39 results about "Laboratory examination" patented technology

The invention relates to the technical field of medical diagnosis processing, and discloses an intelligent disease diagnosis and differential diagnosis system based on a knowledge graph, and the system comprises a data input module which is used for receiving and standardizing clinical symptoms, signs, laboratory examination data and historical medical record data of a patient; the knowledge graph construction and updating module is used for constructing and updating a knowledge graph of diseases and symptoms according to the medical literature and the clinical data, and the knowledge graph automatically extracts an incidence relation between the symptoms and the diseases from the medical literature through a natural language processing technology; and the reasoning and diagnosis module is used for performing intelligent disease diagnosis and differential diagnosis. According to the method, by optimizing reasoning path selection and information gain calculation, the path with the most information content is selected from multiple reasoning paths for diagnosis reasoning, and the most representative path is selected by calculating the correlation degree between each symptom and the disease.

The invention, which relates to the technical field of the medical platform, discloses a medical aid decision-making system based on a multi-modal large model, comprising an electronic medical record, a medical image and a laboratory examination result. Carrying out the preprocessing of the collected multi-modal medical data, and obtaining the preprocessed multi-modal medical data; the data processing module is further used for preprocessing the preprocessed multi-modal medical data, comparing the preprocessed multi-modal medical data with an existing medical knowledge base, verifying the reasonability of the data, evaluating the data, and deleting the non-conforming multi-modal medical data to obtain the evaluated multi-modal medical data; the problem that the generalization ability of a multi-modal large model in the medical field is limited is solved through the decision reasoning module, rich background knowledge is provided for the model by means of construction and distillation of a medical knowledge graph, the understanding and processing ability of the model for different tasks is improved, and the system has good application prospects. And the problem of insufficient reliability of the multi-modal large model in actual clinical application is solved through the verification module.

The invention provides a therapeutic drug monitoring kit and method and system, a magnetic bead extraction method is combined with LC-MS / MS detection to be applied to human blood drug concentration monitoring, a mixed magnetic bead solution containing phospholipid removal magnetic beads and to-be-detected drug adsorption magnetic beads is utilized to perform adsorption and extraction pretreatment on a to-be-detected drug in a sample, automatic treatment equipment is matched, and the concentration of the to-be-detected drug in the to-be-detected drug in the to-be-detected drug in the to-be-detected drug in the to-be-detected drug in the to-be-detected drug in the to-be-detected drug is detected. The pretreatment efficiency is high, the phospholipid content in the supernate is low, and the matrix effect and the chromatographic column washing pressure are greatly reduced. According to the therapeutic drug monitoring method provided by the invention, a unified pretreatment process is used, simultaneous extraction of multiple drugs is realized, and through a magnetic bead automatic extraction method, the pretreatment step of a sample can be rapidly completed, a therapeutic drug monitoring result can be rapidly obtained, the overall detection time is shortened, and the detection efficiency is improved. Therefore, a reliable laboratory examination basis is provided for individualized treatment.

The invention relates to the technical field of data information processing, in particular to a multi-modal medical database construction method and system, and the system comprises a data collection module, a data preprocessing module, a data storage module, and a multi-modal model construction module. According to the method, the deep learning diagnosis model based on multi-modal data fusion is constructed, so that accurate early warning of disease progress key nodes is realized; the model integrates multi-modal data such as clinical indexes, laboratory examination and iconography data, processing is performed through methods such as feature extraction, standardization and normalization, key information in each modal data is extracted, disease development rules and patient individual differences are captured, and high-risk patients are identified; particularly, the deep learning model is utilized to process and predict multi-time-point data, so that the timeliness and accuracy of the model are improved, early recognition and timely intervention are realized, and a better treatment opportunity is provided for a patient.

A digital healthcare ecosystem to transform patient care with real-time queue management, including: an appointment and laboratory test booking module configured to allow patients to easily book and schedule doctor consultations and diagnostic tests; a healthcare provider module configured to manage appointments, patient records, billing, internal laboratory and pharmacy integration, and operational analytics, with the healthcare provider module configured to manage dashboard features for physicians, hospital administrators, and laboratory technicians to monitor and update patient status; a real-time queue management module coupled to the healthcare provider module and the appointment and laboratory test booking module, wherein the real-time queue management module is configured to assign a digital token number to patients upon booking once accepted by the healthcare provider module, wherein the real-time queue management module is configured to update the queue position of patients using time-stamped data; a patient interface configured to allow patients to view real-time queue updates, book appointments, and access lab reports using the digital token number; a notification system configured to send notifications to patients and physicians regarding appointment reminders, queue progress, and follow-up instructions; a cloud-based server configured to securely store patient data and enable seamless communication between the patient interface and the healthcare provider module; a display unit configured to show estimated waiting times and notify patients when it is their turn; and a dynamic scheduling module configured to take into account physician availability, patient preferences, and historical data to minimize waiting times and avoid overbooking.

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 belongs to the technical field of medical data processing, and discloses a craniocerebral injury delayed deterioration prediction method and a related device, and the method comprises the steps: obtaining clinical data of a to-be-predicted patient; the clinical data of the to-be-predicted patient comprises basic clinical data, medical image data and laboratory examination data of the to-be-predicted patient; preprocessing the clinical data of the to-be-predicted patient to obtain clinical baseline features of the to-be-predicted patient; inputting the clinical baseline features of the patient to be predicted into the craniocerebral injury delayed deterioration prediction model to obtain a craniocerebral injury delayed deterioration prediction result of the patient to be predicted; the craniocerebral injury delayed deterioration prediction model is a dichotomy machine learning model under a predetermined outcome event, and the predetermined outcome event is determined according to a preset quantitative index of a craniocerebral ROI region; according to the method, the accuracy of the prediction result of the delayed deterioration of the craniocerebral injury is remarkably improved, the secondary injury risk of a patient with the craniocerebral injury is more accurately predicted, and an effective prognosis evaluation tool is provided for clinic.

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. Name of the design product: Computer with a graphical user interface for traditional Chinese medicine diagnosis guidelines. 2. Use of the design product: For computers. 3. Design key points of the design product: Lies in the content of the user graphical interface. 4. Picture or photo that best shows the design key points: The front view of Design 1. 5. The rear view, left view, right view, top view, and bottom view of Design 2 are the same as those of Design 1, so the rear view, left view, right view, top view, and bottom view are omitted. 6. Design 1 is designated as the basic design. 7. Use of the graphical user interface: The user interface in the front view of Design 1 that reflects the unique clinical syndrome differentiation ideas of experts is divided into two modules: "overall identification" and "disease differentiation"; Interface change state diagram 1 of Design 1 is the interface that appears after clicking "Next" in the front view of Design 1; Interface change state diagram 2 of Design 1 is the interface that appears after clicking "Next" in Interface change state diagram 1 of Design 1; The front view of Design 2 is the interface displayed after clicking the "disease differentiation" option, reflecting the unique clinical syndrome differentiation ideas of experts; Interface change state diagram 1 of Design 2 is the interface displayed after selecting the "diabetes-related diseases" option in the front view of Design 2; Interface change state diagram 2 of Design 2 is the interface displayed after operating on the "diabetes-related diseases" in Interface change state diagram 1 of Design 2, used for symptom collection and selection under the unique clinical syndrome differentiation ideas of experts; Interface change state diagram 3 of Design 2 is the interface displayed after operating on "laboratory examination - elevated blood glucose", "dietary abnormality - excessive hunger", and "drinking condition - thirst" in Interface change state diagram 2 of Design 2, used for symptom collection; Interface change state diagram 4 of Design 2 is the interface displayed in the medical record section of Interface change state diagram 3 of Design 2 after entering "blurred vision", "general heaviness", "frequent urination", and "weight loss", used to complete the medical record; Interface change state diagram 5 of Design 2 is the interface displayed after operating on the "recommended prescription" in Interface change state diagram 4 of Design 2, used for prescription selection.

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 invention discloses a method and a system for predicting NASH advanced hepatic fibrosis. The method comprises the following steps: collecting laboratory examination characteristics of an NASH patient; for each laboratory inspection feature, the SHAP value of the laboratory inspection feature is calculated, and the laboratory inspection features are sorted according to the SHAP values; a plurality of laboratory inspection features are selected in sequence to serve as sample data to construct an XGBoost model so as to predict NASH advanced hepatic fibrosis; wherein the screened laboratory inspection features comprise TG, ALB, INR and HDL. According to the invention, NASH can be effectively identified and intervened.

A medical paperwork delivery system is provided. The system is designed to facilitate the digitalization and transmission of medical paperwork, such as lab work, x-rays, and physical therapy orders. The system comprises at least one digital scanning device, which can be any device capable of converting paper documents into digital form, including but not limited to mobile devices with scanning applications and various types of scanners. After scanning the documents, a communication interface allows a user to select and send the digital document to another user, such as a medical lab or building. Additionally, the system enables a third party, like a patient, to access the document either through a mobile application or via other delivery means like text messages or emails. This accessibility allows the document to be printed or accessed digitally, facilitating a streamlined process for managing medical paperwork.

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.

An exemplary healthcare services recommender implementation may be configured to recommend selectively redeemable bundled services comprising care for conditions predicted as likely by a machine learning model based on patient data input. The likely conditions may be predicted for a patient or a patient population comprising a plurality of patients. The machine learning model may comprise a neural network trained to determine a probability distribution of patient condition trends for a future time period based on patient data input. The patient data input may comprise data from an Electronic Medical Record (EMR), diagnosis, lab test, patient-provided information such as survey / symptom data, and / or wearable device data. The machine learning model may be trained using a training data set including at least healthcare outcomes sampled during a past time period from a plurality of patient healthcare episodes each comprising a plurality of procedures performed for the patient in at least one encounter.

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 automatic hospital guide, and discloses an automatic hospital guide system and method for laboratory examination, and the method comprises the steps: building a symptom-symptom association rule and department diagnosis and treatment range mapping, and endowing a weight to the typicality of the symptom according to the symptom; converting the patient self-description into standard medical terms, and generating a symptom set; comparing the coincidence degrees of the patient symptoms and the disease database, if the patient symptoms are completely matched with the disease database, triggering a primary selection strategy, and directly locking candidate diseases; if not, executing a maximum matching strategy, and triggering inquiry verification in a descending order according to the symptom overlap ratio; inquiring unmatched symptoms of the candidate diseases, and confirming or eliminating the diseases according to patient feedback; and preferentially recommending departments covering compound diseases, and performing parallel recommendation when multiple departments are matched. The system continuously optimizes the model through patient feedback to form a closed-loop learning mechanism. According to the method, knowledge base quantitative analysis and dynamic inquiry verification are fused, and a patient is helped to be guided to complete laboratory examination in a correct diagnosis and treatment department.

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 invention relates to the technical field of data analysis, and discloses a patient data intelligent analysis method and device based on CCE data, and the method comprises the steps: inputting the multi-modal data of a patient into a feature embedding layer of a trained multi-modal classification early warning model for feature conversion, and obtaining feature embedded data; the multi-modal data comprises text data, physiological data, laboratory examination data and CEC data; inputting the feature embedded data into a feature fusion layer of a multi-modal classification early warning model for cross-modal feature fusion to obtain feature fusion data; inputting the feature fusion data into a feature extraction layer of a multi-modal classification early warning model for feature extraction to obtain a feature extraction result; and inputting the feature extraction result into a classification layer of the multi-modal classification early warning model for classification prediction to obtain a disease early warning result about the target disease. Therefore, the analysis accuracy of the patient data can be improved, so that the early warning accuracy of the disease of the patient is improved.

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 clinical data standardized fusion and comprehensive information model construction system, which comprises an infectious lung disease clinical queue data acquisition system used for establishing a unified data acquisition system and standard based on a prospective infectious lung disease special disease research queue of a cooperative unit, clinical indexes, laboratory examination data, iconography data and treatment response multi-dimensional data are collected and integrated, and the data are collected, processed and stored. According to the clinical data standardized fusion and comprehensive information model construction system, by integrating multi-modal data and constructing an advanced model, the death rate of infectious lung disease patients, the ICU admission rate, the breathing machine requirement and the like can be more accurately predicted, and accurate AI auxiliary decision support is provided for critical disease treatment.