637 results about "Clinical research" patented technology

The invention discloses a medical whole-course intelligent management system based on a large model, and belongs to the technical field of large models. Comprising a multi-modal data acquisition module, a privacy calculation preprocessing module, a dynamic knowledge enhancement module, a time sequence data analysis module, an intelligent decision engine module, a multidisciplinary collaboration module, a patient interaction platform module, a dynamic intervention feedback module and a system security center module. The cross-mechanism data security sharing is realized, and the compliance of sensitive information processing is also ensured; a two-channel medical knowledge base is constructed, authoritative guidelines can be synchronized, newest clinical research data can be analyzed in real time, the knowledge base is kept in the newest state all the time, and the frontier scientific basis is provided for clinical decisions; dynamic modeling and trend prediction are carried out on long-term monitoring data of a patient by adopting a hybrid neural network model, and potential health risks and development trends can be identified more accurately.

The invention discloses a clinical research data analysis method based on machine learning, and the method comprises the steps: constructing a multi-modal variable structured causal map, and building a direction adjustable mechanism of a causal path; constructing a bidirectional nested structure attention mechanism, and capturing a cross-modal dependency and dynamic evolution relationship between variables; recording each layer of information propagation path and variable participation degree, and realizing reverse reconstruction of a model decision path in a reasoning stage; target-oriented attribution path regularization is introduced to carry out regularization constraint on an attribution path set of the key target variables; and constructing a nested attribution graph visualization system, and realizing interactive presentation of interpretation sub-graphs corresponding to prediction results so as to improve cognitive trust of model output. According to the method, from structure expression, path tracing and causal constraint to visual presentation, the core problems that a deep model is poor in interpretability, clinicians are not trusted, and existing interpretation tools are insufficient in applicability are solved in a full-link mode.

The invention provides a clinical research medical supervision method and system based on artificial intelligence, and relates to the technical field of medical supervision. The method comprises the following steps: constructing a medical supervision ontology knowledge base by obtaining related medical knowledge texts, and mapping medical supervision data sample data into an ontology framework based on the knowledge base to form a medical supervision knowledge graph. Then, knowledge extraction is carried out on the medical supervision application data to obtain review elements, the review elements are mapped into a knowledge graph to obtain related ontology concept nodes, and a review element graph is formed; then, reasoning and judging the review element graph based on the medical supervision knowledge graph and a preset rule to form a preliminary review result; and finally, performing quantitative scoring on the preliminary review result, and performing review decision based on the score and the preliminary result to obtain a final review decision result. According to the method, the medical inspection efficiency and the inspection quality are improved.

The invention discloses a traditional Chinese medicine clinical research scheme generation method and system, and relates to the field of medicine. The method comprises the following steps: aiming at traditional Chinese medicine clinical research, constructing a knowledge graph covering evidence-based evidence elements PICO; establishing a traditional Chinese medicine cue word project and an element template based on evidence-based evidence elements PICO covered by the knowledge graph; identifying element information selected in the element template by adopting a traditional Chinese medicine cue word project, and generating a cue word feature vector; retrieving data matched with the cue word feature vector from the knowledge graph by adopting a trained language model; when the feature in the cue word feature vector cannot retrieve the matched data from the knowledge graph, automatically generating the matched data by using the trained language model; and integrating the matching data automatically generated by the trained language model and the matching data retrieved from the knowledge graph to obtain a traditional Chinese medicine clinical research scheme. The preparation efficiency and quality of the traditional Chinese medicine clinical research scheme can be effectively improved.

The invention discloses a construction method of a traditional Chinese medicine alert question-answering system based on a large language model. The method comprises the following steps: establishing a traditional Chinese medicine knowledge base; constructing a retrieval enhancement generation model PV-RAG oriented to drug alert, and training and optimizing the PV-RAG model by taking the traditional Chinese medicine knowledge base as a training set; the PV-RAG model comprises a retrieval module, a text extraction module, a query rewriting module and a generation module; and constructing an intelligent question and answer application end, and generating a traditional Chinese medicine alert answer according to a natural language question input by a user. According to the traditional Chinese medicine alert question-answering system based on the large language model and the retrieval enhancement generation technology, deep semantic understanding of traditional Chinese medicine literatures is achieved through the text embedding model, multi-source data such as traditional Chinese medicine specifications, traditional Chinese medicine classics and clinical research literatures are effectively integrated, and the traditional Chinese medicine alert question-answering system is established. And rich knowledge support is provided for traditional Chinese medicine alert questions and answers.

The invention relates to the technical field of medical data management and disease auxiliary decision making, and discloses an auxiliary diagnosis and treatment data management system based on a disease full-path atlas model, and the system comprises a multi-source heterogeneous database which integrates and standardizes medical data; the node coding module carries out diagnosis and treatment node coding; the graph module constructs a disease knowledge graph and identifies path nodes; the diagnosis and treatment auxiliary module generates decision suggestions through path matching; the research auxiliary module is used for screening homogeneous cases to support clinical research; a cross-modal attention mechanism, reinforcement learning optimization coding rules and GNN dynamic path generation are integrated, causal reasoning is combined to eliminate hybrid variables, and multi-center privacy cooperation is realized based on federated learning. According to the method, a multi-modal database is constructed through standardization and path processing, homogeneous cases are screened by utilizing a full-path map, key features are extracted, intelligent decision support is provided for clinical diagnosis and treatment, meanwhile, efficient data analysis service is provided for scientific research, and medical data management and application are optimized.

The invention provides an esophageal cancer marker screening method based on enzyme transfer method methylation sequencing, which comprises the following steps: S1, collecting esophageal early cancer and para-carcinoma tissue samples, and carrying out enzyme transfer method whole genome methylation sequencing; s2, integrating methylation data of esophageal cancer and para-carcinoma tissues in databases of TCGA, GEO, MethMarker DB and the like; s3, screening differential methylation sites, preferably sites which are hypermethylated in esophageal cancer but hypomethylated in samples such as leukocytes, so as to reduce plasma background interference; s4, obtaining a candidate marker combination based on an integrated methylation feature screening model of LASSO regression and a random forest algorithm; s5, constructing a classifier model based on the candidate marker combination, carrying out training and reasoning test on the model, and evaluating the prediction performance of the model; and S6, obtaining the esophageal cancer marker combination. According to the method, enzyme transfer sequencing and machine learning are combined, the marker prediction accuracy is remarkably improved, and a new reference is provided for clinical research of esophageal cancer.

The invention discloses a patient immune response monitoring method and system for cell drug clinical research, and the method comprises the following steps: 1, obtaining a peripheral blood sample of a patient, and preprocessing the peripheral blood sample to obtain patient sample components; multi-dimensional immune indexes and cell drug specific immune response are detected by comprehensively utilizing multiple technologies, immune cell subsets, cell factors, immune globulin, T cell response and the like are covered, and therefore a complex immune response system is comprehensively reflected; peripheral blood samples are collected at multiple time points according to a cell drug action mechanism and a half-life period, and the whole-course change of the immunoreaction is effectively tracked, so that the development trend of the immunoreaction is accurately grasped; the method has the advantages that the characteristics are visually presented by constructing the immune response atlas of the patient, evaluation and prediction are assisted, real-time monitoring reports and treatment suggestions are provided according to evaluation and prediction results, doctors are assisted to adjust treatment in time, and accordingly the safety and effectiveness of cell drug treatment are improved.

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 discloses a lung adenocarcinoma prognosis risk prediction model and a construction method and application thereof, and belongs to the technical field of biomedical detection. The lung adenocarcinoma (LUAD) prognosis model is successfully constructed by systematically analyzing the immune infiltration related gene (CAFRG) of tumor-associated fibroblasts (CAF), and the application value of the lung adenocarcinoma (LUAD) prognosis model in prediction of patient prognosis is verified. The CAFRG risk score is negatively related to the immune microenvironment score and is remarkably related to immune checkpoint gene expression, which suggests that a high-risk patient may have the characteristic of immune escape. Furthermore, based on TIDE tool analysis, the patients in the low-risk group have more active T cell immune response. The risk score is also closely related to the sensitivity of antitumor drugs, especially Doramapimod. Therefore, a new theoretical basis is provided for personalized treatment of lung adenocarcinoma, and a new direction is provided for future clinical research.

The invention relates to the technical field of medical treatment, and discloses an intelligent subject evaluation method based on multi-dimensional data, which comprises the following steps: acquiring the multi-dimensional data corresponding to a subject, inputting the multi-dimensional data into a pre-trained evaluation model, and acquiring an analysis report of the subject participating in clinical research, the multi-dimensional data comprises clinical symptoms, traditional Chinese and western medicine diagnosis, medication information and social background of the subject, and the analysis report comprises accuracy, compliance and risk; based on the analysis report, determining a corresponding group-entering suggestion and a treatment research scheme for each subject, the treatment research scheme including drug selection, dose adjustment and a follow-up plan; in the clinical research process, the latest research data are continuously obtained, and the analysis report and the treatment research scheme of the subject are adjusted based on the latest research data. The quality and effectiveness of clinical research can be improved.

The invention relates to a medical image generation method and device based on a migration model, computer equipment and a storage medium, and aims to ensure data quality and consistency and lay a foundation for model training by acquiring and preprocessing a medical image report data set and generating an initial medical image and corresponding initial pathological text data. And constructing a text-to-image generation model based on a diffusion model, and preliminarily realizing text-to-image conversion. The generation model is finely adjusted by using initial data, and the generation precision and adaptability are improved. And analyzing the multiple pieces of target pathological text data, generating a target medical image by using the optimization model, and verifying the validity of the model in practical application. According to the technical scheme, transfer learning and a diffusion model are combined, the problems of medical data finiteness and distribution inconsistency are solved, the medical image generation effect and application value are improved, and important technical support is provided for clinical research and medical diagnosis.

The invention discloses a three-dimensional clinical research method of deciduous tooth anchorage on maxilla hypoplasia under the guidance of an HPC line. According to the invention, the deciduous tooth anchorage technology guided by the HPC line breaks through the limitation of the traditional tooth support type and bone support type correction, and creates a third anchorage selection mode for osteomalocclusion. By accurately positioning the central axis of the deciduous tooth crown as a supporting point, the technology not only retains the effectiveness of the traditional method on the transverse expansion of the maxilla, but also remarkably reduces the side effects of mesial inclination, vertical lifting and the like of the permanent tooth, and realizes the dual purposes of bony reconstruction and dentition stabilization. The innovativeness is that the physiological characteristics of deciduous teeth are combined with the biomechanical principle, the technical scheme is provided for the first time in China, the blank of correction of complex malocclusion in mixed dentition is filled, and a safer and more efficient treatment path is provided for clinic.

In some implementations, a system dynamically adjusts an electronic form of an application based on user data and form data specified for the electronic form. Form data that specifies characteristics of the electronic form is initially received by a computing device. The form data specifies one or more rules configured to vary the content of the electronic form that is presented for different users. User data indicating characteristics of a user is then obtained by the computing device. An interactive element is then selected from among a set of multiple interactive elements based on the received user data and the rules in the received form data. A view of the electronic form is customized for the user by including, in the customized view, the interactive element. The view of the customized electronic form is then displayed by the computing device.

Provided herein are methods for administering a hepatitis B virus surface antigen (HBsAg) to subjects with chronic hepatitis B to distinguish between responder subjects who respond to the HBsAg administration with an increase in an HBV antibody levels and non‑responder subjects, and treating chronic hepatitis B in the responder subjects with an HBV therapy. Methods of excluding non-responder subjects from a clinical study are also provided.

The present invention relates to a method for producing a canine atopic dermatitis animal model, a canine atopic dermatitis animal model produced thereby, and a use thereof. According to the present invention, a canine atopic dermatitis animal model is produced by skin stimulation on a dog through epicutaneous sensitization with ovalbumin at a concentration of 1 to 5 mg / ml once daily for 1 to 2 weeks and then with ovalbumin at a concentration of 1 to 5 mg / ml once daily for 2 to 8 weeks. The resulting animal model exhibits clinical symptoms of atopic dermatitis, changes in relevant factors such as increased expression levels of IgE and CCL17, and elevated expression levels of Th2-related cytokines IL-4, IL-31, and IL-13. Therefore, this invention enables the establishment of a standardized animal model for symptoms and Th2 immune responses appearing upon the onset of atopic dermatitis, and the model can be used in clinical studies for screening candidate agents for the prevention, alleviation, or treatment of atopic dermatitis.

The invention provides a method for enhancing Treg cell functions and application, and belongs to the technical field of cell engineering. According to the method, hUC-MSCs are irradiated by gamma rays to form feeder layer cells, and then the feeder layer cells and Treg cells are co-cultured to obtain the Treg cells with enhanced cell functions. Through the co-culture of the feeder layer cells and the Treg cells, the inhibition ability of the Treg cells on activation and proliferation of immune cells can be enhanced, and the feeder layer cells and the Treg cells can be used as universal cells in clinical researches of autoimmune diseases, graft versus host diseases and the like.

The invention relates to a construction method of a four-gene editing xenogenic organ transplantation donor pig, and belongs to the technical field of animal biology. In a wild type pig fetal fibroblast cell line, a GGTA1 gene is knocked out by utilizing a CRISPR / Cas9 gene editing technology, three humanized genes of hCD39, hCD55 and hTBM are transferred, a GTKO / hCD39 / hCD55 / hTBM four-gene editing cloned pig is constructed in combination with a somatic cell nucleus transplantation technology, and four-gene editing xenogenic organ transplantation donor pigs are further obtained in batches through genotype identification and protein function phenotype analysis. According to the invention, the common problem of antigen-antibody mediated immunological rejection in a pig-human xenograft process is solved to a certain extent, and a foundation is laid for further research and development of donor pigs suitable for xenograft of organs / tissues such as heart, kidney, liver, skin and the like; the method has an important prospect for carrying out pre-clinical research on pig-non-human primate xenogeneic organ transplantation and clinical application of pig-human xenogeneic organ transplantation.

The invention provides application of TRMT61A and its inhibitor tetramethylthiuram disulfide in tumor immunotherapy, and relates to the technical field of biological medicine, clinical research proves that RNA m1A methyltransferase TRMT61A is highly expressed in tumor tissues and is related to poor prognosis of tumor patients, and can be used as one of tumor therapy targets. Meanwhile, high expression of the TRMT61A is related to poor prognosis of a tumor patient receiving anti-PD-1 immunotherapy, and after anti-PD-1 antibody immunotherapy is carried out on the patient with low TRMT61A expression level in the tumor of the patient, pathological complete relief is easier. Tetramethylthiuram disulfide inhibits TRMT61A, inhibits m1A modification regulation of TRMT61A on PD-L1 mRNA, increases PD-L1 protein expression, and is combined with a PD-1 antibody drug to significantly enhance the immunotherapy effect of the anti-PD-1 immune checkpoint drug in treating malignant tumors.

The invention relates to the technical field of electric digital data processing, and discloses an intelligent data processing method for clinical research. The method comprises the following steps: constructing a basic clinical knowledge graph fused with an international medical ontology; the method comprises the following steps: performing deep learning driven entity recognition and knowledge graph linking on original data from heterogeneous sources such as an electronic medical record and an inspection system; executing cross-source entity alignment and knowledge fusion based on the graph attention network; performing automatic data quality verification and restoration according to clinical logic rules embedded in the atlas; and finally, extracting and generating a standardized analysis ready data set from the enhanced atlas according to research requirements. According to the technical scheme, high-quality, automatic and semantic integration and flexible delivery of clinical research data are realized, and the data processing efficiency and research reliability are improved.

The invention relates to a method for detecting the content of fosfomycin sodium for injection by using high performance liquid chromatography. According to the method, an electrospray detector is used as a detector, a chromatographic column with octadecyl bonded silica gel as a filler and a formic acid aqueous solution as a mobile phase are adopted, and the content of fosfomycin is obtained by combining linear regression analysis and a specific calculation formula. The invention also relates to a kit comprising a chromatographic column and a mobile phase for use in the method of the invention. According to the method, the required fosfomycin content can be conveniently, quickly, accurately and efficiently measured, the operation is simple, the specificity, the reproducibility and the precision are good, the method is very suitable for detection of compatibility experiment samples of fosfomycin sodium for injection, and valuable and referable fosfomycin content data can be provided for clinical research of fosfomycin sodium related preparations.

The invention relates to the technical field of information processing, in particular to a traditional Chinese medicine informatization processing system and method for acute lung injury. The system comprises a traditional Chinese medicine information slice storage module, a traditional Chinese medicine knowledge graph construction module, a dialectical auxiliary reasoning module and a prescription sorting recommendation module. An acute lung injury traditional Chinese medicine information database can be generated by collecting and integrating ancient traditional Chinese medicine poeia information corresponding to acute lung injury, domestic and overseas clinical research information and acute lung injury patient record data extracted from electronic medical record systems corresponding to various hospitals and performing slice coding storage; performing entity and relation extraction and knowledge graph construction according to the acute lung injury traditional Chinese medicine information database, and generating an acute lung injury traditional Chinese medicine knowledge graph; dialectical auxiliary reasoning and traditional Chinese medicine prescription sorting recommendation are carried out based on the acute lung injury traditional Chinese medicine knowledge map, and a traditional Chinese medicine prescription suggestion scheme sequence corresponding to the acute lung injury syndrome type is generated. The information processing efficiency of acute lung injury traditional Chinese medicine prescription recommendation can be improved.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for managing and adapting monitoring programs. In some implementations, a system communicates with a set of remote devices involved in a monitoring program that involves collection of data from the remote devices over a communication network. The system determines a set of attributes associated with an outcome or condition that has occurred for multiple of the remote devices. The system generates parameters to adapt the monitoring program, and the system selects a second group of remote devices to involve in the adapted monitoring program based on profiles or sets of attributes of users associated with the remote devices. The system configures the remote devices in the selected second group to perform monitoring for the adapted monitoring program, including acquiring data for the adapted monitoring program and providing the acquired data to a server over the communication network.

The invention relates to an intelligent inquiry and risk evaluation method for gynecological tumors. The method comprises the following steps: acquiring symptom information of a consultant and tumor medical history information of relatives in a plurality of generations of families of the consultant; constructing a tumor genetic family diagram by using the tumor medical history information, and then evaluating genetic risks of consultants to generate genetic consultation suggestions; constructing a symptom semantic network based on a medical ontology, and converting the symptom information into standard symptom data; calling a corresponding hierarchical inquiry protocol according to the standard symptom data to generate an intelligent diagnosis result; and integrating the genetic risk assessment result and the intelligent diagnosis result and outputting a diagnosis suggestion. Gynecological tumor inquiry consultation and risk evaluation can be automatically completed, and meanwhile a clinical research form is generated and inserted into a hospital electronic medical record system. The whole process does not need manual operation of doctors, the workload of information collection and input of the doctors is greatly reduced, and the doctors can concentrate on the core diagnosis and treatment process.

To provide an examinee candidate extraction system for promptly and efficiently extracting an examinee candidate of a clinical research by utilizing information collected on the basis of the will of a patient, an examinee candidate extraction device, an examinee candidate extraction method and a program.SOLUTION: An examinee candidate extraction system 1 includes an integrated repository 11 and an examinee candidate extraction device 17. The integrated repository 11 stores in an associated manner, medical information of a patient which is inputted or generated by a medical institution and previously permitted by the patient, the physical information of the patient inputted or generated by the patient and previously permitted by the patient, and information relating to the will of the patient to be an examinee candidate. The examinee candidate extraction device 17 extracts from the integrated repository 11 the patient having the predetermined medical information and the physical information from among the patients having the wills to be the examinee candidate in accordance with a selection condition or algorithm.SELECTED DRAWING: Figure 1

The invention discloses an intelligent adverse drug reaction acquisition system and method based on clinical practice, and the method comprises the steps: obtaining a medication record, a disease course text and a nursing record of a patient, and an inspection index; s2, performing identification and relation extraction on the data acquired in the S1, and constructing a time-sequenced knowledge graph of medicine-indications-test abnormality-clinical bad symptoms; training a Transform prediction model through a time sequence knowledge graph, complementing a knowledge graph relationship, and predicting a missing relationship and an entity; s3, monitoring the change of physiological indexes of the patient after medication in real time, performing dual-channel early warning, adding the detected adverse reaction into the prediction model in S3, and performing update training on the prediction model; a time sequence knowledge graph is constructed, the correlation between the medicine and the adverse reaction is comprehensively and systematically presented, powerful support is provided for clinical research and supervision, and the medicine safety monitoring level is improved.

Systems and methods provide efficient access to electronic data capture (EDC) and electronic medical record (EMR) data for monitoring clinical research. A multi-tenant clinical research platform is configured to support remote management of clinical research sites based on EDC and EMR data. The platform is configured to access EDC data from an EDC data store, access related EMR data from an EMR data store that is separate from the EDC data store, and use mappings between the EDC data and the EMR data to contextually integrate the EMR data into a clinical research review workflow provided by the platform. In some embodiments, the platform is configured to prevent persistence of the EDC data and / or EMR data within the platform. The platform provides remote, on-demand controlled access to the EDC data and the EMR data to facilitate offsite reviews of clinical research sites based on EDC and EMR data.

The present invention provides a data management method for clinical research projects, the method comprising: in response to a node event initiated by a workflow management center, triggering a call request, the workflow management center communicating with multiple clinical research tools; in response to the call request, calling a specific research tool among the multiple clinical research tools, and collecting at least one target parameter provided by the specific research tool; creating a customized format copy of the target parameter and storing it in an underlying database. In addition, the present invention also provides corresponding computer equipment and readable media, as well as a data management system for clinical research projects. Implementation of the various embodiments of the present invention helps to improve the efficiency of team collaboration and clinical research.

The invention provides an application of a spleen T lymphocyte exosome in construction of a depression animal model, a stress rat spleen T lymphocyte exosome is injected into a mouse body to construct the depression animal model, and the depression animal model can be used for depression drug screening, drug efficacy evaluation or diagnosis, and can be used for treating depression. And the method can also be used for researching and revealing pathogenesis of depression. The invention also proves that the IL4 / IL4R alpha signal channel and the BDNF signal channel play an important role in depression through experiments. The invention has important significance for clinical research of depression.

The metabolic marker can be used for early diagnosis of heart failure, has the advantages of high sensitivity, rapidness, convenience and accurate and reliable result, provides a basis for clinical decision, provides a certain basis for subsequent fundamental research and clinical research, and has potential application and research values. The metabolic marker comprises one or more of 1-(4-nitrophenyl) piperidine, sphingolipid d19: 3 / 23: 0, phosphatidyl ethanolamine 16: 1e / 22: 5, phosphatidyl ethanolamine 16: 1e / 22: 6, sphingolipid d14: 0 / 22: 1, 3-phosphoglyceric acid, phosphatidylcholine 15: 0 / 20: 4, phosphatidylcholine o-16: 1 / 18: 0, phosphatidylcholine 14: 0e / 20: 1, phosphatidylcholine 17: 0 / 18: 5, betaine, phosphatidylcholine 15: 0 / 18: 2 and phosphatidylcholine 18: 5e / 20: 4.