32 results about "Patient screening" patented technology

The invention provides a natural language-driven scientific research naming and ranking patient matching method and system, belongs to the technical field of artificial intelligence and medical information processing, and solves the problems of high manual workload, difficulty in semantic analysis, insufficient logical reasoning ability and the like in an existing cancer patient screening mode. The method comprises the following steps: analyzing a containing and arranging condition in a natural language text form in the tumor field to obtain a structured containing and arranging condition logic rule set; performing entity recognition, relation extraction and portrait construction on the multi-modal medical record of the cancer patient to obtain a comprehensive cancer portrait of the patient; and performing logic matching on the comprehensive cancer portrait of the patient and the structured accommodating and discharging condition rule set, determining whether the patient meets accommodating and discharging conditions, and generating a traceable matching interpretation path. The method can effectively solve the problems of large manual workload, difficulty in semantic analysis, insufficient logical reasoning ability and the like in the existing cancer patient screening mode.

The invention discloses a chronic obstructive pulmonary disease patient screening system for the respiratory medicine department, particularly relates to the field of computer-aided diagnosis, and is used for solving the technical problems that an existing screening method is low in screening efficiency, low in patient adaptability and difficult to popularize on a large scale in a basic level. According to the system, a standardized patient medical event sequence is constructed by obtaining a target population medical record containing medicine distribution and disease diagnosis codes; identifying a characteristic medication and diagnosis mode before definite diagnosis of the chronic obstructive pulmonary disease based on a sequence pattern mining technology; constructing a medical knowledge graph by using the modes, obtaining a feature embedding vector representing a chronic obstructive pulmonary disease medical trajectory through graph neural network learning, and establishing a high-risk digital portrait; inputting the medical sequence of the person to be screened into a feature extraction model based on same map training, and generating an individualized risk probability prediction value by calculating the matching degree of the medical sequence and the digital portrait; and a screening result report is automatically generated, so that efficient and noninvasive early risk screening is realized.

The invention discloses a patient follow-up visit system and method based on a precise medical image examination item result, and the system comprises a medical image result analysis module which is used for connecting an HIS and a PACS system, carrying out the standardization processing of an image and clinical data through a deep learning model, automatically analyzing a medical image examination report, configuring a differentiated follow-up visit rule through a department rule base, and carrying out the follow-up visit of a patient. Whether follow-up visit is needed or not is judged according to follow-up visit standards set by all departments; the patient screening and task distribution module is used for carrying out priority classification on the patients meeting follow-up visit standards; the follow-up visit execution module is used for contacting the patient, recording follow-up visit feedback and adjusting a follow-up visit plan according to the feedback condition; the return visit effect evaluation module is used for calculating a return visit effect ratio, generating a visual report, quantifying a follow-up visit effect and providing data support for a hospital; and the data analysis and optimization module is used for optimizing the follow-up visit process based on the follow-up visit data. The follow-up efficiency can be improved, the medical resource configuration is optimized, and the follow-up quality of the patient is improved.

The invention provides an image-based eye lesion grading analysis method and device, a medium, a program product and a terminal, and the method comprises the steps: obtaining one or more eye lesion images, inputting the one or more eye lesion images to a pre-trained first model, and generating a corresponding eye feature vector; for a single image, calculating the similarity between the feature vector and each eye disease category, and outputting a prediction category with the highest similarity; and for a plurality of images, respectively calculating the similarity between the feature vectors and each category, and realizing adaptive weighted integration by using the second model to obtain a final prediction category. Sub-model subdivision and risk level layering are carried out according to prediction categories, and a structured risk layering report is output. The problem that in the prior art, the diagnostic value of multi-image information in complex cases is difficult to effectively evaluate is solved, the accuracy of ultrasound in serious vitreous lesion and intraocular tumor recognition is remarkably improved through a self-adaptive weighted integration mechanism, high-risk patient screening and clinical decision making are supported, and the application scene of ultrasound in ophthalmic diagnosis is expanded.

The invention provides a construction method and a construction system of a chronic lymphocytic leukemia prediction model based on machine learning, electronic equipment and a storage medium, and relates to the field of chronic lymphocytic leukemia prognosis research. The construction method comprises the steps of obtaining original sample data, and performing preliminary screening; performing data cleaning on the screened sample data; determining a prediction factor from the plurality of features of the cleaned sample data; dividing the cleaned sample data corresponding to the prediction factor and the target variable into a training set and a test set; inputting the training set after unbalance processing into a LightGBM model for training to obtain a prediction model; inputting the test set into a prediction model, performing hyper-parameter optimization on the prediction model, and evaluating the performance of the prediction model; calibrating the prediction model to obtain a calibrated prediction model; the method has the beneficial effects that the method can be realized only by depending on conventionally available predictive factors, and the patient screening can be realized before the CLL clinical symptoms appear.

A patient screening system for providing recommendations for screening of a hospitalized patient for risk of developing deep vein thrombosis (DVT) or pulmonary embolism (PE) based on their health records and retinal images, is described herein. The patient screening system may include an optical imaging device for capturing retinal images, and a DVT risk assessment system configured to generate the recommendation for further screening tests. The DVT risk assessment system may implement various AI / ML models trained on a training dataset of anonymized patient data. The patient screening system may also implement detectors for various ophthalmic features correlated with blood clot-related conditions of the patient. Any patient screening based on the recommendation may be followed up, and results of such screening used to improve performance of the patient screening system.

The invention provides a chronic patient screening and personalized processing method and system based on a large model, and relates to the technical field of information management, and the method comprises the steps: obtaining and integrating hierarchical diagnosis and treatment coding data of a patient and continuous physiological signal data collected by wearable equipment; respectively mapping the multi-modal data into embedded representations located in a non-Euclidean feature space through a specific model, and executing efficient feature fusion in the non-Euclidean feature space; and calling a large model trained by federated distillation to carry out deep analysis on the fused features to generate a patient recall priority index. According to the method and the device, accurate identification and recall of high-risk patients can be realized, and after the patients see a doctor, a personalized management path scheme is automatically matched or generated according to the diagnosis results of the patients. By making full use of complex internal association of patient data, on the premise that data privacy is protected, accuracy and efficiency of early screening of chronic diseases are remarkably improved, and intelligentization and individuation of subsequent management are achieved.

The invention provides an image-clinical characterization-combined multi-endpoint prognosis evaluation method for transjugular vein intrahepatic portal vein shunt, which comprises the following steps of: constructing a few-label portal vein segmentation module to obtain a preoperative CT portal vein label of a full-dose patient, and extracting deep learning features and radiomics features of the region; establishing a multi-modal interactive representation learning module for implementation, and performing cross-modal fusion with clinical features to form unified representation; and designing a multi-endpoint prognosis prediction module, inputting the data to a plurality of prognosis task decoders for postoperative survival, portal vein pressure gradient, hepatic encephalopathy prediction and the like, and adopting a multi-task learning optimization model to obtain a postoperative multi-endpoint prognosis evaluation result. According to the method, efficient fusion and multi-endpoint prognosis prediction of images and clinical information can be realized under limited labeling, clinical doctors can be assisted in preoperative patient screening and treatment scheme making, and the method has good clinical application value.

A system for short-term screening and triage of a subject includes a smart device in the subject's possession and an application on the smart device programmed to provide instructions to the subject to start a screening procedure, the application programmed with multiple screening procedures, and record sensor data obtained from a sensor located in the smart device.

A system for short-term screening and triage of subjects includes a smart device owned by the subject and an application on the smart device, the application being programmed to provide the subject with instructions to begin the screening process, the application being programmed to have multiple screening processes and record sensor data obtained from sensors located in the smart device.

The invention relates to the technical field of bioinformatics, in particular to a nano-drug curative effect biomarker mining system based on multi-omics data. The method comprises the following steps: firstly, acquiring multiple omics data of a subject, calculating an initial flux capacity corresponding to each enzymatic reaction, and constructing a directed graph model; further driving the directed graph model to synchronously execute supply-demand bidirectional rate updating at each temperature of the simulated temperature stepping sequence, locking the failure temperature and extracting a speed-limiting enzymatic reaction set limiting the global flux at the failure temperature; finally, mapping the rate-limiting enzymatic reaction set into a sensitive patient screening index combination; according to the method, multiple omics data are obtained to construct an enzymatic reaction directed graph, network supply and demand are deduced through temperature stepping, the failure temperature is locked, the rate-limiting enzymatic reaction is recognized, and the screening index combination is mapped, so that the mining accuracy of the nano-drug curative effect related biomarkers is improved; the technical problem that in the prior art, a sensitive patient screening index combination related to the nano-drug curative effect is difficult to determine is solved.

This invention belongs to the field of tumor prognostic assessment technology and discloses a prognostic assessment system for lung adenocarcinoma based on the p53 R248Q mutation. The system includes a detection module, a data input module, a risk stratification module, and a result output module, which sequentially realize data interaction and flow. These four modules achieve unidirectional data flow and bidirectional interactive verification, forming a closed-loop prognostic assessment system. The functions of each module support each other and progress progressively. The core objective is to accurately screen patients with EGFR / p53 R248Q co-mutated lung adenocarcinoma and achieve individualized survival risk stratification based on the clinical cohort's overall survival (OS) quartile method. This system realizes the full-process implementation of molecular testing, patient screening, risk assessment, and clinical application.

The invention relates to the technical field of medical diagnosis, in particular to a pelvic floor patient condition dynamic tracking system based on the Internet technology, and the system comprises a data acquisition module, a clustering module, a historical patient screening module, a model construction module and a data screening module. Analyzing pelvic floor monitoring data of different historical patients, and clustering all the historical patients to obtain a plurality of clusters; screening the historical patients in each cluster according to the reference of different historical patients; based on the pelvic floor monitoring data of the screened target historical patient, constructing an illness state reference model corresponding to each cluster; and determining a matched illness state reference model in all illness state reference models so as to screen effective illness state data in the real-time pelvic floor monitoring data of the patient. According to the invention, the screening accuracy of effective data in pelvic floor function real-time monitoring data is effectively improved.

A computer-implemented method includes receiving from a user over a network a media file including a recorded patient screening interview, receiving from the user over the network data comprising one or more responses provided by the patient to a mental health questionnaire, generating a transcription of audio associated with the media file, presenting to the patient via a graphical user interface (GUI) a first question of the set of questions, performing video sentiment analysis to generate a second data set, presenting to the patient via a graphical user interface (GUI) the second set of questions based on at least one of the transcription, first and / or second data, generating an artificial intelligence model configured to determine a score characterizing a relevance of a mental health topic to the patient and provide predicted risk levels for one or more mental health conditions based on the patient's first and second set of answers.

The invention relates to the technical field of biological medicine. The invention provides application of ethanol and pyrrole in exhaled gas as markers in preparation of products for diagnosis or auxiliary diagnosis of Alzheimer's disease. The markers are ethanol and pyrrole. According to the method, a brand new AD diagnosis marker is mainly found from expired gas of a human body, an expired gas sample is easier to obtain compared with a body fluid sample, the patient and family member matching degree is higher, due to the fact that expired gas collection is completely noninvasive, the corresponding detection means is high in detection speed and easy to operate, and the method has a wider application prospect in AD patient screening.

The invention discloses an intelligent screening system for osteoporosis patients based on big data. The intelligent screening system comprises a data acquisition module, an osteoporosis severity measurement axis construction module, a grade interval division module, a grade boundary calibration module and an intelligent screening module. The invention relates to the technical field of osteoporosis intelligent screening, in particular to an osteoporosis patient intelligent screening system based on big data. According to the scheme, a one-dimensional severity axis is constructed, a sample graph and graph Laplacian smoothing are weighted through similarity, local manifolds are reserved, monotonous micro-projection is learned, equidistant calibration is carried out, interpretable severity scores are output, and the screening precision of osteoporosis patients is improved. Screening and sorting are facilitated, and order level center and interval constraint are carried out through explicit learning; the same-level consistency is improved through flexible distribution with entropy regularization and in-segment tightness, probabilistic distribution is output, ordered curvature constraint is applied based on typical samples and the main projection direction, and the samples and the center are aligned with the assistance of bidirectional correction and alternate optimization, so that the accuracy and comprehensiveness of osteoporosis patient screening are enhanced.

The application provides an artificial intelligence-based patient screening method and system in clinical research, relates to the technical field of artificial intelligence, and comprises the following steps: obtaining a clinical research enrollment standard text and performing semantic analysis to extract a rule tuple, and converting the rule tuple into a standard schema graph; selecting a disease node in a medical knowledge graph to perform extension matching to obtain an adaptive patient subgraph; performing graph structure alignment to identify a matching substructure, a missing item and a conflict item; and finally calculating the adaptability of a patient to a research project and generating a matching result. The application improves the patient screening efficiency and accuracy, and reduces the clinical trial recruitment cost.

The invention discloses an intelligent regulation and control system for combined treatment of glioblastoma, and relates to the technical field of intelligent equipment. The intelligent regulation and control system for glioblastoma combined treatment comprises a patient screening module used for acquiring tumor image data and cell cycle data of a patient; the drug delivery module comprises a temozolomide drug delivery unit and an IL-6 receptor antibody drug delivery unit; the biomarker monitoring module is used for detecting a DNA damage marker level, a serum cell factor concentration and a neuron damage marker level in tumor cells of the patient; the data analysis and decision-making module is used for receiving the patient tumor image data and the cell cycle data output by the patient screening module and the detection result output by the biomarker monitoring module, automatically judging a treatment node based on a built-in preset threshold value and sending a regulation and control instruction to the drug administration module; the synergistic administration of the temozolomide and the IL-6 receptor antibody is realized. The system can realize precise treatment of glioblastoma.

The application provides a natural language driven scientific research patient matching method and system, and belongs to the technical field of artificial intelligence and medical information processing, and solves the problems of large artificial workload, difficult semantic analysis and insufficient logical reasoning ability of the existing cancer patient screening method. The method comprises the following steps: analyzing the tumor field natural language text form of the inclusion and exclusion criteria to obtain a structured inclusion and exclusion criteria logical rule set; performing entity recognition, relationship extraction and image construction on the multi-modal medical records of the cancer patients to obtain a comprehensive cancer image of the patients; performing logical matching on the comprehensive cancer image of the patients and the structured inclusion and exclusion criteria rule set to determine whether the patients meet the inclusion and exclusion criteria, and generating a traceable matching explanation path. The method can effectively solve the problems of large artificial workload, difficult semantic analysis and insufficient logical reasoning ability of the existing cancer patient screening method.

The application discloses application of lncRNA PRDM7-1:2 as a marker in development of a tuberculosis patient screening reagent. The application provides application of a substance for detecting lncRNA PRDM7-1:2 or a characteristic fragment of lncRNA PRDM7-1:2 in preparation of a kit for screening tuberculosis patients or in preparation of a kit for diagnosing or assisting in diagnosing tuberculosis. The inventors have carried out whole genome RNA chip screening, completed lncRNA spectrum screening and comparative analysis in PBMCs from two types of people, i.e. tuberculosis patients and healthy people, and preliminarily obtained a large number of differentially expressed lncRNAs. The inventors have carried out qPCR verification on the preliminarily obtained lncRNAs in another independent sample, and screened two lncRNAs. The inventors have analyzed the performance of the two lncRNAs as separate markers in a new population. The application is very important for rapid diagnosis of tuberculosis and early treatment.

The invention provides a patient screening method and device based on a mild cognitive impairment patient model, a storage medium and electronic equipment, and relates to the technical field of mild cognitive impairment screening, and the method comprises the steps: collecting research sample data based on community natural population health data; extracting training data based on the research sample data; training an extreme gradient lifting algorithm model based on the training data to obtain a plurality of preliminary models; performing model performance evaluation on the plurality of preliminary models, and selecting the preliminary model meeting evaluation requirements as a rapid screening model for mild cognitive impairment; screening patients with mild cognitive impairment based on the rapid screening model for mild cognitive impairment. Through the method, patients with mild cognitive impairment can be accurately screened, and the screening efficiency is high.

Described herein is a patient screening system for providing a recommendation for screening a patient for a potential disease or risk of disease based on the patient's health record and retinal image. The patient screening system can include an optical imaging device operable at a physician's office, and an associated method configured to generate the recommendation. The patient screening system can implement various AI / ML models trained on a training dataset of anonymized patient data. The patient screening system can be based on discovering correlations in the training dataset between features of retinal images and health records, and corresponding disease diagnoses contained in the health records. The patient screening system can also implement classifiers for various diseases based on the data in the training dataset. Any patient screening based on the recommendation can be followed up on, and the results of such screening can be used to improve the performance of the patient screening system.

The invention discloses a medical digital case intelligent management method, and relates to the technical field of medical management, and the method comprises the steps: receiving multi-modal original case data of a patient, carrying out the preprocessing of the multi-modal original case data, and outputting a structured data package with a timestamp; key point location information is extracted based on the preprocessed structured data packet, similarity matching is carried out on the key point location information and a predefined disease template library, and a disease template is determined; according to the key point location information and historical key point location information, a sequential logic rule is verified, and the key point location information is distributed to the corresponding diagnosis and treatment nodes in the disease template and stored. The refined data management mode not only helps to improve the patient screening efficiency of clinical projects, but also provides convenience for later data tracking and analysis.

The present invention relates to the field of medical information technology, specifically to an artificial intelligence-based method for screening and dynamic follow-up management of clinical trial patients, comprising the following steps: collecting and standardizing admission, examination, medication, and imaging data, mapping trial items to form associated health data, screening qualified patients according to the inclusion criteria, extracting populations with differences in medical unit and patient distribution markers, analyzing physiological data fluctuation trends, adjusting the follow-up sequence to generate a dynamic management plan. In the present invention, by collecting structured health information and mapping trial items, accurate screening and synchronous rhythm control are achieved, the distribution of patients and medical units is analyzed, differential groups are identified, resource allocation is optimized, physiological data is constructed into a time series and the difference is calculated to capture health fluctuation trends. The time schedule is adjusted in conjunction with the follow-up nodes to improve execution efficiency, and multiple links collaborate to enhance the response speed of patient management and the integrity of data processing.

The invention provides a mild cognitive impairment patient screening model training data acquisition method and device and electronic equipment, and relates to the technical field of training data acquisition, and the method comprises the steps: constructing a community natural population cognitive health queue, and obtaining related evaluation data of queue research sample cognition; on the basis of a literature review method, a professional domain knowledge injection method, a domain expert consultation method and a Shap interpretable machine learning method, characteristic variables related to the mild cognitive impairment are screened out from the evaluation data and serve as model training data. The degree of association between the model training data obtained through the method and the mild cognitive impairment is higher, the data source is more real, and the method has more practical application significance.

The application provides a peripheral arterial disease patient rehabilitation management system based on a whole process, and relates to the technical field of peripheral arterial disease patient rehabilitation management.The application comprises a patient screening and risk grading module, a rehabilitation evaluation module, a motion prescription generation and dynamic adjustment module, an intelligent propaganda content pushing module and a quality control closed loop management module.The application realizes accurate screening of PAD patients through a medical data interface and NLP technology, dynamically evaluates the risk level of the patients in combination with an intelligent risk grading system;generates personalized motion prescriptions according to evaluation reports, complications and biological monitoring data, and guarantees the safety of motion through a dynamic threshold model and collaborative early warning;realizes dynamic matching and timeliness pushing of propaganda content by using an improved KL divergence algorithm;and realizes whole process quality closed loop management through the quality control closed loop management module.The application can improve the accuracy and safety of PAD patient rehabilitation management, optimize medical resource allocation, and help whole process data-driven rehabilitation practice.

The invention provides a diagnostic radiopharmaceutical based on an anti-tissue factor antibody as well as a preparation method and application of the diagnostic radiopharmaceutical, and belongs to the technical field of biological medicine preparation. The invention relates to a diagnostic radiopharmaceutical based on an anti-tissue factor antibody. The diagnostic radiopharmaceutical comprises an anti-tissue factor humanized antibody and a diagnostic radionuclide conjugated with the anti-tissue factor humanized antibody. The medicine is combined with high-resolution PET / CT imaging or SPECT / CT imaging, so that precise diagnosis before treatment and applicable patient screening can be realized. The two drugs are based on the same target spot and the antibody carrier, and diagnosis and treatment integrated pairing with consistent pharmacokinetics is formed. The medicine shows good tumor targeting property, retention property and remarkable tumor inhibition effect in a tumor-bearing mouse model, is good in safety, is used for preparing medicines for diagnosing and treating various TF high-expression solid tumors (such as pancreatic cancer, cervical cancer and colorectal cancer), and has a wide clinical transformation prospect.

The present invention relates to the field of biomedicine. The present invention provides a diagnostic marker for Alzheimer's disease based on volatile organic compounds in exhaled breath, wherein the marker is one or more of ethanol, isopropanol, ethyl chloride, pyrrole, n-butanol, and benzene. The present invention primarily seeks novel diagnostic markers for AD from human exhaled breath. Exhaled breath samples are easier to obtain than body fluid samples, and patients and their families are more cooperative. Exhaled breath collection is completely non-invasive, and the corresponding detection methods are fast and easy to use, offering broader application prospects in AD patient screening.

The invention discloses a medical decision support method and system based on multi-modal data, relates to the field of medical decision, and solves the problem of poor decision application effect of a medical decision support technology, and the method comprises the steps: S1, carrying out the illness state text collection of a target patient, enabling the illness state text to interact with an inquiry model, and obtaining an illness state interaction strategy text, s2, performing common decision-making patient acquisition on illness state treatment decisions, performing symptom analysis on the common decision-making patients, obtaining decision-making sample patient screening data according to the analysis result, and performing medical index analysis on the illness state interaction strategy text to obtain medical decision preliminary acquisition data according to the analysis result; s3, carrying out decision applicability analysis on the disease treatment decision, and obtaining decision applicability analysis data according to an analysis result, and S4, carrying out medical decision support on the target patient according to the decision applicability analysis data, so that the scientificity and operability of decision support can be improved.