809 results about "Clinical tests" patented technology

The invention relates to the technical field of medical data management, in particular to a medical data document classification and marking system which comprises a document structure analysis module, a semantic information identification module, a classification and marking module and a document intelligent label module. According to the method, document visualization and retrieval efficiency is improved by analyzing document content and extracting titles, texts and appendixes, analytic performance and operability of data are enhanced by identifying key information and semantic relations and accurately extracting and labeling medical terms, medicine research data and medical record records, storage is optimized according to priorities in combination with classification and marking, and the method is high in practicability and easy to popularize and use. Clinical values and application scenes of documents are clarified, document management processes are refined, management compatible with clinical data formats is formed through structured storage of marked data and classification label generation, storage normalization and retrieval efficiency are improved, the requirement for accurate information calling in clinical tests is met, document management capacity is improved, and the document management efficiency is improved. And support is provided for improvement of clinical decision-making and research efficiency.

The invention provides a block chain-based clinical test data integrity system, and relates to the technical field of data processing, and the system comprises the following components: a multi-source heterogeneous data acquisition and preprocessing module, a multi-level encryption and under-chain data storage module, an alliance block chain network, and an alliance block chain network maintenance and verification shared distributed account book. The distributed account book only stores all digitally signed clinical test data fingerprints, metadata and operation logs, does not store original data, and is linked with the intelligent contract automatic management module, so that the clinical test data cannot be tampered in the whole life cycle from the source to archiving. Hash anchoring, digital signature and decentralized verification mechanisms of the block chain technically eradicate the possibility of unauthorized tampering of data, and ensure scientificity and reliability of clinical test results.

The invention relates to the technical field of data distributed storage, and discloses a distributed storage method and system for clinical test data, and the method comprises the steps: carrying out the ring signature encryption processing of the patient test data of a plurality of data centers, and obtaining a patient anonymization identifier and standardized clinical data; performing two-stage random storage node configuration to obtain an optimal storage node configuration scheme; constructing an LPPMM-DA data aggregation model based on the optimal storage node configuration scheme, and performing fragmentation processing on the patient test data to obtain a plurality of target data fragments; performing dynamic operation envelope analysis and alliance graph game calculation on the multiple target data fragments to obtain a storage resource scheduling scheme; according to the storage resource scheduling scheme and the anonymization identifier, distributed data block storage and access authentication are executed on the multiple target data fragments, data block storage distribution information is generated, a complete data access control system is constructed, and safe and reliable storage of clinical test data is ensured.

The invention discloses a compliance verification method and device for medical process data, and relates to the field of medical data, and the method comprises the steps: constructing a medical process knowledge graph, and defining the data dependence relation of clinical test, production and declaration stages; dynamically activating the rule subsets based on the current process stage, and calculating and loading association rules through dependency to form a composite rule set; converting the structured data, the medical image and the handwritten text into a unified vector space, calculating a cross-modal semantic consistency score, and resolving conflicts according to a preset priority or a cross-modal attention fusion mechanism; and when the flow stage is switched or the historical data is modified, full-link backtracking verification is triggered. The device comprises a process modeling module, a rule dynamic generation module, a multi-modal alignment module and a visual proof module. According to the invention, the compliance verification efficiency is greatly improved through the dynamic rule engine, and the supervision risk and operation cost caused by data defects of pharmaceutical enterprises are effectively reduced.

The invention provides a clinical test result prediction model driven by multi-modal data, which relates to the technical field of digital data construction and comprises a data acquisition and preprocessing module, a feature extraction and fusion module, a self-adaptive prediction model, a feedback and updating module and a visualization module. By fusing multi-modal information such as images, genomes, laboratory data and text records, the scheme can more comprehensively obtain multi-dimensional health data of the patient. The feature dynamic weight distribution module dynamically adjusts the weights of different modal data based on global and local attention mechanisms, and ensures the prominent contribution of key features in different prediction scenes, thereby significantly improving the prediction precision and stability of the model.

The invention relates to the technical field of auxiliary medical treatment, and discloses a clinical test matching method, device and equipment, a medium and a product. The medical record information is input into a preset general condition matching model for traversal, the general condition matching model comprises a first preset number of general judgment conditions, and judgment tags in one-to-one correspondence with the general judgment conditions are generated; according to the discrimination label, preliminarily screening test schemes conforming to the patient, and determining a preliminary test scheme; and inputting the medical record information and the preliminary test scheme into a preset specific condition matching model for secondary screening to obtain an ultimate test scheme after secondary screening. According to the method, the accuracy of test scheme matching can be effectively improved, errors are not prone to occurring, and the recruitment efficiency of clinical test subjects can be effectively improved.

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.

A computer-implemented method is provided for generating a trained TTS-GAN which may be used to generate synthetic longitudinal data for use in survival analysis, a clinical trial or clinical research. The TTS-GAN is configured to generate synthetic time-series data based on synthetic context data generated using a machine-learning model by virtue of being trained using training data comprising real context data and added noise data. A technique for executing survival analysis is also provided, which relies on the synthetic longitudinal data.

In some implementations, after a first monitoring program has begun, a system receives input from a user corresponding to a second monitoring program that is related to the first monitoring program. The system provides data indicating one or more candidate items that can be monitored in the second monitoring program. The system receives user input indicating a selection from among the candidate items that specifies a set of physiological and behavioral attributes to be monitored in the second monitoring program. The system generates software or configuration data configured to implement monitoring for the second monitoring program for a subset of the first group of devices for the first monitoring program. The system provides the generated software or configuration data to the subset of the first group of devices to change data collection performed by the devices in the subset of the first group of devices.

In some implementations, a system collects data from remote devices over a communication network, including first monitoring data for a first monitoring program and second monitoring data for a second monitoring program. The system evaluates the second monitoring data obtained for the second monitoring program. Based on the evaluation, the system determines that the monitoring data indicates that one or more criteria for adjusting the first monitoring program based on the second monitoring program are satisfied. The system selects one or more elements of the second monitoring program that are different from elements of the first monitoring program. The system alters the first monitoring program to include the selected one or more elements of the second monitoring program.

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 discloses a clinical test document quality detection and processing method and system and computer equipment. The method comprises the following steps: acquiring a to-be-processed clinical test document; performing format conversion and optimization processing on the to-be-processed clinical test document to obtain a preprocessing result; automatically classifying the preprocessing result to obtain a classification result; extracting document key information according to the classification result, and generating a file name to obtain an extraction result; checking the integrity, compliance and archivability of the corresponding document according to the extraction result, and performing automatic processing according to the detection result to obtain an automatic processing result; and storing and archiving the automatic processing result. By implementing the method provided by the invention, efficient and accurate document quality detection and automatic processing can be realized, so that the document processing quality and efficiency are improved, and the problems of low efficiency, poor consistency and high error rate in the prior art are solved.

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 discloses a clinical test period prediction system and method based on a virtual reality technology, and relates to the technical field of electric digital data processing. The clinical test period prediction system based on the virtual reality technology comprises a data acquisition module, a real-time evaluation module and an accuracy evaluation module. The collected test data is transmitted to the central processing unit to obtain the transmission data and the processing data, then real-time evaluation is carried out in combination with the interaction response time to obtain the data real-time evaluation value, and whether accuracy evaluation is carried out or not is judged based on the data real-time evaluation value and the preset real-time threshold value. And finally, according to the transmission data and the processing data, obtaining a data accurate evaluation value, and based on the data accurate evaluation value and a preset accurate threshold value, determining whether to carry out clinical test period prediction, thereby improving the accuracy and real-time performance of the data, further realizing improvement of the clinical test period prediction accuracy, and improving the clinical test period prediction accuracy. The problem of low accuracy of clinical test period prediction in the prior art is solved.

The invention provides an AI-based clinical research scheme optimization method and system, and relates to the technical field of clinical research, and the method comprises the steps: collecting current clinical test data, and extracting basic features and derivative features related to the recruitment efficiency and shedding rate of a patient from the current clinical test data; the extracted basic features and derivative features are input into a plurality of different pre-trained multivariable regression models at the same time, preliminary optimization suggestions are output through the multivariable regression models, and the multiple different multivariable regression models comprise a multiple linear regression base model, a Logistic regression base model and a random forest base model; inputting the preliminary optimization suggestions output by each multivariable regression model into a pre-trained meta-model, and generating a final optimization suggestion by integrating the multiple preliminary optimization suggestions by the meta-model; and based on the final optimization suggestion, executing the optimization of the clinical research scheme, and achieving the purposes of improving the recruitment efficiency of the patient and reducing the falling-off rate.

The invention relates to the technical field of bioinformatics, and discloses an adverse drug reaction prediction method based on comment text information enhancement, and the method comprises the following steps: constructing an adverse drug reaction dictionary; extracting adverse drug reaction information based on the comment text; integration of adverse drug reaction information; integrating the drug characteristic information of the public database; calculating the similarity; constructing a heterogeneous graph; building a graph convolutional network model; model training and evaluation; predicting adverse reaction cases of drugs; and analyzing the occurrence mechanism of adverse drug reaction. According to the adverse drug reaction prediction method based on comment text information enhancement, diversified data such as drug comments, clinical test data, actual medication experience and feedback are comprehensively utilized, patient features are added to enrich the relationship between drugs and adverse reactions, more comprehensive drug safety assessment is provided, and the prediction accuracy is improved. In addition, compared with other models, the GCN model has the advantages that the training and prediction are quicker, and the calculation difficulty can be reduced.

The invention discloses a tumor disease assessment method based on big data, and particularly relates to the technical field of tumor disease assessment. Medical data and living habit data of a patient are collected through multiple data interfaces, integration is carried out through preprocessing and a multi-modal data fusion technology, tumor microenvironment features and drug metabolism features are extracted, a comprehensive feature vector is generated, the effectiveness of a chemotherapy regimen is predicted by using a machine learning model, and when the effectiveness of the chemotherapy regimen is low, the chemotherapy regimen is identified. Newest clinical tests and drug data are introduced regularly, model parameters are dynamically optimized in combination with the chemotherapy regimen improvement effect, it is ensured that the model reflects the newest clinical progress in real time, the problems of model updating lag and drug innovation mismatching are effectively solved, the accuracy and effectiveness of a treatment regimen are improved, and the treatment cost is reduced. And meanwhile, side effects caused by blind attempts are reduced, and powerful support is provided for personalized precise medical treatment.

The invention provides a self-adaptive clinical trial design method based on machine learning, relates to the technical field of clinical trials, and adopts a method integrating reinforcement learning, causal inference and data enhancement technologies to predict an optimal treatment scheme and adjust a distribution proportion in real time through a dynamic optimization module, and limits high-risk treatment selection in combination with dynamic ethical constraints. The problems that a traditional clinical test preset rule lacks real-time performance and neglects patient safety are solved, a data collection and enhancement module generates virtual patient data conforming to a causal relationship, real and virtual data are combined to form a unified data set, a high-quality data set is constructed, the data size is effectively expanded, the statistical power of a small sample scene is improved, and the accuracy of a clinical test result is improved. The termination rule module of comprehensive statistics, ethics and resources shortens the test period through weighted evaluation, significantly reduces the risk of improper allocation of high-risk groups, enhances the benefit of patients, ethics compliance and test result reliability, and expands the application range in rare disease research and personalized medical treatment.

The invention relates to a clinical test item subject intelligent screening method and device, a medium and equipment, and relates to the technical field of clinical trials, and the method comprises the following steps: inputting each processed data into a preset subject screening model to obtain a target subject; screening out evaluation indexes easy to be abnormal from the curative effect evaluation indexes corresponding to the target clinical test item, and determining corresponding easy-to-occur dimension data when each evaluation index easy to be abnormal is abnormal; when easy-to-occur dimension data exist in the patient data of each target subject, determining the corresponding easy-to-occur dimension data as key dimension data, and verifying the corresponding target subject according to the key dimension data and the easy-to-anomaly evaluation index of the key dimension data existing in the corresponding easy-to-occur dimension data; and if the verification is passed, determining the corresponding target subject as a final subject of the target clinical test item. The method has the effect of improving the accuracy of the screening result.

On one hand, the invention provides a clinical test patient matching method based on a large model. The invention further discloses a clinical test patient matching system based on the large model. The clinical test patient matching system based on the large model is used for achieving the clinical test patient matching method based on the large model. According to the method, traditional patient containing and ranking are converted into case retrieval and problem-based filtering, a professional knowledge data set is constructed, a field large model is obtained based on large model fine tuning, and the cost can be greatly reduced and the accuracy can be improved by utilizing the understanding and processing capability of the large model for long texts. According to the method and the system, the answers of a plurality of containing and arranging standard questions can be more effectively integrated by fusing evaluation rules of various strategies, the medical semantic information is mined, the relationship between the patient and the clinical test is obtained, and the matching accuracy of the clinical test and the patient is effectively improved.

The invention relates to the technical field of data processing, in particular to an intelligent analysis method and system for clinical test data, and the method comprises the steps: obtaining a data sequence of each physiological index of a patient, and calculating the deviation degree through quantifying the difference between a target data point and a corresponding physiological index normal range, calculating an abnormal index based on the deviation degree and the distribution characteristics of the corresponding data in the data sequence of the corresponding physiological indexes, and obtaining a fitting curve of each physiological index by fitting the abnormal index, so as to screen out time periods in which all the fitting curves are in a rising trend; and taking the physiological index pairs of which the correlation is greater than a preset value in the time period meeting the screening condition as target index pairs, constructing a causal graph by using a PC algorithm based on the target index pairs, and identifying the causal relationship of each physiological index. According to the method, the complexity of constructing the causal graph by the PC algorithm can be reduced while the important causal relationship among the physiological indexes is reserved.

The invention discloses a clinical test quality evaluation method and system based on artificial intelligence, and relates to the technical field of clinical test quality evaluation. Through a series of automatic data processing and model evaluation processes, automatic operation is basically realized from data acquisition to final report generation, manual complex evaluation calculation on a large amount of data is not needed, and the evaluation efficiency is greatly improved; evaluation is carried out through an artificial intelligence algorithm and a rule-based expert system, so that the influence of subjective factors on an evaluation result is effectively reduced, and the consistency and accuracy of the evaluation result are improved; according to the method, multiple types of high-dimensional data are processed through an artificial intelligence algorithm, key feature subsets are extracted through feature extraction and screening, a trained deep learning model is used for evaluation, the method adapts to constantly changing large-scale and high-dimensional data conditions by constantly optimizing model performance, and the requirement for efficient and accurate evaluation is effectively met.

The invention belongs to the field of drug design, and particularly discloses a virtual screening method of a small-molecule inhibitor targeting YTHDF1 protein, which comprises the following steps: acquiring a YTHDF1 protein structure, and carrying out homologous modeling to generate various conformation proteins; small molecules combined with the YTHDF1 protein are obtained, bait molecules are generated, and ligand molecules are formed; a ligand molecule is in butt joint with a binding site on the YTHDF1, and the compound is characterized through PLEC fingerprints; the method comprises the following steps: constructing a specificity scoring function by using a machine learning algorithm, carrying out scoring screening on a compound to be screened and a compound butted with YTHDF1 molecules, carrying out affinity prediction, and screening out the small-molecule inhibitor targeting YTHDF1. According to the virtual screening method provided by the invention, the YTHDF1 targeting molecules with high affinity can be accurately screened on a large scale, candidate molecules are provided for research and development of medicines for treating YTHDF1 related diseases, and a foundation is laid for clinical tests and medicine optimization.

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 quality control method of a traditional Chinese medicine preparation based on an IV-catfish orifice-freeing prescription, and relates to the technical field of analysis of traditional Chinese medicine preparations, and the quality control method comprises the following steps: carrying out microscopic identification on traditional Chinese medicine components in the IV-catfish orifice-freeing prescription, and determining a pharmacodynamic material basis. According to the quality control method for the traditional Chinese medicine preparation based on the IVV orifice-freeing prescription, various evaluation means such as a drug effect equivalent evaluation index, a stability test, a quality test and a clinical test are comprehensively applied, the consistency and reliability of the curative effect of the traditional Chinese medicine preparation can be remarkably improved, and the drug effect equivalent evaluation index is analyzed from the two factors of drug effect intensity and action time; the method ensures the equivalence of the medicinal component group and the original traditional Chinese medicine preparation, comprehensively detects the raw material medicines and the preparation, including characters, identification, inspection and content determination items, and ensures that the quality of each batch of samples meets the standard, so that the curative effect of the traditional Chinese medicine preparation is effectively guaranteed, and the efficacy of the traditional Chinese medicine preparation is improved.

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 an automatic input and output method and device, electronic equipment and a storage medium, and belongs to the technical field of data processing, and the automatic input and output method comprises the steps: obtaining a basic medical record image of a patient; identifying text information in the basic medical record image; identifying key information and related information of the key information in the text information by using a trained first deep learning model; and using a trained second deep learning model to determine whether the patient accords with an enrollment condition according to the key information and the related information. According to the method, manual intervention is reduced, the matching speed and accuracy are improved, and a patient can find a clinical test suitable for himself / herself more quickly. The method is of great significance in accelerating the progress of medical research, especially in the field of precise oncology.

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.