24 results about "Laboratory examination" patented technology

The invention discloses a fusion mode health physical examination psychological test system, which belongs to the technical field of psychological test and comprises a physical examination data acquisition module, a psychological test module, a multi-modal data fusion module, a result analysis module and a user interaction module. The physical examination data acquisition module is used for acquiring physiological health data of a user, including physical sign data, laboratory examination data and image examination data; the psychological test module provides a plurality of standardized psychological assessment scales; the multi-modal data fusion module is used for performing feature alignment and information fusion on the physiological health data acquired by the physical examination data acquisition module and the psychological test original data generated by the psychological test module, performing fusion analysis on the physiological health data and the psychological test data through the multi-modal data fusion module, and mining the relevance between the physiological health data and the psychological test data; the problems that traditional physical examination and psychological testing are independent and evaluation is not comprehensive are solved, and more complete health condition cognition is provided for the user.

The invention discloses a VTE occurrence risk assessment device. The device comprises a data acquisition module, a data analysis module and an early warning module, the data acquisition module is used for automatically acquiring the age, height and weight of the patient from the hospital information system, acquiring the past medical history, operation duration, family history and symptom signs of the patient from the electronic medical record system and acquiring the laboratory examination result of the patient from the laboratory information system when the evaluation time arrives; the data analysis module automatically evaluates the VTE occurrence risk level of the patient according to the obtained data; and the early warning module automatically generates a corresponding alarm message when the VTE occurrence risk level of the patient is a high risk level, and visually displays the alarm message. According to the method and the system, the assessment result of the VTE generation risk is generated by automatically analyzing related data, so that the time cost of manual acquisition and statistics of nurses is greatly reduced, and the assessment efficiency of the VTE generation risk is improved.

1. The name of the design product: data identification collection graphical user interface of electronic equipment. 2. The use of the design product: an electronic device. 3. The design points of the design product: the interface content of the graphical user interface in the screen. 4. The picture or photo that best indicates the design points: interface change state diagram 3. 5. The electronic equipment is a conventional design, and the rear view, left view, right view, top view and bottom view are omitted. 6. The use of the graphical user interface: the graphical user interface is used for the management, control and operation of scientific research clinical data identification collection. The front view is the display interface of the home page, the "OCR" icon option on the right side of the "blood routine" below the "screening period" on the upper left side of the middle part of the front view is clicked, and the interface change state diagram 1 is entered; the "shooting" option in the interface change state diagram 1 is clicked, and the interface change state diagram 2 is entered; the right side recognition result table row data in the interface change state diagram 2 (such as clicking the second row data on the right side) is clicked, the real-time annotation recognition reference data position in the left side diagram is marked, the interface change state diagram 3 is entered, the "mark as" option on the right side of the "sampling time" in the interface change state diagram 3 is clicked, all the recognized time data in the picture can be automatically marked as the corresponding date field of the laboratory examination form, the interface change state diagram 4 is entered, the "confirm" option at the bottom of the interface change state diagram 4 is clicked, and the interface change state diagram 5 is entered.

A system for an automated medical data processing based on patient-related data, including a processor of a healthcare processing server node configured to host at least Artificial Intelligence (AI) and machine learning (ML) modules and connected to at least one medical records cloud-based database and to at least one patient entity node over a network; and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: acquire user account creation and input data from at least one patient entity node using an OCR module; analyze patient intake data derived from the input data by an AI module configured to analyze the intake data; process user insurance data by an insurance AI module configured to generate an insurance verification verdict; acquire recommended lab test and triage data of the user and ingest the lab test and the triage data into an AI module configured to analyze the lab tests; receive treatment and medication suggestions and generate a feature vector based on the treatment and medication suggestions; provide the feature vector to an ML module configured to generate at least one clinical outcome model; derive clinical documentation data from the patient intake data and from the at least one medical records cloud-based database and apply NPL processing to the clinical documentation data; and acquire revenue cycle data from the clinical documentation data and ingest the revenue cycle data into an AI module configured to generate billing parameters.

The utility model relates to a pierceable artery blood sampling device suitable for being connected with a disposable blood sampling needle, which aims to solve the technical problems that the existing blood sampling device needs to be used for one test and examination of a patient but needs to be punctured twice and the operation is complicated, and adopts the technical scheme that the pierceable artery blood sampling device comprises a tail needle joint, a hose, a puncture needle head, a needle cylinder, a tail needle and a rubber sleeve, the tail needle connector is connected with the hose, the hose is connected with the puncture needle head, and the tail needle is arranged at the tail of the connector and sleeved with the rubber sleeve. The sealing cap comprises a shell, a rubber plug and a connector, a supporting base is arranged on the lower middle portion of the inner side wall of the shell, an edge is arranged on the outer circumference of the rubber plug, the rubber plug is arranged in the shell through matching of the edge and the supporting base, the annular columnar connector is arranged in the middle of the bottom of the shell and connected with the head of the needle cylinder in a clamped mode, and the tail needle stretches into and penetrates through the rubber plug from the upper end of the sealing cap. The needle cylinder extends into the needle cylinder. The original operation of pricking two needles can be completed by only one needle, so that the pain of a patient is relieved, and the workload of a nurse is reduced.

The invention discloses a visual multi-time-point hypertension risk scoring system. The system comprises a patient baseline data parameter and daily health information acquisition module, a project parameter acquisition module, a scoring module, a survival rate calculation module, a graphic display module and a project parameter correction module. The patient baseline data parameter and daily health information acquisition module is used for acquiring general health information, clinical text data, laboratory examination, sleep examination, blood pressure and heart rate of each patient; the project parameter acquisition module is used for acquiring parameters of a to-be-scored project; the scoring module is used for calculating a total risk score of the hypertensive patient; the survival rate calculation module is used for calculating the occurrence risk probability value of the specific adverse cardiovascular event according to the total risk score; the graphic display module is used for displaying the occurrence risk probability of the adverse cardiovascular event and the proportion weight of related risk factors; and the item parameter correction module is used for enabling the user to manually correct the parameter value of the scoring item.

The invention provides a system and method for consolidated patient data reporting in healthcare facilities utilizing artificial intelligence for decline and terminal status assessment. The system includes a data input module configured to receive patient data from multiple healthcare providers across different facilities in various formats, and a processing module for processing and amalgamating stored patient data. An artificial intelligence module utilizes optical character recognition, natural language processing, and specialized algorithms to scan unstructured medical documents and extract pertinent patient data focused on deterioration markers including weight trends, laboratory results, symptom patterns, functional changes, and healthcare utilization patterns. A reporting module generates consolidated patient data reports providing real-time updates and summaries highlighting significant decline trends and terminal indicators. The system includes modal-based human-in-the-loop validation, comprehensive HIPAA compliance measures, and tiered access control for enhanced security and data integrity.

Methods of providing a prognosis for a patient who has been treated for ST-segment-elevation myocardial infarction (STEMI) are provided The methods comprise: receiving the values of a plurality of predetermined features associated with the patient, the predetermined features comprising: one or more patient demographic features, one or more hospital admission history features, one or more clinical history features, one or more vital signs features and / or one or more laboratory tests features; and predicting, using the values of said plurality of features, a prognosis for the patient, wherein said predicting comprises using one or more machine learning models to predict a risk of the patient experiencing one or more respective post-treatment complications.

The invention discloses an intelligent diagnosis system and method for multiple myeloma based on multi-modal data fusion, and relates to the technical field of myeloma. Comprising a medical professional module, a digital cloned construction module, a continuous optimization module, an interactive service module, a service requirement module and a basic service module, wherein the medical professional module is responsible for providing professional knowledge support related to multiple myeloma, including clinical manifestations, laboratory examination, diagnostic standards, prognosis layering and therapeutic schedules. According to the intelligent diagnosis system and method for multiple myeloma based on multi-modal data fusion, multi-channel medical knowledge can be acquired and integrated in real time, standardized and structured professional knowledge support is provided through systematic knowledge graph construction and algorithm reasoning, meanwhile, multi-source heterogeneous medical data are comprehensively integrated, and the diagnosis efficiency is improved. And constructing a full-life-cycle data file of the patient so as to provide personalized diagnosis standards and treatment schemes in combination with personal information, medical history and multi-dimensional examination results of the patient.

This invention discloses a treatment pathway recommendation method based on IBS multidimensional data. This invention relates to the field of intelligent diagnosis and treatment technology, and includes the following steps: S1, constructing an IBS treatment plan library. The treatment plan library contains standardized treatment plans corresponding to different diagnostic types and severity levels of IBS. The standardized treatment plan includes at least follow-up recommendations, medication recommendations, probiotic recommendations, and nursing guidance, and each treatment plan is associated with a corresponding IBS multidimensional data feature threshold. This treatment pathway recommendation method based on IBS multidimensional data overcomes the limitations of existing technologies with single-dimensional diagnostic data. It comprehensively integrates four core dimensions: patient symptom data, physical sign data, laboratory test data, and medical history data. Symptom data is obtained through a combination of patient self-reporting and doctor consultation to ensure no omissions; physical sign data relies on professional medical equipment to ensure objectivity; and laboratory test data is obtained synchronously from the hospital's testing system to reduce errors.

The invention relates to the technical field of medical information, in particular to an intelligent diagnosis auxiliary analysis system based on medical data driving. The system comprises a multi-source medical data acquisition module, a dynamic feature engineering analysis module, a medical feature screening module and a diagnosis auxiliary analysis decision module, and can acquire medical image data, medical laboratory examination data and medical gene reaction data and perform missing difference complementation and time sequence alignment. Obtaining a multi-source medical time sequence driving data set; performing dynamic feature engineering analysis and feature offset contribution screening on the multi-source medical time sequence driving data set to obtain a multi-source medical important contribution feature set; and constructing a corresponding medical drive diagnosis analysis model based on the graph neural network and Transform in a mixed manner to carry out diagnosis reasoning prediction so as to output a diagnosis probability corresponding to the medical data, and carrying out decision analysis according to the diagnosis probability to obtain a corresponding medical diagnosis suggestion scheme. The accuracy of medical diagnosis can be improved.

The invention discloses an intelligent pre-warning method and system for interventional postoperative complications based on multi-source data fusion, and relates to the technical field of risk assessment, and the method comprises the steps: carrying out the model training based on a historical data set, and obtaining an interventional postoperative complication risk assessment model; the characteristic value data set comprises vital sign data, laboratory examination data, intraoperative parameter data and instrument parameter data; performing standardization processing on the characteristic value data set to obtain a characteristic value matrix; and inputting the eigenvalue matrix into a risk assessment model for risk prediction to obtain a complication risk prediction result. The problems that intervention postoperative complication assessment depends on artificial experience, efficiency is low and subjectivity is high are solved, the technical problems that multi-source heterogeneous data are difficult to process and real-time risk prediction is insufficient are avoided, postoperative risk intelligent prediction, quantitative assessment and interpretable report generation based on multi-source data fusion are achieved, clinical decision is assisted, and the risk assessment efficiency is improved. And the prediction accuracy and the management efficiency are improved.

The present application belongs to the field of pneumonia prognosis prediction, and in particular to a severe pneumonia post-pulmonary fibrosis prognosis prediction method and system based on multi-modal artificial intelligence. The present application constructs a database for storing clinical medical record texts, laboratory examination results and image CT data. Data acquisition is realized by connecting with a hospital information system, data preprocessing is carried out by using natural language processing, standardization processing and feature extraction, important features are selected after feature integration, a model is constructed and trained by selecting a suitable algorithm, and the model is verified and evaluated. Finally, the prediction results are integrated into a clinical decision support system, a personalized prediction report is generated and associated with clinical data, and a friendly front-end interface is provided for doctors to query, thereby providing a more accurate, efficient and clinically valuable solution for severe pneumonia post-pulmonary fibrosis prognosis prediction.

The invention relates to the field of Crohn's disease analysis, and discloses a multi-center research method for evaluating Crohn's disease activity, which comprises the following steps: firstly, carrying out radiomics model multi-region focus segmentation, feature extraction, feature selection, model establishment, multi-modal clinical construction, hybrid model construction, model evaluation and comparison and a statistical method; according to the method, a clinical prediction model is constructed by adopting a machine learning algorithm based on demographic characteristics, clinical manifestation, laboratory examination and image examination data of a CD patient; image omics features are extracted from intestinal wall and mesenteric fat areas of a CT small intestine angiography CTE image, clinical and image omics features are fused, and a plurality of machine learning models are constructed to evaluate the disease activity of a CD patient; the problems that in the prior art, a treatment scheme cannot be systematically formulated for a patient suffering from the Crohn disease, the disease progress cannot be monitored, the application vacancy of medical imaging and artificial intelligence in the field of inflammatory intestinal diseases exists, and the application effect is not ideal are solved.

The application relates to the technical field of disease prediction, in particular to a digestive system severe disease early warning detection method, device and equipment, wherein the method comprises the following steps: acquiring patient multi-modal data information, wherein the patient multi-modal data information comprises clinical symptoms, laboratory examination and imaging examination; a clustering model is constructed and trained to obtain a plurality of clustering sets; the patient multi-modal data information is input into a target clustering set in the clustering model; whether the patient multi-modal data is abnormal is judged; if the patient multi-modal data information is abnormal, a probability model is constructed; the target clustering set is generated to generate a severity label and is input into the probability model to obtain a probability result of severe disease severity evaluation; hierarchical early warning is carried out according to the probability result; and clinical decision is made according to the hierarchical early warning. Therefore, the problems that a same method cannot be used to evaluate a plurality of digestive system diseases, the evaluation results of diseases are different, and diseases are not timely evaluated in the prior art are solved.

The invention discloses a real world data-based personalized follow-up visit strategy generation and optimization method for patients with type 2 diabetes mellitus, which comprises the following steps of: calculating a patient state transition probability by utilizing a Markov decision process model through integrating demographic characteristics, a baseline health state, a laboratory examination result and life style data; and further, an individual follow-up visit frequency and a follow-up visit mode are dynamically generated. When a patient's clinical event or health status changes, the method may adjust the follow-up plan in real time. Compared with an existing standardized follow-up visit method, the method has remarkable advantages in the aspects of reducing the occurrence rate of complications, prolonging the health life and reducing the medical cost, is particularly suitable for basic medical institutions and resource-limited environments, and has universality and popularization value.

The invention relates to the technical field of disease prediction, in particular to an early warning detection method, device and equipment for severe diseases of a digestive system, and the method comprises the steps: obtaining multi-modal data information of a patient, the multi-modal data information of the patient comprises clinical symptoms, laboratory examination and iconography examination; constructing and training a clustering model to obtain a plurality of clustering sets, inputting the multi-modal data information of the patient into a target clustering set in the clustering model, judging whether the multi-modal data information of the patient is abnormal or not, if the multi-modal data information of the patient is abnormal, constructing a probability model, and if the multi-modal data information of the patient is abnormal, determining whether the multi-modal data information of the patient is abnormal; generating a severity label from the target cluster set, and inputting the severity label into a probability model to obtain a probability result of severity assessment of the severe disease; and performing graded early warning according to a probability result, and performing clinical decision making according to the graded early warning. Therefore, the problems that in the prior art, various digestive system diseases cannot be evaluated by using the same method, and results are different and not timely when the diseases are evaluated are solved.

The invention discloses a colposcope image processing algorithm based on multi-modal feature fusion. The algorithm comprises the following steps: data acquisition: acquiring a colposcope image acquired by medical equipment; labeling the collected colposcope image, wherein the collected colposcope image comprises a marker of a lesion area and an image category; data preprocessing: carrying out image preprocessing on the colposcope image, carrying out preprocessing on text data, and aligning multi-modal data; dividing the preprocessed data into a training set and a test set; establishing and training a neural network model, wherein the model is used for performing feature extraction and feature fusion to obtain a colposcope image after multi-modal feature fusion; the feature fusion comprises the step of fusing the extracted image features, text features, physiological signal features and laboratory examination result features. Richer and more comprehensive input is provided for the model, so that the diagnosis performance is remarkably improved.

The invention relates to the field of medical artificial intelligence, in particular to a multi-modal deep learning fused early-stage intelligent screening system for rheumatic diseases, which comprises an intelligent question and answer module, a knowledge graph module, an auxiliary diagnosis module and a hierarchical diagnosis module, innovatively introduces an algebraic topology theory, and represents medical knowledge as a pure complex structure, so that the medical knowledge can be quickly and accurately screened; weak signals in the early stage of diseases are captured through the continuous homology feature extraction technology, clinical phenotypes, laboratory examination and iconography information are integrated through a spectrum topology fusion network, the system adopts a multi-head topology perception attention mechanism, contributions of different modes are dynamically balanced, early accurate screening and clinical verification display of rheumatism are achieved, and the system has a wide application prospect. The system can identify the early performance of rheumatism six months in advance, the diagnosis accuracy reaches 85%, reliable decision support is provided for early intervention, and the system is particularly suitable for primary medical screening, specialized auxiliary diagnosis and multi-center clinical research.

The invention discloses a community and pension institution-oriented individualized medication guidance system for old people, and the system comprises a data collection module which is used for obtaining basic information, vital signs, clinical examination and medication data of the old people; the elderly clinical reasonable medication knowledge base module is used for analyzing the use signs, medication routes and doses of the elderly medicines, and formulating medication rules of various diseases in combination with clinical examination information; the medication evaluation module is used for generating an individualized medication evaluation result and executing interception or prompt operation; and the real-time interaction module is used for generating an individualized medication report based on the real-time medication condition, vital signs and laboratory examination data, and the doctor terminal / customer service terminal dynamically performs medication guidance in real time. According to the individualized medication guidance system for the old people for the communities and the pension institutions, real-time monitoring and adjustment of multiple medication of the old people are achieved, and medical risks and social consumption caused by multiple medication, unreasonable medication, poor medication compliance and the like of the old people are solved.

The application provides a differential model based on CT enteroclysis imageomics and clinical features, and relates to the technical field of medical treatment.The differential model comprises the data of clinical information, laboratory examination, image examination, enteroscopy and pathological examination, extracts the imageomics of intestinal wall and mesenteric fat characteristics based on CT enteroclysis, and constructs a differential diagnosis model of Crohn's disease and intestinal tuberculosis through machine learning.The differential diagnosis model of Crohn's disease and intestinal tuberculosis is constructed by integrating the clinical features and the imageomics parameters of CT enteroclysis, and is significantly better than a single model.The imageomics characteristics reveal the essential differences of the two diseases in the texture of intestinal wall and mesenteric fat.The nomogram constructed by the application provides a convenient visual tool for the clinic, and is particularly suitable for precise diagnosis in tuberculosis high-incidence areas.

The invention provides a death risk prediction model and system established based on Transform, which can simultaneously capture complex relationships among multiple feature dimensions in clinical data through a multi-head attention mechanism (Multi-Head Attention), and is suitable for processing structured and unstructured high-dimensional clinical information, such as inspection indexes, medical record texts, medication records and the like. Compared with traditional RNN, GRU and other sequence models, the coding type Transform model does not depend on a recursive structure, is based on a global self-attention mechanism (Self-Attention), can effectively capture long-term dependence, and is especially suitable for time sequence data of patients, such as vital signs during hospitalization, laboratory examination and other dynamic change processes.

The invention discloses an automatic filing management system for electronic medical records, and belongs to the technical field of electronic medical records. The system comprises an information acquisition module, a filing service processing module and a unified document conversion module, the information acquisition module is used for acquiring electronic medical record data of various medical information systems; the various medical information systems comprise an electronic case EMR system, an LIS laboratory examination LIS system, an image archiving and communication PACS system and a hospital information system HIS. The unified document conversion module is used for performing format conversion on the acquired data and outputting the data subjected to format conversion into a structured database; and the archiving service processing module is used for establishing an electronic medical record index database and is used for uploading, downloading and storing and managing archived medical records. The medical record data stored across various systems can be archived and managed, an information island is easily broken through, big data fusion is realized, and the medical record data is independently extracted, archived and stored.

The invention relates to a traditional Chinese medicine and western medicine combined intelligent complete-cycle comprehensive management platform for osteoporosis, and aims to solve the problems that screening is not systematic, diagnosis is not comprehensive, treatment individualization is insufficient, follow-up visit is not standard and data is dispersed in existing osteoporosis management. The platform comprises a data capturing module, a risk assessment module, an examination suggestion pushing module, a diagnosis integration module, a treatment scheme generation module, a follow-up visit management module and a data management module, and can be in butt joint with a hospital HIS system to capture patient data, grade patient risks through built-in IOF one-minute risk assessment questions, and push bone mineral density and laboratory examination suggestions; western medicine examination results and traditional Chinese medicine syndrome differentiation type clear diagnosis are integrated, and an individualized scheme containing basic treatment, anti-osteoporosis medicines, traditional Chinese medicines and exercise prescriptions is generated; the hospital discharge follow-up visit is realized by means of a WeChat applet and a Web terminal, and data of multiple hospitals are integrated to establish a special disease database. The whole-cycle intelligent management and control of osteoporosis are realized, and the diagnosis and treatment efficiency and standardization are improved.