1095 results about "Diagnosis laboratory" patented technology

The invention discloses a blood tumor patient clinical data analysis method and system based on a knowledge graph, and relates to the technical field of medical information processing. The method comprises the following steps: analyzing an unstructured text by utilizing a natural language processing model, and constructing a standardized entity set; aggregating laboratory indexes through a dynamic window, and constructing a time axis data set for marking a treatment stage; a standardized entity is integrated based on a blood tumor ontology model, a dynamic global knowledge graph is constructed in combination with a clinical causal relationship and a time sequence path, and chain reaction early warning is realized; based on the real-time patient sub-atlas, successful cases with the same molecular features are retrieved in the knowledge atlas to generate a candidate treatment scheme set, a graph neural network trained through an optimization algorithm is utilized to predict the complication risk of each scheme, and finally the optimal treatment scheme is evaluated and selected; according to the method, a clinical data barrier is broken through, dynamic reasoning, advanced risk early warning and personalized scheme optimization of the whole treatment cycle are realized, and the diagnosis and treatment accuracy, safety and medical resource utilization efficiency are remarkably improved.

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 diagnosis, in particular to a diagnosis and treatment auxiliary system and method for the obstetrics and gynecology department, and the system comprises a parameter collection module, a feature offset recognition module, a feature reconstruction module, a collaborative trend recognition module and a diagnosis and treatment suggestion generation module. According to the system and the method, the body temperature, the heart rate, the pulse, the blood pressure and the image edge density are synchronously acquired and the timestamps are bound, so that a structured diagnosis and treatment data unit is formed, the time sequence comparison capability of multi-source information is effectively improved, and potential risk points can be positioned and feature tags can be constructed based on a numerical value offset identification and synchronous index combined judgment mechanism; according to the method, physical signs, images and laboratory data are normalized and fused into three-dimensional vectors, cross-dimensional feature linkage expression is achieved, the dynamic insight ability to pathological state changes is improved by screening features with large variation amplitude and long duration time and judging trend consistency, and the accurate response to the fetal risk state is enhanced by combining a specific trend combination matching rule.

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 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 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, 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 relates to the field of medical treatment, and discloses a full-process quality control management system for neonatal disease screening, which comprises a data real-time acquisition module for realizing data intercommunication and dynamic acquisition with a laboratory information system, detection equipment and a sample management system through a standardized API (Application Program Interface) and a data transmission protocol; the indoor quality control module is used for performing real-time monitoring and rule management on laboratory internal detection quality by applying a Westgard multi-rule control algorithm and a real-time data monitoring technology; and the interroom quality evaluation module is used for carrying out multi-laboratory detection result comparison and quality evaluation by adopting a data encryption and anonymization processing technology. Through cooperation of a cross-system docking unit and a dynamic data analysis unit of the data real-time acquisition module, data islands of a laboratory information system, detection equipment and a sample management system are broken through, and real-time intercommunication and standardized processing of sample information, detection data and process nodes are realized.

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 relates to the field of medical informatization, in particular to a pathology laboratory ISO15189 intelligent face-to-face inspection auxiliary system based on a knowledge graph, and is applied to the quality management and standard authentication process of a pathology laboratory. According to the method, automatic mapping of the ISO15189 standard terms and pathological quality inspection key points is realized by constructing the knowledge graph in the pathology field and combining a natural language processing technology, so that the inspection efficiency is greatly improved; the system automatically generates a meeting inspection report meeting the standard, and shortens the document arrangement time from several days to several hours; meanwhile, the risk monitoring module collects service flow data, carries out risk assessment and early warning, and ensures that the inspection process is compliant. The application of the knowledge graph technology effectively overcomes the limitation of the traditional method in understanding the terminology semantics, and enhances the quality management and inspection preparation work of the pathology laboratory.

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 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 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 a single nucleotide polymorphism (SNP) primer of a DSTN gene related to musk ketone content in natural musk and application of the single nucleotide polymorphism (SNP) primer, and belongs to the technical field of biological detection. The invention provides a single nucleotide polymorphism (SNP) primer of a DSTN gene related to the musk ketone content in natural musk, a molecular marker corresponding to the SNP primer is located at a 682371th basic group of a chromosome 23 of a forest musk deer reference genome, the basic group is mutated into A or C, the genotypes are AA and AC, and deoxyribonucleotide sequences of the SNP primer are shown as SEQ ID NO: 1 and SEQ ID NO: 2. According to a breeding target, by eliminating A / A genotype individuals and retaining A / C genotype individuals, the method has the beneficial effects that the relative content character of musk ketone in musk of the forest musk deer is efficiently and quickly identified, and a scientific basis is provided for early breeding of high-quality forest musk deer. The detection method disclosed by the invention is simple and easy to operate, can be carried out in a laboratory, and can also be applied to a genome breeding technology.

The invention relates to a disease prediction method, a model, equipment and a storage medium. The method comprises the following steps: modeling medical events and static demographic statistical data by adopting a time awareness attention mechanism and a time enhanced Transform to obtain a medical event level representation, and modeling laboratory detection data by using a hierarchical Transform to obtain a laboratory test data representation; constructing a diagnosis time sequence heterogeneous graph by integrating the external knowledge graph; fusing node embedding and edge semantic information of the diagnosis time sequence heterogeneous graph by utilizing a node-edge-type perception heterogeneous graph attention network to obtain a patient graph-level representation; multi-modal complementary features are extracted from the multi-modal representation by adopting multi-head attention and soft orthogonality constraints, and a disease prediction result is generated according to the multi-modal complementary features. According to the method, the complementary relationship between laboratory detection data and medical events can be fully mined, it is ensured that the model can extract really valuable complementary features, and the comprehensiveness and accuracy of patient characterization are improved.

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 wart-shaped neoplasm cutter and the wart-shaped neoplasm cutting and collecting integrated device comprise a fixed handle and a rotating handle, an installation space of a wart body collecting structure is reserved between the fixed handle and the rotating handle, a wart body cutting structure is arranged on the lower section of the fixed handle, and the wart body cutting structure is in mechanical linkage with the lower section of the rotating handle. The wart collecting structure is used for cutting off warts along with rotation of the rotating handle, and after the warts are cut off, the rotating handle can trigger the wart collecting structure through the triggering element on the upper section to complete collection of the cut warts at the same time. The utility model aims to solve the problems of inconvenience in treatment and cross infection risk after resection of neoplasm verrucosa in the prior art. Specifically, by means of the ingenious design, efficient cutting and instant collection of wart-shaped neoplasm are achieved, the operation process is simplified, safety is improved, and medical staff can conveniently and rapidly transfer collected wart samples to a laboratory for pathological examination.

The invention discloses a chest surgery postoperative complication early warning system. The chest surgery postoperative complication early warning system comprises a data acquisition layer, a data transmission layer, a data processing and storage layer, an analysis and early warning layer and a user interface and feedback layer. The data acquisition layer is used for acquiring vital signs of a patient, laboratory inspection, image data and information manually input by medical staff; the data transmission layer transmits the collected data to the data processing and storage layer in real time; the data processing and storage layer preprocesses, stores and manages the collected data; the analysis and early warning layer analyzes the data through a predefined rule and a machine learning model, calculates a risk score in real time, and triggers early warning; and the user interface and feedback layer provides a graphical operation interface, mobile terminal pushing and alarm feedback. According to the invention, the change of the condition of the patient after the thoracic surgery can be accurately monitored in real time, the risk of complication can be early warned in time, decision support is provided for medical personnel, and the postoperative recovery effect of the patient is improved.

In part, the disclosure relates to methods, and systems suitable for evaluating image data from a patient on a real time or substantially real time basis using machine learning (ML) methods and systems. Systems and methods for improving diagnostic tools for end users such as cardiologists and imaging specialists using machine learning techniques applied to specific problems associated with intravascular images that have polar representations. Further, given the use of rotating probes to obtain image data for OCT, IVUS, and other imaging data, dealing with the two coordinate systems associated therewith creates challenges. The present disclosure addresses these and numerous other challenges relating to solving the problem of quickly imaging and diagnosis a patient such that stenting and other procedures may be applied during a single session in the cath lab.

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 laboratory safety intelligent monitoring management system and method based on the Internet of Things and the data driving technology. The monitoring management system comprises a laboratory environment monitoring module, an experimenter behavior monitoring module, an experimental equipment state monitoring module, a hazardous chemical substance management monitoring module, an intelligent potential safety hazard early warning and evaluation module and an emergency response remote cooperative processing module, and the six modules carry out cooperative processing. The monitoring management method comprises five modes of laboratory environment monitoring, experimenter behavior monitoring, experimental equipment state monitoring, hazardous chemical substance management monitoring and emergency response remote cooperative processing. According to the invention, a newly proposed time sequence prediction attention mechanism module prediction unit is adopted, the prediction accuracy is improved, various factors which are easy to cause experimental accidents in a laboratory are comprehensively considered, early warning can be carried out for different abnormal conditions, and effective supervision of laboratory safety risks is ensured.

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.

The invention discloses a method for evaluating pesticide residues in food based on big data, and relates to the technical field of data management.The method comprises the steps that firstly, a purification process is optimized according to purification process parameters of samples and laboratory environment states, and it is ensured that each sample is subjected to optimal pretreatment; then, automatically extracting signal characteristics of optical biosensing or immunoassay in a rapid detection stage, preliminarily marking suspicious positive, and switching to a high-resolution mode for confirmation; then, double spectrum scanning is carried out on the samples marked as positive, chromatographic conditions are subjected to graded optimization according to the values, and early warning is automatically given out for abnormity or drifting in the whole process; the self-adaptive mechanism from purification to detection to confirmation not only realizes omnibearing correction of matrix interference, but also remarkably improves sensitivity and specificity of pesticide residue assessment through continuous iteration of real-time feedback, thereby ensuring high efficiency and reliability of food safety monitoring.

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.