238 results about "Clinical decision" patented technology

The invention discloses a medical clinical decision support method and system based on a knowledge graph, and the method comprises the following steps: S1, collecting structured and unstructured medical data, and constructing an initial medical knowledge graph; s2, performing term standardization and semantic alignment on the graph to generate a fusion knowledge graph; s3, constructing a time-labeled medical record graph structure based on the medical record data, and aligning the time-labeled medical record graph structure with the fusion graph; s4, inputting the fusion atlas and the medical record graph into the hypersphere graph neural model, and generating semantic representation; s5, calculating a gravitation vector by using a path traction module, and guiding the propagation direction of the reasoning path; s6, generating a diagnosis and treatment candidate set and a corresponding recommended path according to the node state; s7, optimizing a model structure and initial parameters through a black widow spider optimization algorithm; and S8, outputting diagnosis and treatment suggestions and reasoning paths. According to the method, intelligent organization of medical knowledge and accurate diagnosis and treatment path recommendation are realized, and the auxiliary decision making efficiency and reliability are improved.

The invention discloses an enteritis patient condition severity risk early warning method, a medium and equipment. The method comprises the following steps: acquiring a borborygmus original signal and a clinical multi-dimensional physiological parameter sequence through a sensing device; constructing a borborygmus dynamic characteristic spectrum based on the borborygmus original signal to generate an acoustic biomarker time sequence; inputting the acoustic biomarker time sequence and the clinical multi-dimensional physiological parameter sequence into a multi-modal fusion early warning model to obtain an intestinal inflammation risk index; executing a signal quality self-evaluation process and generating a data quality warning code when the signal quality is abnormal; triggering a multi-node collaborative monitoring mechanism based on the risk index to generate an intestinal state multi-dimensional situation map; establishing an individualized risk baseline and generating a graded early warning instruction; and finally outputting a comprehensive early warning report. According to the method, multi-modal fusion analysis of the borborygmus signal and the clinical parameters is realized, the accuracy and timeliness of illness state early warning are remarkably improved through dynamic risk assessment and signal quality monitoring, and a reliable basis is provided for clinical decision making.

The invention discloses a machine learning-based intra-operative bleeding risk prediction method and device for spinal surgery. The machine learning-based intraoperative bleeding risk prediction method for spinal surgery comprises the following steps: acquiring information of a patient to be predicted; obtaining a trained hemorrhage risk prediction model; and inputting the information of the patient to be predicted into the trained massive hemorrhage risk prediction model so as to obtain a prediction result. According to the method, the high-precision prediction model is trained through large-scale patient data (including basic information, operation parameters, blood indexes and the like), so that the accuracy and the stability of intraoperative SBL risk prediction are improved. And a Cell Saver use suggestion based on a risk threshold is provided, and blood resource allocation is optimized. Clinical decision-making efficiency is improved through an automatic tool, blood transfusion related complications (such as infection and immune response) are reduced, and patient prognosis is improved.

The invention provides a medical information retrieval enhancement method and device based on a large model and related equipment, and relates to the technical field of artificial intelligence. The method comprises the following steps: in response to a medical information query request, executing double-path joint retrieval of keywords and semantics in a pre-constructed medical knowledge base to obtain a candidate data set containing at least one medical information query result, the medical knowledge base is a database which analyzes the multi-modal medical data based on a multi-modal medical data deep analysis large model and is constructed according to the analyzed medical text data; and adopting a plurality of sorting tools to resort the medical information query results in the candidate data set, and performing fusion processing on a plurality of resorting results obtained by resorting to obtain a medical information query result after retrieval enhancement. According to the method and the device, the precision improvement and response efficiency optimization of medical knowledge retrieval can be realized, a high-credibility knowledge enhancement service is provided for a clinical decision support system, and the method and the device have important application value.

The invention discloses a case resource integration data system based on big data analysis, and belongs to the technical field of medical data. The method comprises the following steps: acquiring hospital case data and corresponding disease type data to construct a resource integration range, acquiring a personal case information set provided by medical consultation of a patient, performing sensitive data extraction on the personal case information set to obtain a dynamic case parameter set, and sending the dynamic case parameter set to a data risk analysis module; the multi-source data acquisition module processes the personal case information set as follows; according to the method, a data integration-risk analysis-clinical intervention closed-loop system is constructed, preorder data standardization integration guarantees analysis reliability, accurate risk analysis provides a direction for intervention, multi-level alarm and pre-plan matching is achieved through linkage of the preorder data standardization integration and the accurate risk analysis, prediction diagnosis reports and intervention suggestions are automatically generated, invalid operations are reduced, the clinical decision-making efficiency is improved, and the system is suitable for large-scale popularization and application. And meanwhile, through dynamic threshold updating and system self-iteration optimization, the adaptability and practicability of the system are continuously enhanced.

The invention discloses a medical informatization data intelligent analysis system based on a large model, and belongs to the technical field of medical informatization and artificial intelligence, and the system comprises a data collection and standardization module, a medical knowledge graph construction module, a knowledge enhancement inference analysis module, a data quality evaluation module and a structured output module. The system collects multi-source heterogeneous medical data from an electronic medical record system, a laboratory information system, a medical image information system and a hospital information system, performs standardization processing, automatically constructs a medical knowledge graph, and performs intelligent analysis and reasoning on the medical data by using a knowledge retrieval enhanced medical field large language model. Meanwhile, the data quality is evaluated in four dimensions of integrity, accuracy, timeliness and relevance, and finally a structured analysis report is generated. The multi-source medical data can be effectively integrated, the accuracy and interpretability of medical data analysis are improved, and intelligent support is provided for clinical decision making.

The invention provides a large model collaborative reasoning and dynamic optimization method for oral clinical decision, and belongs to the field of artificial intelligence. A structured thinking chain auditing reasoning mechanism is constructed, and multi-source evidence fusion and traceable output are realized through a planner, an actuator and a verifier; designing a dynamic expert routing and risk gating mechanism, packaging a large language model, a knowledge graph, image analysis and the like into pluggable experts, dynamically selecting and fusing according to context, and supporting security degradation when evidence is insufficient; a continuous optimization closed loop driven by multi-source feedback is established, doctor adoption and editing behaviors and patient follow-up results are converted into multi-dimensional rewards, a model is updated in combination with reinforcement learning and preference alignment, and meanwhile, elastic weight consolidation and knowledge distillation are introduced to prevent catastrophic forgetting. The method effectively solves the problems of uninterpretability, uncredibility, static solidification and single capability of the model, significantly improves the transparency, robustness and safety of decision making, and is suitable for orthodontics, implantation, maxillofacial surgery and other high-risk scenes.

The invention relates to the technical field of wisdom medical treatment, in particular to a wisdom medical data self-supervision deep processing analysis system, which comprises a multi-department data sensing layer for acquiring multi-department data and postoperative follow-up visit data of a patient; the operation period feature processing center extracts data features of all departments, correlates data before and after an operation through an attention mechanism, and generates a patient perioperative period unified feature map; the full-cycle dynamic analysis unit analyzes the state of the patient in stages based on the patient perioperative period unified characteristic spectrum, and outputs a risk index in combination with a self-supervised training prediction model; the adaptive clinical decision module generates customized suggestions for different departments according to the analysis result and the risk index; the cross-department data collaboration unit is used for constructing a visual collaboration platform, so that doctors of all departments share and interact with a whole-cycle analysis result of an operation; and the dynamic feedback optimization unit collects clinical diagnosis and treatment results and performs reverse fine tuning on parameters of the self-supervised model. Therefore, the problems of strategy solidification, insufficient energy utilization efficiency and the like in the prior art are solved.

The invention discloses a traditional Chinese medicine clinical auxiliary decision making system and method based on a multi-mode large language model. According to the method, through a multi-modal information perception and fusion step, visual feature extraction is carried out on a tongue picture image, and the tongue picture image is fused with clinical text semantics; through a case matching step of retrieval enhancement, similar cases are retrieved from a clinical case knowledge base; a clinical auxiliary evaluation report including syndrome discrimination, diagnosis conclusion, traditional Chinese medicine prescription and reasoning process is generated through a context-aware diagnosis generation step; and the clinical rationality of an output result can be ensured by adopting field customization indexes through specialized evaluation steps. A corresponding system comprises a tongue diagnosis generator, a clinical case knowledge base, a diagnosis generator and other components, and end-to-end clinical decision support is achieved. According to the method, the problems of difficulty in integrating multi-modal information, lack of professional data sets and the like in traditional Chinese medicine diagnosis are effectively solved, and doctors are assisted to improve the accuracy and reliability of diagnosis results.

The invention relates to a medical behavior compliance auditing method and system based on an AI model, and the method comprises the steps: obtaining multi-source heterogeneous medical data and medical compliance data, and carrying out the standardization of the medical data; based on the standardized medical data and medical compliance data, respectively constructing a medical data set and a compliance data set; based on event graph analysis, analyzing an examination report from the standardized medical data through a first agent, identifying a diagnosis and treatment behavior, identifying clinical decision logic, and outputting a patient diagnosis and treatment portrait; based on the patient diagnosis and treatment portrait and the compliance data set, performing compliance auditing on the compliance of the to-be-audited medical behavior through a second agent; the compliance auditing comprises compliance judgment and output of the confidence degree of a judgment result. According to the invention, automation and accuracy of compliance auditing of medical behaviors are improved through multiple agents in combination with a patient diagnosis and treatment portrait and a causal chain.

The invention provides a deep learning-based dynamic early warning analysis method for disseminated intravascular coagulation, which comprises the following steps of: acquiring and structuring a core physiological mechanism knowledge graph of the disseminated intravascular coagulation, preprocessing standardized and clean multi-modal clinical time sequence data, generating a physiological consistency constraint vector by utilizing a dynamic graph matching and graph embedding technology, and performing dynamic early warning analysis on the disseminated intravascular coagulation core physiological mechanism knowledge graph. Inputting the medical logic correlation variable into a time sequence encoder fused with a knowledge graph attention mechanism to realize efficient modeling of the medical logic correlation variable; anti-factual reasoning is carried out based on model output, the risk of dispersive intravascular coagulation of a patient is dynamically scored, a grading early warning signal is triggered, and clinical decision making is supported; the model can update the knowledge graph and optimize parameters according to new cases and clinical feedback increments, the adaptability and generalization are enhanced, and the accuracy, early warning ability and medical interpretation of risk assessment of disseminated intravascular coagulation are effectively improved.

The invention discloses a large language model liver cancer prediction method and system based on an improved sampling strategy, and relates to the technical field of artificial intelligence medical diagnosis, and the method comprises the steps: obtaining an electronic medical record of a patient, carrying out the sequential reconstruction, and constructing a structured cue word; a plurality of reasoning paths are generated in parallel by using a random decoding strategy according to a general large language model with frozen input parameters; constructing a target distribution model based on gamma distribution, and calculating the normalized weight of each path by adopting an importance sampling algorithm to suppress low-quality paths and amplify the weight conforming to medical logic paths; and finally, performing weighted aggregation on the diagnosis conclusion based on the weight to obtain a prediction result, and outputting the reasoning process with the highest weight as an interpretability report. The accuracy of liver cancer prediction and the clinical decision transparency can be remarkably improved without fine adjustment of the model.

The invention discloses a tumor medical record time axis generation method based on multi-modal recognition and dynamic cue words, which thoroughly solves the problem of disordered stage division of a traditional method by introducing a dynamic cue word mechanism into an operation discrimination node. Through a multi-mode OCR + NLP fusion strategy, the accuracy of medical record information extraction is greatly improved; the medical compliance of the time axis is ensured through time sequence verification and field completion driven by the knowledge graph; and through front-end mind map or medical history outline visualization, the complex medical record can be clearly displayed. Practice shows that compared with manual arrangement, the time consumption of sequential output of the complete medical record is reduced by more than 90%, the item missing rate is reduced by more than 80%, and the method can be widely applied to scenes such as clinical decision support, follow-up visit management, scientific research data integration and medical insurance quality control.

The invention discloses a medical video risk identification and diagnosis and treatment scheme generation method for a surgical operation and a layered multi-agent framework, and relates to the technical field of artificial intelligence medical auxiliary diagnosis. The method comprises the following steps: constructing an association database according to collected multi-type operation videos, clinical data of patients and a surgical field knowledge database; receiving a surgical scene query, generating an initial retrieval instruction according to the query, and dividing a query task into a visual semantic task and a cognitive reasoning task according to the initial retrieval instruction; respectively executing instrument identification and action identification according to the initial retrieval instruction and the associated database, and executing a risk early warning task and a diagnosis and treatment scheme generation task according to the results of instrument identification and action identification and the associated database to form an intraoperative risk assessment report and a postoperative diagnosis and treatment scheme of the patient. According to the method, the whole process of'intra-operative risk investigation and postoperative diagnosis and treatment scheme generation 'is covered, high-risk positioning precision and interpretability are achieved, and the reliability of clinical decision making is guaranteed.

AIHeal system is a new solution creating explainable and causal AI infrastructure in a way that is accessible to physicians, solution in which they can trust, which open new routes to delivering better, faster, more reliable, secure, and more cost-effective medical care. The data from wearables and implants are transferred by mobile networks and collected by big data architecture in the innovative Electronic Health Record forming the "digital twin" of the patient. The ground-breaking innovative contribution of the proposed patent is to reach naturally interpretable AI through the synergistic AI concept. The main idea is to combine the benefits of the accuracy of deep-learning algorithms with visibility on the factors that are important to the algorithm's conclusion. Our approach is an attempt to follow the physicians thinking through the clinical decision procedure. Physicians first follow some rules and knowledge, then "call" experience (data) and make a decision.

A system for real-time prediction of heart disease and to support clinical decisions; the system includes: a patient data acquisition module configured to receive multimodal inputs, including physiological signals selected from electrocardiographic waveforms, blood pressure readings, heart rate readings, and oxygen saturation readings, as well as demographic and lifestyle information, including age, gender, cholesterol levels, smoking habits, and family medical history; a preprocessing engine configured to perform data cleansing, imputation of missing values, categorical coding, and feature scaling, so that the input data is normalized and converted into a format suitable for machine learning; a processing core comprising a multi-core CPU / GPU system-on-chip operationally coupled with a secure storage unit, wherein the processing core is configured to execute a variety of pre-trained machine learning models, including Naive Bayes, Random Forest, Logistic Regression and Decision Tree classifiers; a model evaluation unit configured to calculate validation metrics such as precision, recall, F1 score and area under the curve for each of the models and dynamically select the model with optimal performance to generate real-time predictions for the risk of heart disease; a display interface configured to present predictive results in the form of risk probability values, confidence indices, and actionable recommendations that are mapped to clinical treatment guidelines; and a communication interface configured to transmit emergency alerts based on high-risk predictions to remote caregivers, hospitals, and emergency response systems via wireless communication protocols such as WLAN, Bluetooth, and 4G / 5G cellular connections.

The invention relates to the technical field of intelligent medical treatment, and discloses a critical patient enteral nutrition tolerance dynamic evaluation device, which comprises a multi-source data acquisition module, a causal topology construction module, a causal path tracking module, a risk quantitative evaluation module, an early warning traceability module and a decision fusion module, synchronously collecting enteral nutrition and key physiological parameters of the critical patient, and obtaining multi-dimensional time sequence data; a causal topology is constructed for the multi-dimensional time sequence data, and a time-varying causal inference map is obtained; taking a preset gastrointestinal intolerance core index as a root node, tracking the map topological structure, and obtaining a key causal path; quantitatively evaluating the key causal path risk to obtain a path risk measure; interpreting a path risk measure threshold, generating a causal early warning signal, and reversely tracing to determine a priority intervention target; fusing the early warning signal, the key path, the risk measure and the intervention target, and outputting a clinical decision basis; according to the method, the efficiency of dynamic evaluation of the enteral nutrition tolerance of the critical patient can be improved.

The invention particularly relates to a real-time intelligent auxiliary and generative quality control method and system for electronic medical records, and relates to the technical field of medical information. An intelligent differential transmission module; a server context reconstruction and management module; a real-time intelligent auxiliary generation module; and a generative quality control and feedback module. According to the invention, the client interaction module supports multi-modal input and fine-grained snapshot, so that a doctor can input more conveniently, differential transmission is combined with an anchor point mechanism to greatly reduce the data volume, stable transmission can be realized under a weak network, medical record editing gets rid of network constraints, and average editing time consumption is reduced; intelligent auxiliary generation is based on a chapter accurate matching model, continuous writing is pushed in real time, suggestions are complemented, doctors focus on clinical decisions, and repeated input is reduced; according to the generation type quality control, multi-dimensional real-time error correction from integrity, accuracy and consistency is achieved, post rectification is changed into pre-prevention, the medical record quality is guaranteed, and medical resource waste caused by error backtracking is avoided.

The present disclosure introduces an AI-based system (100) and method for predicting the risk of acute exacerbation of chronic obstructive pulmonary disease (COPD) over three months. The system interfaces with consumer devices through REST APIs, ensuring secure communication via HTTPS. Client modules collect and pre-process raw data for integrity and compatibility. This data is then sent to the server (104) which is then transmitted to Input module, the input module (112) authenticates and validates it. The module processes personal details, respiratory history, allergies, vaccination and medication history, and demographic data for air quality analysis. The prediction module (116) uses a CART Model with 90% accuracy to evaluate COPD risk. Based on predefined thresholds, the clinical pathway module (118) and risk prediction response module (120) offer personalized treatment protocols and clinical decision support. The system complies with ISO 13485 standards for safety and reliability.

The invention discloses a correlation analysis method for sleep disorder and cardiovascular adverse events of an MINOCA patient, relates to the field of clinical decision of cardiovascular diseases, and aims to perform anti-factual reasoning on data of a specific patient based on a structural causal model and simulate the correlation between sleep disorder and cardiovascular adverse events of the patient under the condition of changing one or more sleep characteristic variables. The theoretical change value of the cardiovascular adverse event risk of the patient is calculated, and the expected risk reduction benefit of intervention is calculated; and generating a decision support report, and performing visual display. According to the method, by means of time sequence causal discovery and a structural causal model, the causal relationship and effect quantification of sleep features, intermediary indexes and adverse events are defined, and a core action mechanism is revealed; a personalized intervention effect is simulated through anti-factual reasoning, and a precise risk prediction and intervention basis is provided for clinic; the visual decision support report reduces the clinical application threshold, assists in optimizing the personalized treatment scheme of the MINOCA patient, and effectively reduces the risk of cardiovascular adverse events.

The invention discloses a diagnostic reasoning RAG system for resisting retrieval noise based on a self-generated knowledge base, and the system comprises a knowledge retrieval module which carries out the retrieval from an external knowledge base according to a user question to obtain a plurality of target text blocks; the evaluation generation module is used for independently evaluating each target text block through an evaluation model so as to identify retrieval noise, generating a correlation judgment result and a noise degree judgment result of each target text block, and generating internal memory information as a self-generated knowledge base based on a user question; and the diagnosis reasoning module is used for integrating the user question, each target text block, the corresponding correlation judgment result, the noise degree judgment result and the internal memory information into enhanced prompt information and inputting the enhanced prompt information into a large language model to generate a diagnosis reasoning answer. According to the method, retrieval noise interference can be effectively resisted, model knowledge support is strengthened by means of the self-generated knowledge base, the precision and timeliness of medical diagnosis reasoning are improved, and the requirements of the medical field for high-precision and strong-timeliness clinical decision making are met.

The invention discloses an intelligent medical system based on multi-modal data fusion, and the system comprises a multi-modal data feature extraction module which is used for extracting the internal information of medical image data Fimg, pathological section data Fpath, text case data Ftext and voice case data Faudio; the inter-modal relationship analysis module is used for obtaining a visual consistency feature Fvis and a first association intensity mark of image-pathology fusion, obtaining a language consistency feature Flang and a second association intensity mark of text-voice fusion, and obtaining a key association feature Fcross representing the association intensity of vision and language and a third association intensity mark; and the fusion module is unified to obtain final judgment data. Aiming at the common problem of image and pathology inconsistency in medical practice, the method not only records consistency information, but also systematically analyzes various aspects of inconsistency, and provides more comprehensive reference for clinical decision making.

The invention relates to the field of control, in particular to an infusion pump drug dosage control method and system fusing clinical decision support, and the method comprises the steps: monitoring at least two physiological parameters in real time, including a main treatment index and a side effect precursor index; for each parameter, calculating the parameter credibility based on the parameter signal fluctuation characteristic and the deviation degree of the parameter signal fluctuation characteristic and the predicted value of the PK-PD model, and during state updating, weighting the monitoring data by using the parameter credibility to correct the state of the patient; in the constraint conditions, a multi-dimensional safety state space formed by the main treatment indexes and the side effect precursor indexes is defined, and boundaries are adjusted according to the credibility of the side effect precursor indexes; in the cost function, the length of a prediction time window and the asymmetric penalty weight of a prediction track deviating from a target are adjusted according to the credibility of main treatment indexes, a model prediction control problem is solved to obtain an optimal administration dosage sequence, and a first control instruction is output to an infusion pump to be executed.

The application belongs to the technical field of disease risk prediction, and discloses a computer network-based risk prediction system for senile dementia, which comprises the following steps: collecting multi-modal data of a target user to obtain social interaction data, physiological parameter data and cognitive behavior data; extracting cognitive fluctuation nodes, social degradation nodes and physiological disorder nodes through dynamic fluctuation feature analysis to form a potential key node feature set; constructing an individualized dementia conversion risk map based on time sequence correlation and cross-modal coupling characteristics of the potential key node feature set to identify key risk nodes; determining a risk inflection point and predicting a senile dementia risk grade by analyzing the topological structure change and time evolution mode of the dementia conversion risk map; and providing a personalized risk intervention scheme and monitoring intervention effects in real time; the application realizes early and accurate prediction and active intervention of dementia risk, and significantly improves prevention efficiency and clinical decision value.

The invention discloses a disease medical case deep derivation system based on medical case cross-domain fusion, and the system comprises a data collection and preprocessing layer which is used for carrying out the collection and preprocessing of multi-source medical case data, and obtaining initial data; the knowledge fusion and representation layer is used for converting the initial data into structured knowledge and generating a target medical case knowledge graph with a unified semantic basis; the intelligent derivation and reasoning layer is used for performing deep mining and intelligent reasoning through the target medical case knowledge graph; the application interface layer receives medical case information of a patient and inputs the medical case information to the intelligent derivation and reasoning layer; and deriving the medical case information based on the intelligent derivation and reasoning layer to obtain a derivation result. The data coverage capability is improved based on multi-source heterogeneous medical case deep fusion, the analysis depth is improved based on multi-level deep derivation and hybrid reasoning, the reasoning capability is improved based on combination of semantic reasoning, case reasoning and deep learning, knowledge discovery is achieved based on intelligent mining of deep diagnosis and treatment rules, and meanwhile all-around clinical decision support is facilitated.

The invention relates to the technical field of electric digital data processing, in particular to a medical record AI intelligent integration and analysis system based on big data, and the system corresponds to the steps: determining a first class cluster and a second class cluster in medical data; determining each keyword group between the first category cluster and the second category cluster, and combining the medical records in the first category cluster to construct a co-occurrence matrix; determining the confidence of the target medical record by using the patient condition improvement degree obtained based on the diagnosis and treatment data, the examination data and the medication data in the co-occurrence matrix; and by utilizing the confidence coefficient and each element of the target keyword group in the co-occurrence matrix, determining the importance degree of the second-class cluster to the first-class cluster, and determining a splitting threshold value of the first-class cluster and each sub-class cluster in the first-class cluster. Through the technical scheme of the invention, the accuracy of medical data clustering is improved, and the interpretability of data organization and the support capability of clinical decision are improved.

The invention belongs to the field of intelligent medical treatment. The invention provides a two-channel feature screening method for cognitive impairment recognition modeling and a modeling method thereof, and the method comprises the steps: taking multi-site cognitive impairment screening data as input data, and carrying out the preprocessing of the input data; constructing a traditional robust feature screening channel and an LLM knowledge enhancement screening channel, and performing feature screening on the preprocessed input data; integrating dual-channel feature screening results, performing bias perception joint score optimization, and obtaining a feature set; and performing semantic alignment and version normalization on the feature set to obtain a high-quality feature set. And applying the high-quality feature set to a classifier for cognitive impairment recognition modeling, and generating a clinical decision support result. According to the parallel feature screening method based on combination of a large language model and traditional feature screening, the problem of data heterogeneity bias in cognitive impairment recognition is systematically solved by constructing a dual-channel collaborative feature evaluation architecture, and organic unification of statistical robustness and clinical interpretability is achieved.

A clinical decision support device, comprising: a parameter acquisition module, configured to acquire measured values for markers in a biomarker combination of a subject, wherein the biomarker combination at least comprises an alpha fetoprotein, an abnormal prothrombin, γ-glutamyltransferase, and an albumin; a risk assessment module, configured to input the measured values for the markers in the biomarker combination into a calculation model to obtain an output of the calculation model as a liver cancer risk prediction result of the subject; and an output module, configured to output the liver cancer risk prediction result of the subject. Also disclosed are a sample analysis system and a method for assessing a liver cancer risk of a subject, capable of better assessing a liver cancer risk of a subject.

The invention provides a risk prediction method and device based on digestive system department diseases, and the method comprises the steps: obtaining clinical data and intestinal microbiome data of a target object, and carrying out the preprocessing and standardization of a clinical data set and the intestinal microbiome data; inputting the preprocessed clinical data into a first encoder to obtain a clinical feature matrix; inputting the preprocessed intestinal microbiome into a second encoder to obtain an intestinal microbiome characteristic matrix; based on a cross-modal attention mechanism, performing bidirectional attention calculation on the clinical feature matrix and the intestinal microbiome feature matrix to obtain fusion features; the method comprises the following steps: performing two-way interaction and fusion on clinical data and microbiome data of a target object, calculating an SHAP value of a fusion feature, taking the fusion feature as an input of a preset risk prediction module, and obtaining a risk prediction value of a disease of the digestive system department, and obtaining the risk prediction value of the disease of the digestive system department through two-way interaction and fusion of the clinical data and the microbiome data of the target object. The method can assist doctors in interpreting which key clinical indexes or intestinal microorganism characteristics have relatively large multi-risk prediction influence, assist clinical decision, and further improve the interpretability of digestive system department disease risk prediction.

The application discloses an abnormality detection system for high-dimensional medical time series data, which first proposes a hierarchical time series causal SHAP value method. Through the overall architecture of dimension grouping pruning and time hierarchy decomposition, the method can greatly reduce the feature contribution degree calculation complexity of high-dimensional medical time series data. Through a time series causal constraint mechanism, the method realizes a causal explanation conforming to physiological logic, provides more accurate decision support information for clinics, helps doctors more accurately understand the causes of abnormalities, and further reduces the feature contribution degree calculation complexity of high-dimensional medical time series data through frequency domain sparse sampling, avoids high overhead of time domain point-by-point calculation, and further reduces the feature contribution degree calculation complexity of high-dimensional medical time series data. In addition, a cross-cycle deviation degree matrix can be outputted, so that the contribution degree of each time step relative to the deviation degree of the historical same period normal level is analyzed, and more rich decision support information is provided for clinical decision making.