831 results about "Diagnosis laboratory" patented technology

The invention discloses a laboratory analytical instrument automatic calibration system based on artificial intelligence, and belongs to the technical field of instrument automatic calibration. Comprising the following modules: an intelligent hardware interaction module for realizing plug-and-play of different instruments and accurate conveying of standard substances; the instrument digital twinning module automatically identifies the characteristics of the instrument and simulates the behaviors of the instrument; the multi-mode AI calibration module is used for optimizing calibration parameters and adjusting the calibration process in real time; the environment intelligent compensation module calculates and generates a compensation coefficient in real time, and eliminates the influence of environmental factors on a calibration result; the instrument health management module is used for evaluating the health state of the instrument and predicting the service life of a core component, and realizing conversion from passive maintenance to predictive maintenance; the calibration knowledge graph module is used for realizing automatic extraction, reasoning and application of calibration knowledge through a graph neural network and continuously optimizing a calibration strategy; and the intelligent scheduling and management module is used for generating an optimal calibration plan, coordinating the work of each module and realizing reasonable distribution and efficient utilization of calibration resources.

The invention discloses a laboratory automatic process management and multi-source data fusion system based on the Internet of Things, and belongs to the technical field of laboratory management. The system comprises a sensing layer, an analysis layer, an intelligent management layer and an application layer. The sensing layer carries out global data acquisition and real-time analysis; the analysis layer integrates data, mines an association relationship and optimizes task matching; the intelligent management layer performs full-process automatic management and control on a laboratory, and comprises an equipment cooperation module, a dynamic detection module, a dynamic configuration module, a real-time simulation module, a preset trigger module and an intelligent auditing module; and the application layer performs multi-terminal collaborative interaction and intelligent assistant assistance processing. According to the system, intelligent collaboration of laboratory equipment, automatic detection of samples, dynamic optimization of equipment parameters and automatic early warning and processing of abnormal conditions are realized through technical means such as knowledge graph construction, a digital twinborn technology and multi-mode biological recognition, and the laboratory management efficiency and the data processing accuracy are effectively improved.

The invention relates to the technical field of intelligent medical treatment, and discloses an AI-based severe patient sepsis early warning method and system. According to the method, a multi-organ interaction mechanism is deeply analyzed by dynamically constructing an organ-level causal network, and early-stage accurate early warning of sepsis is realized: high-frequency physiological waveforms, asynchronous laboratory indexes and treatment intervention data are fused, and the capture ability of microcirculation failure and autonomic nerve decline is improved; the early warning threshold value is dynamically adjusted based on the treatment coverage degree, and the delay and false alarm defects of a fixed threshold value mechanism are effectively overcome; a pathogen targeted therapy map and an organ function support scheme are automatically generated by a graded triggered clinical action chain, and the clinical decision response time is shortened; according to the method, the risk of missed diagnosis is reduced while the early warning sensitivity is improved, and an earlier and more reliable intervention window is provided for critical patients.

The invention discloses a multi-agent large-model disease diagnosis knowledge reasoning system based on data dual drive, and relates to the technical field of artificial intelligence assisted medical diagnosis. The system collects patient symptom follow-up records, laboratory test results, observation diagnosis probabilities and expert diagnosis recommendation results in a multi-source manner; time sequence evolution characteristics are extracted, a time sequence diagnosis sensitivity coefficient is calculated, and early recognition of disease risks is achieved; in combination with anti-fact simulation and statistical reasoning, a causal consistency coefficient is obtained and is used for verifying causal reasonability of observation diagnosis and contrast results; based on agent group consensus analysis, calculating a game consistency coefficient for judging the credibility of a diagnosis conclusion; positioning and multi-level verification are carried out on abnormal reasoning steps and knowledge fragments, so that the reliability and safety of a result are guaranteed; continuous optimization of the diagnosis model is realized through a log analysis and knowledge backflow mechanism; according to the invention, the accuracy, interpretability and safety of disease diagnosis can be obviously improved.

The invention relates to a renal transplantation post-operation rejection risk monitoring and early warning method and a renal transplantation post-operation rejection risk monitoring and early warning system. The method comprises the steps of collecting recipient nursing monitoring data, laboratory indexes and transplanted kidney ultrasonic blood flow parameters in a follow-up visit period, performing timestamp alignment, deletion processing and standardization on multi-source data, extracting features to construct a time sequence feature sequence, inputting the time sequence feature sequence into a pre-training risk prediction model, outputting the rejection reaction occurrence probability of the next period, and forming a risk trend. Calculating a nursing sensitive index contribution weight based on the model contribution information, and screening a target nursing monitoring index; and establishing an individualized baseline model to obtain a baseline value and an allowable fluctuation interval, extracting characteristics such as deviation amplitude, direction, rate, fluctuation and continuous deviation duration and the like, and performing individualized calibration on the occurrence probability to obtain a calibration risk score. When the threshold value is not reached and the trend is not triggered, generating a nursing monitoring suggestion of the next period; and pushing early warning and generating grading intervention suggestions when a threshold value is reached or a trend is triggered.

The invention belongs to the technical field of medical data processing and health assessment, and discloses a multi-source medical data-based risk prediction method, which comprises the steps of multi-source medical data acquisition, data preprocessing, feature selection, model training, risk prediction and model explanation. According to the method, by adopting a systematic multi-stage feature selection strategy, a specific key feature combination highly related to a specific medical event or disease can be screened out from multi-source heterogeneous medical data including clinical data, laboratory data, heart MRI (Magnetic Resonance Imaging) and the like; the screened feature subsets can be used for constructing an interpretable machine learning model, and through combination with SHAP and other model interpretation technologies, clinicians are helped to understand prediction logic, the credibility of results is enhanced, and more valuable reference information is provided for individualized clinical decision and intervention.

The invention discloses a measurement laboratory anomaly detection and diagnosis method, system and device based on deep learning and a medium, and relates to the technical field of anomaly detection and diagnos.The method comprises the steps that multi-source real-time data are collected and preprocessed; performing alignment processing based on sampling inconsistency among the data sources, and constructing unified data representation; generating a corresponding prediction result by using the prediction model; calculating a comprehensive abnormal score based on the aligned data and the prediction result; comparing the comprehensive abnormal score with a threshold value, and judging whether a comprehensive abnormal state exists or not; if the judgment result is abnormal, performing abnormal cause decoupling processing and causal inference to obtain a candidate root cause set; and inputting the candidate root cause set into a deep learning causal inference model to obtain an anomaly diagnosis result. A physical perception residual scoring mechanism is introduced, a comprehensive anomaly score is combined on the basis of anomaly detection, a weighted calculation method is adopted, and the contribution degree of each data source to an abnormal state can be accurately evaluated.

The invention discloses a pheromone binding protein derived peptide capable of effectively monitoring sex pheromones of fall webworms and a biosensor, and belongs to the field of biosensors. At present, methods such as sample plot survey and sex traps commonly used in fall webworm monitoring have the problems of poor timeliness, high labor cost, insufficient data continuity and the like. In order to solve the problem, the pheromone binding protein-derived peptide provided by the invention has an amino acid sequence as shown in SEQ ID NO: 2, the biosensor comprises a substrate and an interdigital electrode, a single-walled carbon nanotube is attached to the electrode, and the electrode is also connected with the pheromone binding protein-derived peptide. The sensor can specifically detect sex pheromones of fall webworms, has no response to 17 plant volatile matters, and can detect sex pheromones released by as low as five live female fall webworms under laboratory conditions. The method has application potential in the fields of early pest monitoring, prevention and control efficiency improvement, ecological safety guarantee and the like.

The invention relates to the technical field of heating, ventilation and air conditioning control, in particular to an intelligent laboratory air quality control system and method.The intelligent laboratory air quality control system comprises a data acquisition module, an intelligent decision module, a fault diagnosis module and an execution control module; meanwhile, feedback type multi-target collaborative operation parameters are generated through a plurality of heterogeneous strategy models; the fault diagnosis module analyzes equipment health data through an AI health degree baseline model, and outputs diagnosis information as a dynamic constraint; and the execution control module fuses the compensation control sequence, the cooperative parameters and the diagnosis information, generates a final physical control instruction verified by a safety boundary rule, and drives a heating ventilation air conditioner execution mechanism. According to the invention, through a control strategy integrating prediction, optimization and diagnosis, the stability of the laboratory environment, the operation economy and the long-term operation reliability of the system are significantly improved.

The invention provides a sepsis early-stage risk prediction system, and relates to the technical field of sepsis risk prediction, the sepsis early-stage risk prediction system comprises a data acquisition module, a data preprocessing module, a risk assessment module, a result display module and a data management module, and the data acquisition module is used for acquiring vital signs, laboratory data and medical record data through vital sign acquisition, laboratory data acquisition and medical record data acquisition; the data is collected from three aspects; the data preprocessing module can process the data and find features related to the early stage of sepsis from the collected data; the risk assessment module firstly scores, and determines whether machine prediction or expert prediction is performed according to the score level; and the result display module displays the evaluation result through visual display, and generates a report according to patient demands. The system has the advantages that multi-dimensional data such as vital signs of a patient, laboratory examination and electronic medical records are comprehensively collected through the data acquisition module, data islands are broken, and a rich information basis is provided for accurate prediction.

The invention relates to an old inpatient intestinal preparation failure risk prediction method and system, and the method comprises the steps: collecting the multi-dimensional electronic medical data of a patient from a hospital information system (HIS), a laboratory information system (LIS) and a pharmacy information system (PIS), carrying out the cleaning, missing value filling and standardization preprocessing of the original data, so as to construct a high-quality data set, and carrying out the prediction of the intestinal preparation failure risk of the old inpatient. Generating three core risk characteristics based on clinical knowledge: an age-weighted complication index, a specific drug use mark and a key physiological index anomaly mark; inputting the features into a preset rule engine, performing weighted calculation according to a fixed weight coefficient to obtain a comprehensive risk score, finally dividing patients into low, medium and high risk levels according to a risk threshold value determined by historical data, and outputting a result and storing the result into a database for clinical retrieval and early warning. The method can effectively evaluate the intestinal preparation failure risk of the elderly patient, and provides an objective basis for clinical intervention.

The invention relates to a laboratory safety detection method and device, equipment and a medium. The method comprises the steps that a networking retrieval module is adopted to select a tool for large-scale data retrieval; guiding an intelligent agent RAG module to screen and verify information by adopting a thinking chain technology; in the generation stage, a contrast decoding method is used, the confidence difference between the reference lexical elements and the illusion lexical elements is calculated, and confidence reweighting is conducted on the reference lexical elements and the illusion lexical elements so as to strengthen real safety information and inhibit model illusion content. And inputting the laboratory scene image, the user input text instruction, the structured comprehensive query instruction and the effective information of all rounds into a multi-mode large language model reinforced by a thinking chain technology to generate a laboratory safety detection report, and proactively providing an optimization suggestion of the laboratory safety layout based on the laboratory safety detection report. According to the invention, the reasoning capability of the model in a complex security scene is enhanced, and the accuracy and reliability of a detection result are improved.

The invention discloses a surface antigen gene StSAG1 of Sarcocystis tenella, the nucleotide sequence of the surface antigen gene StSAG1 is as shown in SEQ ID NO: 1, and according to the analysis result of protein antigenicity and antigen epitope, the 686 bp-1175 bp segment of the StSAG1 gene is selected to construct a recombinant protein expression vector and engineering bacteria; the antigen disclosed by the invention has very strong antigenicity and specificity, and can be subjected to effective antigen-antibody reaction with an antibody generated in serum after sheep (Ovis aries) is infected with sarcosporidium tenella, so that diagnosis is realized; low-titer antibodies can be detected two weeks after infection, and the detection window period is obviously advanced; by adopting the ELISA detection method, a visual result can be directly obtained. The method breaks through the limitation of an infection window period, has the advantages of low cost, high sensitivity, simplicity and convenience in operation, no need of expensive instruments and analysis and the like, is suitable for field detection in primary laboratories and pastures, and is beneficial to industrial production and market popularization and application.

Methods and systems are provided for optimizing the digital scanning of pathology slides in a transportable lab. A computing device-implemented method is described for receiving a plurality of pathology slides in the transportable lab, sorting the plurality of pathology slides based upon pathology slide condition to determine which of a plurality of scanners to utilize, and scanning at least one of the plurality of pathology slides utilizing Whole Slide Imaging or Whole Slide Imaging with Robotic Z-Stacking to generate a digital pathology slide. Transportable systems for scanning pathology slides, as described herein, include a triage stage for analyzing each of the pathology slides for digital scanning, a plurality of first slide imaging apparatuses for Whole Slide Imaging, and a plurality of second slide imaging apparatuses for Whole Slide Imaging with Robotic Z-Stacking.

The invention discloses a cerebral hemorrhage patient tracheotomy risk prediction method and system based on machine learning, and the method comprises the steps: obtaining an initial clinical data set of a target patient, calculating laboratory inspection data according to a predefined rule, and constructing a composite physiological state index to generate a feature vector for prediction; inputting the feature vector for prediction into a risk prediction model pre-trained based on an ensemble learning algorithm to obtain a risk quantitative index; the model interpretation module generates an individualized prediction contribution decomposition result based on an SHAP value calculation framework, and explains the specific influence of each feature on the risk index; and finally comprehensively generating a risk prediction report. According to the method, the feature representation and model prediction capability is enhanced by constructing the composite indexes, and meanwhile, the decision process is transparent and credible by utilizing interpretability analysis, so that clinical risk assessment and decision support are effectively assisted.

The invention relates to the technical field of biological and medical diagnosis, in particular to a biomarker for predicting whether a patient suffering from pyotoxic myocardial injury has a short-term death risk or not and application of the biomarker, and the biomarker is the ratio of erythrocyte distribution width to albumin. According to the method, retrospective queue research design is adopted, two data sets, namely MIMIC-IV and eICU-CRD, are used, suppurative myocardial injury patients meeting conditions are included, covariants such as baseline features, laboratory inspection results and complication information of the patients are collected, the patients are divided into two groups according to RAR medians, the relation between RAR and short-term death of the patients is analyzed by adopting multiple statistical methods, and the accuracy of the short-term death of the patients is improved. A risk prediction model of short-term death of the patient suffering from the pyotoxic myocardial injury is constructed, so that a clinician can identify the patient suffering from the pyotoxic myocardial injury with high death risk in an early stage, a personalized treatment strategy can be formulated more accurately, the survival rate of the patient suffering from the pyotoxic myocardial injury is increased, and the method has a wide application prospect.

The invention discloses a laboratory environment safety management system driven by calling of a chemical reagent, and relates to the technical field of data management, and the system comprises a monitoring data collection module which is used for collecting full-life-cycle monitoring data of the chemical reagent; the security management hierarchical library construction module is used for performing security analysis and constructing a security management hierarchical library; the target monitoring data acquisition module is used for acquiring target monitoring data; the chemical reagent calling data acquisition module is used for acquiring chemical reagent calling data; the environment safety risk coefficient identification module is used for identifying an environment safety risk coefficient; and the environment safety tracking management module is used for carrying out environment safety tracking management. The technical problem that the real-time performance and the accuracy of a safety risk prediction result are insufficient due to the fact that the full-life-cycle safety monitoring of a chemical reagent is insufficient and the environment risk is difficult to accurately evaluate in the prior art is solved, and the technical effect of improving the identification precision of the laboratory environment safety risk and the real-time performance of tracking management is achieved.

The invention relates to the technical field of liver disease prediction, in particular to a metabolism-related fatty liver disease intelligent prediction method and system and a storage medium. The method comprises the following steps: collecting multi-source data, respectively obtaining basic demographic information, laboratory indexes and prediction indexes, and extracting quantitative and qualitative tongue picture parameters; performing variable screening on the tongue picture parameters and the clinical indexes, and determining key prediction variables; obtaining a key variable value according to the key prediction variable, obtaining a prediction result of the occurrence risk of the metabolism-related fatty liver disease, and outputting the prediction result; the system comprises a multi-source data acquisition module, a variable screening module and a prediction result acquisition module. By means of the mode, the intelligent tongue picture parameters and the clinical indexes are fused, and the effect of early prediction of the metabolism-related fatty liver diseases is achieved.

The invention relates to an in-situ icicle collection and automatic layered cutting detection device used in a surface water freezing period, during working, a drilling cylinder rotates and descends to cut an ice body to drill icicles, a lifting mechanism lifts the icicles to a cutting station, and a cutting mechanism completes automatic layered cutting of the icicles through a cutting tool bit. The cut ice sample is conveyed to a detection position by the transfer mechanism, and the detection mechanism obtains physicochemical index data of the ice sample in real time through ice sample spraying and sensor analysis. The device integrates four functional modules of drilling, cutting, transferring and detecting, interference caused by sample transportation and laboratory treatment is avoided through a full-process in-situ operation mode, and primitiveness and accuracy of data are ensured. Mechanical disturbance and manual intervention are reduced through automatic operation, original structural characteristics of an ice layer are reserved, and working efficiency and sampling consistency are improved. The device provides reliable technical support for stratified collection of ice columns in the surface water freezing period and scientific research on physicochemical property change of pollutants in the freezing process.

The invention discloses a stomach cancer postoperative survival prediction method and system based on machine learning, and belongs to the technical field of medical worker crossing and medical worker combination. According to the technical scheme, the method comprises the following steps: acquiring clinical data of a gastric cancer patient, wherein the clinical data comprises demographic characteristics, tumor pathology characteristics, operation related parameters and laboratory detection indexes; filling missing values in the clinical data by using an iterative random forest missing value filling method based on mutual information weighting; on the basis of the filled data, a feature subset with the most information content for postoperative three-year survival prediction is screened out through a dual feature selection strategy; training a machine learning model by using the feature subset so as to predict the survival risk of the gastric cancer patient in three years after operation; and outputting a prediction result. The method has the beneficial effects that a plurality of key challenges from data preprocessing, feature engineering and model construction to interpretability and clinical application are systematically solved, and an accurate, reliable, transparent and practical gastric cancer postoperative survival prediction solution is finally formed.

The invention relates to the field of medical artificial intelligence, and discloses a CRRT data omnibearing acquisition and optimization processing system based on artificial intelligence, and the system comprises an acquisition calibration module which is used for acquiring CRRT equipment parameters, physiological signals and laboratory inspection data, forming multi-source data, and carrying out the time alignment and space calibration of the multi-source data, and obtaining calibration data; the modeling estimation module is used for constructing a space-time dynamic model for substance transportation and biochemical reaction in the CRRT treatment process based on a random partial differential equation, estimating a state variable in combination with calibration data, and generating high-dimensional state data; and the fusion reasoning module is used for carrying out dimensionality reduction and adversarial denoising processing on the high-dimensional state data. Through multi-source acquisition and space-time calibration of CRRT equipment parameters, physiological signals and laboratory data, a treatment data basis is constructed, space-time dynamics modeling of substance transportation and biochemical reaction is performed based on a random partial differential equation, and dynamic description of a treatment process is realized.

There is provided a method of classifying cancer, comprising: feeding an image of a histology slide of a cancer into a machine learning (ML) model, obtaining a score indicative of a probability of a positive status or a negative status of a marker from the ML model, accessing an indication of the positive status or the negative status of the marker obtained by a laboratory test, computing a threshold for determining whether the score generated by the ML model is discordant with respect to the indication according to the laboratory test, in response to the score being greater than a threshold and the negative status of the marker according to the laboratory test, classifying the cancer as a first category, and in response to the score being less than the threshold and the positive status of the marker according to the laboratory test, classifying the cancer as a second category.

The present invention relates to a method, device and medium for visual supervision of the entire process of hazardous chemicals in a laboratory, comprising: an applicant enters a laboratory hazardous chemical application and use system through a user terminal to apply for the use of chemicals, and an administrator processes the application form based on whether the chemicals applied for by the applicant are hazardous chemicals; when the applicant receives the hazardous chemicals and conducts a test, the application form enters a pending mode; after the test, the applicant submits a hazardous chemical waste liquid treatment registration on time according to the use requirements and stores the registration in an application form database; if the applicant fails to submit the hazardous chemical waste liquid treatment registration before the test ends, the administrator locks the account, and the applicant cannot unlock the hazardous chemicals next time; a laboratory waste liquid management personnel checks the hazardous chemical waste liquid submitted by the applicant through a laboratory hazardous chemical waste liquid early warning system, and detects whether there are safety hazards by visually displaying the type and quantity of the waste liquid.

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 relates to the technical field of clinical medical treatment, and discloses a disease deterioration risk identification method and system based on depth time sequence clustering enhancement, and the method comprises the steps: 1, obtaining multi-modal clinical sequence data of a patient, including physiological indexes of a time sequence, a laboratory detection result, historical diseases and medication data; 2, performing feature extraction and classification on the patient sequences by adopting a knowledge enhanced sequence clustering method, and grouping the patient sequences according to future outcome distribution of the patient sequences; 3, enabling the model to quickly adapt to a prediction task of a new patient subgroup through a meta-training process; and step 4, based on the trained meta-model, carrying out rapid adaptation on the new patient subtype, and predicting the possibility that the new patient subtype has a deterioration event in a certain time window in the future. The method and the system can effectively learn the disease change mode of the patient under the condition of limited clinical data, improve the prediction accuracy of the new patient subgroup, and are especially suitable for clinical prediction scenes under the condition of small samples.

The present invention relates to a system and a method for measuring and analyzing minimal residual disease (MRD) in pediatric B-cell precursor acute lymphoblastic leukemia (B-ALL) using multiparameter flow cytometry (MPFC). The invention finds application in clinical diagnostics and hematology-oncology for quantifying MRD in B-ALL patients with high sensitivity and specificity, needed for risk stratification, monitoring treatment response, and informing therapeutic decisions. The system comprises interconnected subsystems including an acquisition subsystem with an MPFC instrument, a control and file generation subsystem, and an analytical subsystem. The analytical subsystem incorporates modules for sequential data reduction, automated data cleaning, automated unsupervised data clustering, and interactive cluster analysis. Key advantages include high MRD detection sensitivity (e.g., 10⁻⁵ or 0.001%) and high specificity, without reliance on reference samples or supervised machine learning models, making it applicable in laboratories with different measuring equipment and using different panels of antibodies for identification of leukemic cells.

A multi-modal AI model for assisting knee joint tuberculosis diagnosis belongs to the field of assisting knee joint tuberculosis diagnosis and comprises a 3D image modal input module, a text modal input module, a test result modal input module, a multi-modal feature fusion module and a large language module. Aiming at the problems that the early diagnosis time of knee joint tuberculosis is too long and subjective interpretation is different, the diagnosis accuracy can be improved while the diagnosis time is shortened; according to the invention, an M3D model framework is optimized, CT / MRI images, laboratory indexes and text data are fused, and a multi-modal feature interaction auxiliary diagnosis system is constructed; according to the invention, accurate identification of early lesions of knee joint tuberculosis is realized, the misdiagnosis rate is reduced, and efficient and objective quantitative decision support is provided for clinic; according to the invention, the problems of high misdiagnosis rate and long flow caused by insufficient specificity in early diagnosis and examination of knee joint tuberculosis are solved through technical innovation.

The invention relates to the technical field of medical informatization, in particular to a clinical aid decision-making optimization method, device and equipment based on guide knowledge, and the method comprises the steps: obtaining electronic medical record data from a hospital information system and a laboratory information management system communication system, data standardization is achieved through the steps of duplicate removal, missing value processing, abnormal value processing, format standardization and the like; according to authoritative knowledge such as clinical pathways, diagnostic guidelines and disease consensus provided by doctors, key feature information is extracted by utilizing a natural language processing technology, structured data is extracted by adopting a regular expression, and unstructured texts are marked by pre-training a medical word vector dictionary, performing word segmentation by a classifier and performing part-of-speech tagging. The weight is optimized based on the loss function; an initial case knowledge base is constructed, and high-quality cases are screened out according to evaluation indexes such as knowledge richness and curative effect; dividing the high-quality cases into well-known physician cases and rare disease cases through syntactic analysis according to the classification indexes; case matching is carried out by adopting selective weighted heterogeneous value distance measurement, discrete attributes adopt value difference measurement, continuous attributes adopt standardized difference square, and attribute weights are optimized by means of a genetic algorithm, so that auxiliary clinical decision and knowledge services are realized. The invention can provide a scheme which can efficiently construct a high-quality case library, automatically extract key feature information, fuse guide knowledge for classification and screening, and introduce more accurate measurement and weight optimization in matching.

The invention relates to the technical field of risk scoring tools, in particular to a non-invasive simple risk scoring tool for traumatic hemorrhagic shock for wearable equipment. The method has the advantages that a non-invasive simple risk scoring tool for traumatic hemorrhagic shock is combined with clinical practice, economic benefits and time benefits are comprehensively considered, five vital sign indexes including systolic pressure, diastolic pressure, heart rate, respiratory rate and oxyhemoglobin saturation are used as the basis, a multi-factor Logistic regression method is used for constructing the risk scoring tool, and the risk scoring tool is used for evaluating the risk of traumatic hemorrhagic shock. The evaluation process does not need to depend on laboratory detection or highly specialized operators, and the first-aid personnel can master the use method in a short time and quickly complete evaluation. Due to the light design and the low operation threshold of the tool, the tool can be effectively applied in a non-professional scene, meanwhile, extra wounds to the wounded are reduced, the operation complexity is greatly reduced, and the risk scoring tool is more suitable for pre-hospital first aid and rapid deployment and application requirements in special scenes.

According to the AI-assisted marine microorganism anti-tumor activity product separation method provided by the invention, a microorganism strain with high anti-tumor activity potential can be directly and quickly screened out by constructing a marine microorganism sample database and applying a trained HetGNN model; the workload and time cost of tedious fermentation culture and biological activity testing in a traditional screening process are greatly reduced, meanwhile, the screening period is shortened, and the screening efficiency is greatly improved. Besides, a closed-loop feedback system formed by the invention feeds back a high-purity target compound sample separated from a laboratory and related data thereof to a microorganism sample database, and continuously optimizes the integrity of the database and the performance of the HetGNN model, so that the accuracy and efficiency of subsequent anti-tumor active compound screening are remarkably improved; the problem that in the prior art, screening precision is limited by initial data, and iterative upgrading is difficult is solved.