploading channel adjustment process; executing flow limiting operation in the high flow disturbance stage; obtaining a load pressure evaluation result; and completing data supplementation. According to the method, under the condition of data uploading peak or resource shortage in the clinical test process, the whole-process regulation and control of the disturbance data, such as current-limiting protection, back compensation and abnormal filing, is realized by effectively identifying the high-flow disturbance stage, dynamically adjusting the data uploading behavior and intelligently distinguishing the processing priorities of the key data and the non-key data. The method has the advantages of being high in adaptability, accurate in resource scheduling and safe and reliable in data, solves the problems of system congestion, uploading failure or important data lag in the data uploading stage, and improves the overall operation stability and the data integrity guarantee capability of a clinical test data processing system.

The invention provides a block chain-based clinical test data traceability and sharing platform. The block chain-based clinical test data traceability and sharing platform comprises the following modules: a life cycle state definition module, an on-chain event generation module, an identity verification module, a state transition logic unit and an event chain construction module. According to the invention, through cooperation of a life cycle state definition module, an on-chain event generation module, a state transition logic unit and an event chain construction module, key behaviors generated by a subject in the stages of screening, informed agreement, group entering, visit, adverse event processing, follow-up visit and the like are solidified into a chain event structure according to a time sequence; each event node comprises a behavior abstract, a preorder node hash and a timestamp, continuous association and accurate positioning of cross-stage operation are achieved, a clinical test is converted into a state transition chain capable of tracking the whole process from a fragment type data uplink, and the problem that a continuous behavior chain cannot be constructed in the prior art is solved.

The embodiment of the invention discloses a medical equipment control system based on a clinical test scheme. According to one specific embodiment, the system comprises a scheme generation server, a document abstract extraction server, a data analysis server and a medical robot, and the document abstract extraction server is used for generating a query argument tree and a retrieval information result set based on text information and document request information; generating an ideal answer vector set according to the retrieval information result set; querying the ideal answer vector set to generate a retrieval result group; processing the retrieval result group to obtain a literature abstract; the data analysis server is used for performing instruction conversion on the query information to obtain a query instruction and executing the query instruction to obtain an information query result; and the scheme generation server is used for generating a clinical test scheme and sending the clinical test scheme to the medical robot to execute clinical test operation. According to the embodiment, the system response time and the waste of test samples can be reduced.

The invention provides a data processing method and device based on clinical test data, in the application, a prior probability and a historical likelihood of a symptom corresponding to target clinical test data can be determined through a historical clinical database, the prior probability reflects a basic epidemic rate of an indication in a population, and the historical likelihood of the symptom corresponding to the target clinical test data can be determined through the historical clinical database. According to the historical likelihood, association rules between symptoms and indications are mined from historical cases, the association rules and the indications are dynamically updated through a Bayesian formula, an inference chain conforming to clinical logic is formed, then, a complex multi-feature joint probability estimation problem is converted into product calculation of single-feature statistics through conditional independent assumption, and a probability estimation result is obtained. The problem of calculation feasibility under high-dimensional data is solved, probabilistic output provides a quantitative basis for auxiliary determination of indications, a data-driven statistical rule is converted into a clinically understandable auxiliary support tool, and objectivity, consistency and scientificity of diagnosis decisions are effectively improved.

According to the M proteinemia diagnosis and prediction method and device based on multi-modal data fusion, the problems that effective adaptation cannot be achieved, the feature difference of heterogeneous multi-modal data is large, and no-label data is difficult to apply can be solved, and the data utilization rate and the accuracy of M proteinemia diagnosis and illness state prediction are improved. The method comprises the following steps: (1) acquiring M proteinemia patient data, and combining other label-free IFE images to form a sample set; (2) performing data preprocessing on the sample set; (3) constructing an auxiliary task to pre-train an IFE image feature extractor by using the preprocessed labeled and unlabeled IFE image samples; meanwhile, feature engineering is designed for the clinical examination index values preprocessed in the step (2) according to the correlation between the examination indexes; (4) constructing a negative and positive 2 classifier; (5) constructing a positive feature classifier; and (6) constructing a patient condition predictor.

A computer-implemented method of predicting, simulating, or forecasting values of one or more specified subject-related attributes during a clinical trial comprises: receiving input data comprising: a medical history of a subject, the medical history comprising values of a plurality of subject-related attributes of a subject; and data specifying a requested output, the data comprising: the one or more specified subject-related attributes of the subject and a time frame; and applying a trained generative machine-learning model to the received input data, the trained generative machine-learning model configured to generate output data based on the input data, the output data comprising: respective values of the one or more specified subject-related attributes of the subject in the specified time frame.

The invention discloses a clinical subject data matching and recommending method based on a database and a knowledge graph. The method comprises the steps of 1, collecting structured clinical data of clinical subjects; 2, preprocessing is carried out, and a standardized clinical data set is generated; 3, constructing a medical knowledge graph; 4, generating a subject feature vector and a test condition feature vector through an improved GraphGPS network; 5, executing hard constraint screening and similarity matching through an improved GraphRAG algorithm, and generating a clinical test set; 6, constructing a multi-level evidence sub-graph, and executing semantic enhancement screening on the clinical test set to generate a recommended clinical test set; and 7, based on the recommended clinical test set, constructing a recommended explanation path and a recommended explanation tuple, and generating a recommended visual card to be pushed to a clinical end. According to the method, the accuracy of clinical test subject matching and the interpretability and response efficiency of recommendation results are improved.

The invention relates to the technical field of intelligent medical treatment and accurate risk management of obstetrics, in particular to a dynamic risk assessment system and method for venous thromboembolism of pregnant and lying-in women based on multi-modal data fusion. The system is composed of a multi-modal data acquisition module, a cloud data processing module and an early warning feedback module. The multi-modal data acquisition module is integrated with the micro-fluidic chip to rapidly detect blood coagulation markers, the genetic information analysis unit is used for screening gene mutation, and the intelligent pressure socks are used for monitoring hemodynamic parameters in real time; the cloud data processing module adopts a long-short-term memory network model, inputs dynamic feature sequences of continuous 30 days, and outputs VTE risk level probabilities in the future 7 days; the early warning feedback module pushes a grading intervention scheme; dynamic monitoring is implemented in early, middle and late pregnancy stages and postpartum stages, and dynamic risk updating is realized in combination with clinical rules and deep learning. Clinical tests show that the sensitivity, the specificity and the early warning timeliness of the system are remarkably improved, and the ICU transfer-in rate is reduced.

The invention provides a traditional Chinese medicine composition for treating nephropathy as well as a preparation method and application thereof, and the anti-renal fibrosis effect is remarkably improved by optimizing a leech extraction process and a novel preparation technology. Animal experiments show that urine protein of model animals can be reduced by 67% and serum creatinine can be reduced by 42% after 12 weeks of continuous administration. Clinical tests prove that the traditional Chinese medicine composition has a definite curative effect on IgA nephropathy, membranous nephropathy and the like, and a new choice is provided for treating chronic kidney diseases.

Disclosed are methods and systems for artificial intelligence-driven clinical trial protocol data retrieval and augmentation. A natural language user request is received via a user interface. The user request is combined with contextual data to produce a prompt for a large language model. The prompt is input to the model to produce a model response including a database query, in a database query language, and metadata. Clinical trial protocol data is retrieved from a first database based on the database query. Application programming interface (API) requests are generated based on the database query and / or the metadata. API calls are performed using the generated API requests to obtain clinical trial metrics. A response to the user request is generated based on the retrieved protocol data and the metrics.

Aspects and features relate to computationally reducing the size or complexity of a database in order to improve the speed and efficiency with which such a database is processed by a computing system in order to identify investigators for clinical trials. In some aspects, a processing device performs operations including identifying data sources for geographically clustered data containing corresponding descriptors for database records. The operations further include formatting the corresponding descriptors to produce standardized, corresponding descriptors, and matching each standardized, corresponding descriptor to produce a record score for the descriptor. The record scores can be combined to produce an overall score for each database record and the database record can be selected and written to the data store based on the overall score.

The invention relates to the technical field of test design, in particular to a key factor mining-based accommodating and arranging condition optimization method, medium and equipment, which are characterized in that accommodating and arranging conditions are generated by analyzing a target text, an initial simulation queue is constructed in combination with database retrieval, and baseline feature distribution is analyzed, so that representative research crowds can be obtained without real object recruitment; the test cost is reduced; a panoramic object feature matrix is constructed by integrating multi-dimensional data, the limitation of a single feature is broken through, and comprehensive data support is provided for key factor mining; the contribution degree of each characteristic variable to the preset key outcome is analyzed based on the matrix, and the key characteristic variables are positioned, so that the experience driving is changed into the data driving, and the scientificity and the pertinence of the accommodating and arranging condition design are improved; the accommodating and arranging conditions are dynamically adjusted through the closed-loop optimization process, it is ensured that the target accommodating and arranging condition set gives consideration to queue representativeness, feature pertinence and distribution rationality, and the design scientificity and success rate of clinical tests are improved.

A system is provided.SOLUTION: A system comprising: means for receiving clinical test data and medical record information; means for obtaining drug information from a drug database; means for classifying data using a specific algorithm and extracting features; means for recommending optimal drugs based on the features; and means for generating and transmitting a recommended drug list.SELECTED DRAWING: Figure 1

Aspects relate to a method, a data processing system, a laboratory instrument, a computer program, and a computer readable medium. The method includes obtaining, by a computing device, a relationship result of a first clinical test. A first clinical test is performed on a biological sample by a first laboratory instrument, the biological sample being in a sample container. The relationship result includes first information and second information, the first information specifying that a laboratory result of the first clinical test is outside a reliability range of the first clinical test performed by the first laboratory instrument, and the second information specifying one or more of an upper bound and a lower bound of the reliability range. The method further includes evaluating, by the computing device, whether a condition of the laboratory rule is determinable by using the first information and the second information. The laboratory rule specifies one or more actions to be taken depending on the result of the determination of the condition. At least one of the one or more actions is to be taken on one or more of the biological sample, the sample container, and the relationship result, and / or the one or more actions include displaying information about one or more of the biological sample, the sample container, the relationship result, and the patient. If the condition may be determined and one or more actions are to be taken according to a result of the determination of the condition, the method includes causing, by the computing device, the one or more actions to be taken.

The invention aims to provide an electrocardiogram dynamic monitoring and grading early warning system for clinical trials, which comprises a wearable multi-parameter sensing terminal for collecting multi-modal physiological signals of a user; the edge calculation unit is used for receiving a real-time data stream from the wearable multi-parameter sensing terminal and executing local signal synchronization, filtering preprocessing, preliminary anomaly detection and emergency level local alarm; the cloud analysis platform is used for storing the multi-modal data and executing depth feature extraction, abnormal mode recognition, cross-parameter fusion modeling and individualized risk grading evaluation, and the cloud analysis platform is in two-way communication with the edge calculation unit and is used for synchronizing model update feedback and an alarm result; meanwhile, the method has the inter-lead topology perception capability, the unsupervised semantic alignment capability, the multi-modal fusion reasoning capability, the individualized risk discrimination capability and the edge-cloud collaborative closed-loop capability.

The utility model discloses a clinical examination smear device, which relates to the technical field of clinical examination smear, and comprises a mounting table, a buffer clamping component is mounted at the top of the mounting table, the buffer clamping component is used for protecting a smeared slide, the buffer clamping component comprises a clamping seat, a mounting groove, a buffer spring, a clamping plate and a non-slip mat, and the clamping seat is fixedly connected with the mounting groove. The clamping seat is mounted at the top of the mounting table, the mounting grooves are symmetrically formed in the inner wall of the bottom of the clamping seat, the buffer springs are mounted in the mounting grooves, the clamping plate is mounted at the top ends of the buffer springs, and the non-slip mat is mounted at the top of the clamping plate. When the coating on the slide is scraped to be uniform, and after the slide is pressed by the scraping plate, the slide moves downwards, so that the buffer spring is compressed downwards, a space is provided for buffering of the slide, and the slide is prevented from being damaged due to the pressure.

The invention discloses a compound traditional Chinese medicine formula for treating viral infection and a production method of the compound traditional Chinese medicine formula. 5-10 parts of radix isatidis; 5-15 parts of coix seeds; 3-6 parts of liquorice; 5-15 parts of poria cocos; 1-3 parts of mung beans; 5-10 parts of tremella fuciformis; 3-10 parts of quercetin; and 5-15 parts of carambola. The invention also discloses a preparation method of the compound traditional Chinese medicine oral liquid, capsules, granules and tablets. The medicines are combined, and clinical tests show that the medicine has the effects of clearing away heat and toxic materials, tonifying spleen and promoting diuresis, protecting liver and kidney, killing viruses and the like. The traditional Chinese medicine is suitable for cold caused by various viruses and has the effects of relieving cough, abating fever, diminishing inflammation and the like.

A method for determining whether a patient may be enrolled into a clinical trial includes the steps of examining the patient's medical record from an electronic health record system, deriving a plurality of first concepts from the medical record, normalizing each concept in the plurality of first concepts to produce, for each normalization, a normalized concept, comparing each normalized concept to a list of study criteria, to indicate if the normalized concept meets the criteria, and if each criteria is met, indicating that the patient is not ineligible for enrollment in the clinical trial