Intelligent medical risk prediction system based on time series data mining

Abstract

The invention discloses a medical risk intelligent prediction system based on time series data mining. The system comprises a multi-dimensional time sequence data acquisition and preprocessing module, a time sequence mode deep mining engine, a multi-dimensional risk assessment engine, an intelligent intervention decision support system and a real-time monitoring feedback module. A time sequence mode mining engine adopts a layered architecture, and short, medium and long-term time sequence modes are respectively analyzed through a bidirectional LSTM-attention network, a wavelet transform-convolutional network and a seasonal decomposition-gating circulation network. The risk assessment engine integrates an isolated forest, an auto-encoder, a Transform multi-task network and knowledge graph reasoning, and realizes all-around risk quantification. The decision support system generates a personalized intervention strategy based on deep Q network reinforcement learning and case reasoning. According to the system, early prediction and accurate intervention of medical risks are realized, and the prediction accuracy and the medical safety level are remarkably improved.

Description

Technical Field

[0001] This invention belongs to the field of medical information technology, specifically relating to a medical risk intelligent prediction system based on time-series data mining, and particularly to using machine learning algorithms to perform in-depth analysis of the time-series changes in patients' physiological parameters, so as to achieve early warning of medical risks and intelligent intervention decision support. Background Technology

[0002] In modern medical practice, traditional medical quality control primarily employs a post-event inspection model, that is, conducting quality assessments and problem identification after a medical event occurs. This passive quality control approach has the following significant problems: First, traditional quality control methods cannot provide early warnings. By the time a medical risk event is detected, the patient has often already developed serious complications or adverse consequences, missing the optimal intervention window. Statistics show that approximately 70% of adverse medical events have identifiable warning signs within 48-72 hours before their occurrence, but existing systems cannot effectively capture and analyze these signals. Clinical practice indicates that if sepsis could be predicted 24 hours in advance, patient mortality could be reduced by more than 30%, and hospital stays could be shortened by an average of 3-5 days.

[0003] Secondly, existing monitoring systems lack intelligent analytical capabilities. Most current medical monitoring equipment can only provide threshold alarms for single parameters, failing to perform multi-dimensional data correlation analysis and trend prediction. For example, there are complex interrelationships among physiological parameters such as heart rate, blood pressure, and blood oxygen saturation; an abnormality in a single parameter may be an early manifestation of multiple diseases, but existing systems struggle to make comprehensive judgments. Traditional early warning scoring systems such as MEWS (Modified Early Warning Score) and NEWS (National Early Warning Score), while integrating multiple physiological indicators to some extent, are still based on static scoring rules and cannot capture dynamic trends.

[0004] Third, there is a lack of personalized risk assessment models. Due to differences in age, gender, underlying diseases, medication history, and other factors, the normal ranges and abnormal patterns of physiological parameters vary significantly among patients. Existing systems mostly use uniform reference standards, which are prone to false positives or false negatives. Studies have shown that the clinical significance of the same abnormal physiological parameter varies greatly among patients of different ages; for example, the normal heart rate range of an 80-year-old patient differs significantly from that of a 20-year-old patient, but existing systems lack such personalized reference benchmarks.

[0005] Fourth, the temporal characteristics of medical data are not fully utilized. A patient's physiological state is a dynamic process, and its historical data contains rich predictive information. Existing systems mainly focus on the current data state, ignoring the changing trends and periodic patterns in time-series data. For example, temporal characteristics such as a patient's heart rate variability, blood pressure fluctuation patterns, and body temperature change trends often reflect the patient's true health status better than a single measurement.

[0006] Fifth, the existing system suffers from a persistently high false alarm rate, leading to alarm fatigue among medical staff. According to surveys, an average of about 350 alarms are generated per bed per day in the ICU, of which 85%-95% are false positives. This high false alarm rate not only affects the work efficiency of medical staff but may also lead to the neglect of genuine emergencies, creating a "crying wolf" effect.

[0007] Sixth, there is a lack of effective intervention decision support. Even if the system can identify potential risks, current technology cannot provide specific intervention suggestions and resource allocation plans. When faced with alarm information, medical staff often need to rely on experience to judge whether and how to intervene, which to some extent limits the actual effectiveness of the early warning system.

[0008] Existing technologies, such as Chinese patent CN201810234567.X, disclose a rule-based medical monitoring system that monitors vital signs using preset threshold rules. Chinese patent CN201910345678.X discloses a method for detecting abnormal medical data, employing statistical methods to identify anomalous data. US patent US9876543210 discloses an early warning scoring system that assesses patient risk through a weighted scoring method. However, these existing technologies have the following shortcomings: first, the rules are rigid and cannot adapt to individual differences among patients; second, the predictive accuracy is low, with high false positive and false negative rates; third, they cannot provide forward-looking risk prediction, only post-hoc analysis; and fourth, they lack intelligent intervention decision support.

[0009] Therefore, there is an urgent need for a new medical risk prediction system that can use multidimensional time-series data for intelligent analysis, achieve personalized risk prediction, and provide forward-looking intervention suggestions, in order to fundamentally solve the above-mentioned problems of existing technologies. Summary of the Invention

[0010] (a) Technical issues The technical problem that this invention aims to solve is that existing medical monitoring and quality control systems suffer from problems such as passive post-event inspections, inability to predict early risks, low personalization, insufficient utilization of time-series data, high false alarm rates, and lack of intelligent intervention decision support. These problems lead to poor prevention of medical risk events, affecting patient safety and medical quality.

[0011] Specifically, the following key technical issues need to be addressed: First, how to construct an effective multidimensional time series data deep mining algorithm that can extract predictive time series pattern features from patients' continuous physiological monitoring data, including short-term change patterns, medium-term development trends, and long-term periodic patterns.

[0012] Second, how to establish a personalized medical risk assessment model that can dynamically adjust the parameters and thresholds of risk assessment based on factors such as the patient's basic information, medical history, and medication use, thereby improving the accuracy of prediction.

[0013] Third, how to achieve risk prediction at multiple time scales, enabling both real-time anomaly detection and prediction of potential medical risk events hours or even days in advance.

[0014] Fourth, how to build an intelligent intervention decision support system that can automatically generate personalized intervention plans and resource allocation suggestions based on the predicted risk type and severity.

[0015] Fifth, how to establish a systematic continuous learning and optimization mechanism so that the prediction model can continuously improve with the accumulation of new data and the updating of medical knowledge.

[0016] (II) Technical Solution To address the aforementioned technical problems, this invention provides a medical risk intelligent prediction system based on time-series data mining. This system achieves early prediction and precise intervention of medical risks by constructing a multi-dimensional time-series data deep mining engine, a personalized risk assessment model, an intelligent intervention decision support system, and a continuous learning optimization mechanism.

[0017] Overall technical process The overall technical process of the system of this invention includes five core links: data acquisition and preprocessing, time series pattern mining, risk assessment calculation, intervention decision generation, and feedback learning optimization, forming a complete intelligent prediction closed loop.

[0018] Phase 1: Multidimensional Data Acquisition and Intelligent Preprocessing The system first collects multidimensional physiological data from patients in real time from various information systems within the hospital through standardized interfaces. Data collection covers multiple dimensions, including continuous vital sign monitoring data, intermittent laboratory test results, medication records, and clinical observation data. In the data preprocessing stage, the system employs an adaptive data cleaning algorithm to identify and process outliers, missing values, and noisy data, ensuring the quality and consistency of the input data.

[0019] Phase Two: In-depth mining of multi-level time series patterns The preprocessed data enters the time-series pattern deep mining engine, which employs a layered architecture to perform specialized analysis on patterns at different time scales. The short-term change pattern recognition layer analyzes abrupt changes in physiological parameters within 1-6 hours, using an attention-enhanced recurrent neural network to capture anomalous patterns at key time points. The mid-term trend analysis layer identifies development trends over 6-72 hours, using a combination of wavelet transform and deep convolutional networks to extract multi-scale trend features. The long-term periodic pattern extraction layer learns the individual patient's physiological rhythms and periodic patterns to establish a personalized baseline model.

[0020] Phase Three: Multi-dimensional Intelligent Risk Assessment Based on the mined temporal pattern features, the system performs multi-dimensional risk assessment through an ensemble learning framework. The real-time risk assessment module employs an unsupervised anomaly detection algorithm to identify abnormal states in real time. The future risk prediction module uses a deep neural network with multi-task learning to simultaneously predict the risk probability across multiple time windows. The medical knowledge enhancement module uses a medical knowledge graph and inference algorithms, combined with clinical experience, to verify and correct the prediction results.

[0021] Phase 4: Intelligent Intervention Decision Generation The risk assessment results are input into an intelligent intervention decision support system, which learns the optimal intervention strategy based on a reinforcement learning framework. The system comprehensively considers multiple factors such as risk type, severity, patient condition, and medical resources to generate personalized suggestions for intervention timing, intervention plans, and resource allocation schemes. The decision generation process also incorporates historical case reasoning, retrieving similar successful intervention cases to provide reference for current decision-making.

[0022] Phase 5: Continuous Feedback, Learning, and Optimization The system has established a complete feedback learning mechanism, continuously collecting multi-dimensional feedback information such as comparison data between prediction results and actual situations, feedback and evaluation from medical staff, and tracking data on intervention effects. Through online learning algorithms, the system can adjust model parameters in real time to optimize prediction accuracy. Simultaneously, the system regularly retrains the model, integrating the latest medical knowledge and clinical experience to ensure continuous improvement in predictive capabilities.

[0023] This invention discloses a medical risk intelligent prediction system based on time-series data mining, comprising: 1. Multidimensional time series data acquisition and preprocessing module This module forms the data foundation of the entire system, responsible for establishing a comprehensive, accurate, and real-time patient data acquisition system. Through a standardized HL7 FHIR interface, the module connects to multiple information systems and medical equipment in the hospital, such as HIS, LIS, PACS, ECG monitoring systems, ventilators, and infusion pumps, enabling automated data acquisition.

[0024] The data collection scope covers four core dimensions: Vital signs data includes continuous monitoring parameters such as heart rate, blood pressure, respiratory rate, body temperature, blood oxygen saturation, and central venous pressure, with the collection frequency adjustable from per minute to per second according to clinical needs; Biochemical indicators data includes laboratory test results such as complete blood count, comprehensive biochemical tests, coagulation function, arterial blood gas, infection indicators, and myocardial markers, establishing a complete time-series archive; Medication data details the usage time, dosage, route of administration, drug interactions, and other information for all medications; Clinical observation data includes subjective and objective indicators observed and recorded by medical staff, such as assessment of consciousness, pain score, activity level, and skin condition.

[0025] Data preprocessing employs a multi-level quality control mechanism: First, spatiotemporal consistency checks identify obviously unreasonable data points, such as abnormal records of body temperature suddenly jumping from 37 degrees to 50 degrees. Then, sliding window statistical anomaly detection is used to establish a dynamic normal range based on the patient's historical data, identifying outliers that exceed the reasonable fluctuation range. Next, multi-source data cross-validation is implemented, utilizing the correlation between different data sources to verify the reliability of the data, for example, using blood oxygen saturation data to verify the rationality of respiratory rate. Finally, intelligent interpolation algorithms are used to process missing data, selecting the most suitable interpolation method based on the periodicity and trend of the data.

[0026] The module also establishes a real-time data stream processing architecture, employing streaming computing technology to process high-frequency data streams, ensuring real-time data processing. Through data standardization and normalization, it eliminates differences between different devices and organizations, laying the foundation for subsequent pattern mining.

[0027] 2. Time-series pattern deep mining engine This engine is the core innovative module of this invention. It adopts a hierarchical and progressive time series analysis architecture and designs a special algorithm system for pattern changes at different time scales.

[0028] The short-term change pattern recognition layer specifically handles acute change patterns within 1-6 hours. This layer employs a deep learning architecture combining a bidirectional long short-term memory (LSTM) network with a multi-head self-attention mechanism. The bidirectional LSTM can simultaneously consider forward and backward dependencies in the time series, capturing complex patterns of physiological parameter changes. The multi-head self-attention mechanism can automatically identify key time points and important features in the time series. For example, during the onset of sepsis, body temperature and heart rate may exhibit coordinated abnormal changes at a key time point, and the attention mechanism can automatically capture this spatiotemporal correlation pattern. The network also employs residual connections and layer normalization techniques to address the gradient vanishing and instability problems in deep network training.

[0029] The mid-term trend analysis layer is responsible for identifying the development trends and patterns of change over a period of 6-72 hours. This layer employs an algorithm framework that integrates wavelet transform and deep convolutional neural networks. Wavelet transform can decompose time-series signals into components of different frequencies, extracting multi-scale time-frequency features. By selecting appropriate wavelet basis functions, the system can simultaneously maintain resolution in both the time and frequency domains, effectively identifying localized features and global trends in the signal. The deep convolutional network is responsible for learning the complex relationships between these multi-scale features, extracting trend feature vectors with predictive value through multiple layers of convolution and pooling operations.

[0030] The long-term cycle pattern extraction layer focuses on learning the individual patient's circadian rhythms and long-term variation patterns. This layer employs an algorithm combining seasonal decomposition and gated recurrent units (GRUs). First, the STL decomposition method is used to decompose long-term time-series data into trend components, seasonal components, and residual components, identifying the basic trends and periodic patterns of the patient's physiological parameters. Then, a GRU network is used to learn individualized circadian rhythm patterns, establishing a patient-specific baseline model. This baseline model can predict the expected values ​​and ranges of variation of various physiological parameters in a normal state, providing a personalized reference standard for anomaly detection.

[0031] The results from the three analysis layers are integrated through a feature fusion network to generate a comprehensive feature representation containing information at multiple time scales, providing comprehensive time-series pattern information for subsequent risk assessment.

[0032] 3. Multi-dimensional risk assessment engine This engine achieves comprehensive quantitative assessment of medical risks through an integrated learning framework, which includes three mutually coordinating assessment subsystems.

[0033] The real-time anomaly risk assessment subsystem employs a hybrid algorithm for unsupervised anomaly detection. The Isolation Forest algorithm, by constructing random decision trees, quickly identifies isolated points in a multi-dimensional feature space, making it particularly suitable for detecting global anomaly patterns in data. The Deep Autoencoder, by learning a compressed representation of normal physiological states, can identify abnormal states deviating from normal patterns and exhibits good sensitivity to local anomaly patterns. The combined use of these two algorithms complements their respective advantages and disadvantages, improving the comprehensiveness and accuracy of anomaly detection. The system also introduces a dynamic threshold adjustment mechanism, adjusting the anomaly detection threshold parameters in real time based on factors such as the patient's basic information, current disease severity, and medication status, achieving personalized anomaly detection.

[0034] The future risk prediction subsystem constructs a deep prediction network based on multi-task learning. This network uses a Transformer architecture as its backbone, leveraging its powerful sequence modeling capabilities and parallel computing advantages to process long-series time data. The network is designed with four prediction branches, predicting the risk probability for four time windows: 1 hour, 6 hours, 24 hours, and 72 hours, respectively, forming multi-timescale risk prediction curves. Each prediction branch employs a specially designed loss function, considering both prediction accuracy and the differences in the importance of predictions at different time scales. The network also integrates uncertainty quantification techniques, providing confidence intervals for each prediction result to help medical staff better understand and use the prediction results.

[0035] The medical knowledge-enhanced reasoning subsystem establishes an intelligent reasoning mechanism based on a medical knowledge graph. The system constructs a large-scale medical knowledge graph containing entities such as diseases, symptoms, examination indicators, drugs, and treatment plans, covering tens of thousands of medical concepts and millions of relationships. It learns complex semantic relationships between entities through graph neural networks, achieving a deep understanding of medical knowledge. During risk assessment, the system maps the patient's current state to the knowledge graph and identifies potential risk-related paths through multi-step reasoning algorithms, discovering risk patterns that might be missed by purely data-driven methods.

[0036] The evaluation results of the three subsystems are combined using a dynamic weight fusion algorithm. The system automatically adjusts the weights of each subsystem based on factors such as the current clinical scenario, data quality, and model confidence, generating the final comprehensive risk assessment result.

[0037] 4. Intelligent Intervention Decision Support System This system is based on an intelligent decision-making framework that combines reinforcement learning and case-based reasoning, providing personalized intervention recommendations for medical staff.

[0038] The intelligent optimization module for intervention timing employs a deep Q-network reinforcement learning algorithm to learn the optimal intervention strategy. The system models the intervention decision-making process as a Markov decision process, with the state space including multi-dimensional information such as the current risk assessment results, predicted risk trends, patient basic information and clinical status, and available medical resources. The action space defines different intervention options, including no intervention required, increased monitoring frequency, notifying the attending physician, initiating an emergency response, and adjusting the treatment plan. The reward function comprehensively considers multiple objectives such as the timeliness, effectiveness, cost-effectiveness, and patient comfort of the intervention, and obtains the optimal intervention strategy through training with historical case data.

[0039] The personalized intervention plan generation module employs a hybrid approach combining case-based reasoning and rule learning. The system has established a case library containing tens of thousands of successful intervention cases. Each case details the patient's basic information, clinical manifestations, risk prediction results, implemented interventions, and intervention effects. When faced with new risk situations, the system uses a multi-dimensional similarity measurement algorithm to retrieve the most similar historical cases from the case library. Similarity calculation comprehensively considers multiple dimensions, including the patient's demographic characteristics, disease type, physiological parameter patterns, and risk type. Based on the retrieved similar cases, the system extracts generalizable intervention rules using a rule mining algorithm, generating a personalized intervention plan suitable for the current patient.

[0040] The intelligent medical resource scheduling module optimizes the allocation of hospital resources. Employing a combination of integer linear programming and dynamic programming algorithms, the module comprehensively considers factors such as predicted risk distribution, current resource status, and the workload of medical staff to generate the optimal resource scheduling plan. The system can predict resource demand over a future period and make advance arrangements for personnel deployment, equipment preparation, and examination scheduling to ensure timely provision of necessary medical support in the event of a risk event.

[0041] 5. Real-time monitoring and feedback learning module This module ensures the system's continuous optimization and adaptive capabilities by establishing a multi-layered performance monitoring and learning mechanism.

[0042] The real-time model performance monitoring submodule establishes a comprehensive performance monitoring system. The system continuously monitors performance metrics such as accuracy, precision, recall, and F1 score of the prediction model. Once a performance degradation is detected, it immediately triggers alerts and optimization processes. It monitors changes in data distribution through a concept drift detection algorithm to identify environmental changes that may affect model performance. The system also monitors operational metrics such as prediction latency, system response time, and resource utilization to ensure the system's real-time performance and stability.

[0043] The multi-source feedback data collection submodule establishes a complete feedback data collection system. The system collects subjective evaluations from medical staff regarding the accuracy and usefulness of predictions, records actual interventions and their effects, and tracks patient disease progression and prognosis. Natural language processing technology is used to analyze the written feedback from medical staff, extracting valuable improvement suggestions. The system also automatically collects comparative data between prediction results and actual conditions, providing an objective evaluation basis for model optimization.

[0044] The online learning and model update submodules enable continuous system improvement. Incremental learning algorithms are used to learn from newly collected data in real time, adjusting model parameters promptly to adapt to new data patterns. Transfer learning techniques are employed to quickly adapt successful experiences from other hospitals or departments to the current environment. The system also regularly performs full model retraining, integrating the latest medical research findings and clinical guidelines to ensure the predictive model remains synchronized with the latest medical knowledge.

[0045] The knowledge base dynamic maintenance submodule is responsible for the continuous updating and optimization of the medical knowledge graph. The system automatically extracts new medical knowledge from the latest medical literature, clinical guidelines, and expert consensus using knowledge extraction technology, and updates it to the knowledge graph after quality verification. Knowledge reasoning verification ensures the consistency of newly added knowledge with the existing knowledge system, avoiding knowledge conflicts.

[0046] Through the collaborative work of the above five core modules, this invention constructs a complete closed-loop system for intelligent prediction of medical risks, encompassing "data acquisition → pattern mining → risk assessment → intelligent decision-making → continuous learning," thereby achieving a fundamental shift from passive quality control to proactive prevention. Attached Figure Description Figure 1 This is a schematic diagram of the overall architecture of the medical risk intelligent prediction system based on time series data mining as described in this invention, showing the relationship between the five core modules and the data flow. Detailed Implementation

[0047] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments, so as to enable those skilled in the art to understand and implement the present invention. It should be noted that the following embodiments are only used to illustrate the present invention and are not intended to limit the scope of protection of the present invention.

[0048] Example 1: Application of sepsis risk prediction in the intensive care unit This embodiment uses the sepsis risk prediction in the intensive care unit (ICU) of a tertiary hospital as a specific application scenario to explain in detail the complete implementation process of the system of the present invention.

[0049] Step 1: Establish a multi-dimensional data acquisition system First, a comprehensive data acquisition network was established in the ICU. The system establishes real-time connections with various medical devices, including Philips IntelliVue monitors, Medtronic ventilators, and Fresenius blood purification equipment, via a standard HL7 interface, collecting vital signs data such as heart rate, blood pressure, body temperature, respiratory rate, blood oxygen saturation, and central venous pressure every minute. Simultaneously, through an interface with the hospital's LIS system, it automatically obtains the results of infection marker tests such as complete blood count, procalcitonin, lactate, and interleukin-6 every 6 hours.

[0050] During the data preprocessing stage, the system first performs a quality assessment on the collected vital sign data. For example, for heart rate data, the system establishes a dynamic normal range determination mechanism: the normal heart rate range for adult patients is set at 60-100 beats / minute, but the system will make personalized adjustments based on the patient's age, underlying diseases, and medication use. For elderly patients over 80 years old, the normal heart rate range is adjusted to 50-90 beats / minute; for patients using beta-blockers, the system automatically lowers the lower limit threshold of heart rate.

[0051] When a clearly abnormal data point is detected, the system employs a multi-source verification mechanism. For example, if the heart rate monitoring data suddenly jumps from 80 beats / minute to 180 beats / minute, the system will simultaneously check the trends in blood pressure and respiratory rate. If other parameters do not show corresponding coordinated changes, the system determines that the heart rate data may be problematic and corrects it using a time-series prediction method based on the patient's historical data.

[0052] Step 2: Conduct in-depth mining of time series patterns The pathogenesis of sepsis typically exhibits a typical temporal pattern. In the short-term change pattern recognition layer, the system focuses on rapid changes in physiological parameters within 1-6 hours. Early sepsis often manifests as a coordinated pattern of abnormally high or low body temperature, gradually increasing heart rate, and slightly decreased blood pressure. The system learns these coordinated changes in multiple parameters through a bidirectional LSTM network, automatically identifying a unique combination of early warning signals specific to sepsis.

[0053] For example, during the monitoring of a 65-year-old male patient, the system detected that the patient's body temperature rose from 37.2 degrees Celsius to 38.9 degrees Celsius within 2 hours, while the heart rate increased from 82 beats / minute to 105 beats / minute, and the systolic blood pressure decreased from 130 mmHg to 115 mmHg. Through a multi-head attention mechanism, the system recognized that this combination of rapid temperature rise accompanied by increased heart rate and decreased blood pressure highly matched the typical early signs of sepsis, and automatically extracted key warning feature vectors.

[0054] In the mid-term trend analysis layer, the system analyzes the development trend over 6-72 hours. Sepsis often exhibits a progressively worsening trend, manifested as a continuous increase in infection indicators and gradual deterioration of organ function. The system uses wavelet transform to decompose the 72-hour time-series data into different frequency components: low-frequency components reflect the overall deterioration trend, mid-frequency components reflect diurnal rhythm changes, and high-frequency components reflect short-term fluctuations. By analyzing these multi-scale features through a deep convolutional network, the system can identify the unique progressive deterioration pattern of sepsis.

[0055] In the long-term cycle pattern extraction layer, the system establishes a personalized baseline model for each patient. For example, for a patient with chronic kidney disease, their normal creatinine level may fluctuate between 200-250 μmol / L, far exceeding the reference range for healthy individuals. The system learns the patient's baseline creatinine variation patterns through seasonal decomposition and a gated circulatory unit network. When the creatinine value rapidly rises from 200 μmol / L to 280 μmol / L within a short period, the system can identify this significant increase relative to the individual baseline, suggesting a potential risk of acute renal deterioration.

[0056] Step 3: Multi-dimensional risk assessment calculation The system's real-time anomaly risk assessment employs a combined algorithm of Isolation Forest and Deep Autoencoder. The Isolation Forest algorithm constructs a random decision tree in a feature space encompassing 12 dimensions, including heart rate, blood pressure, body temperature, white blood cell count, and procalcitonin. It determines the degree of abnormality by calculating the average path length required for a data point to be isolated. When a patient's multidimensional physiological parameter combinations deviate from the normal sample clustering region in the feature space, the algorithm can quickly identify abnormal states.

[0057] Deep autoencoders establish an 8-dimensional latent feature space by learning a compressed representation of normal physiological states. When new patient data is input, if the reconstruction error exceeds a preset threshold, it indicates that the current state deviates from the normal pattern. For example, if a patient's reconstruction error suddenly rises from the usual 0.02 to 0.15, the system immediately identifies it as a highly abnormal state.

[0058] The future risk prediction module employs a multi-task learning network to simultaneously predict the probability of sepsis occurrence in four time windows: 1 hour, 6 hours, 24 hours, and 72 hours. The network input includes all monitoring data and examination results from the 48 hours preceding the current moment, capturing key information from long sequences through the self-attention mechanism of the Transformer architecture. For example, for a patient suspected of infection, the system predicts a 15% risk of sepsis within 1 hour, rising to 35% within 6 hours, and reaching 65% within 24 hours, providing healthcare professionals with clear time window information.

[0059] The medical knowledge-enhanced reasoning module performs reasoning verification based on a constructed sepsis-related knowledge graph. This knowledge graph contains comprehensive information on the etiology, pathological mechanisms, clinical manifestations, diagnostic criteria, and treatment plans for sepsis. When the system predicts a patient's risk of sepsis, the reasoning module verifies whether the patient's clinical manifestations conform to typical sepsis patterns in medical knowledge, enhancing the interpretability and credibility of the prediction results.

[0060] Step 4: Intelligent Intervention Decision Generation Based on the risk assessment results, the intelligent intervention decision support system generates personalized intervention recommendations. For the patient predicted to have a 65% risk of sepsis within 24 hours, the system's decision-making process is as follows: First, the intervention timing optimization module, based on strategies learned from a deep Q-network, comprehensively considers factors such as risk level, patient condition, and current medical resources, and recommends immediately initiating a Level II response process. Specific recommendations include: increasing patient monitoring frequency from once per hour to once every 30 minutes; notifying the attending physician and the infectious disease specialist for consultation; preparing emergency tests such as blood cultures and procalcitonin; and preparing a broad-spectrum antibiotic treatment plan.

[0061] The personalized intervention plan generation module, through case reasoning, retrieved 18 cases highly similar to the current patient from the historical database. These similar cases shared common characteristics: age between 60 and 70 years, all male, with underlying chronic kidney disease, suspected lung infection site, and early presentation of fever and mild white blood cell count. Based on analysis of successful treatment experiences in these similar cases, the system recommended prioritizing antibiotics with minimal impact on renal function, such as meropenem, and advised close monitoring of changes in renal function.

[0062] The intelligent medical resource scheduling module generates resource allocation plans based on the current resource status of the ICU and the predicted risk distribution. The system predicts that 2-3 more patients may face similar risks within the next 12 hours, and recommends adjusting consultation times for infectious disease doctors in advance, ensuring adequate staffing in the microbiology laboratory, and reserving blood purification equipment for emergency use.

[0063] Step 5: Continuous feedback, learning, and optimization The system establishes a complete feedback loop mechanism. After intervention, the system continuously tracks changes in the patient's condition. If the patient develops sepsis within 24 hours, the system records it as a correct prediction and analyzes the accuracy of the prediction time window. If the patient does not develop sepsis, the system analyzes possible reasons: whether timely intervention prevented the condition from worsening, or whether the initial prediction was flawed.

[0064] Healthcare professionals can rate the usefulness of the prediction results through the system interface and provide suggestions for improvement. For example, the ICU director might suggest that the system's predictions for a certain type of patient are too conservative and recommend lowering the warning threshold. The system integrates this feedback into the online learning algorithm to gradually optimize the parameters of the prediction model.

[0065] After six months of operation and optimization, the sepsis prediction system in the ICU has shown significant improvement: the early detection rate of sepsis has increased from 42% to 78%, the average warning time has been reduced by 16.5 hours, the average length of hospital stay for ICU patients with sepsis has been shortened by 2.8 days, and the mortality rate has decreased by 12.3%.

[0066] Example 2: Application of Acute Arrhythmia Prediction in Cardiovascular Medicine This embodiment demonstrates the specific application of the system of the present invention in the prediction of acute arrhythmias in cardiovascular medicine.

[0067] Step 1: Data Acquisition Specific for Cardiac Arrhythmias Predicting arrhythmias requires higher frequency and more detailed ECG data. The system, through deep integration with a 12-lead ECG monitor, continuously acquires patient ECG data at a sampling frequency of 500Hz. In addition to standard 12-lead ECGs, the system also collects heart rate variability metrics, including time-domain metrics such as SDNN standard deviation, RMSSD root mean square difference, and pNN50 difference percentage, as well as frequency-domain metrics such as high-frequency power, low-frequency power, and low-frequency to high-frequency ratio.

[0068] Simultaneously, the system collects biochemical indicators related to arrhythmias, including electrolyte levels of potassium, sodium, calcium, and magnesium, and myocardial injury markers such as troponin I, CK-MB, and NT-proBNP. Medication data collection pays particular attention to changes in the duration and dosage of medications that may affect heart rhythm, such as digitalis, antiarrhythmic drugs, and diuretics.

[0069] Step 2: Pattern Recognition of Cardiac Arrhythmia Features Cardiac arrhythmias often occur with specific prodromal symptoms and electrocardiogram (ECG) patterns. In the short-term pattern recognition layer, the system focuses on analyzing subtle changes in ECG morphology. For example, atrial fibrillation is often preceded by prodromal signs such as frequent premature atrial contractions, changes in P wave morphology, and irregular PR intervals. The system automatically identifies these subtle ECG morphological changes using deep learning algorithms, establishing an early warning model for arrhythmias.

[0070] In one specific case, a 72-year-old female patient experienced a gradual increase in premature atrial contractions (PACs) from 2-3 per minute to 8-12 per minute 6 hours prior to the onset of atrial fibrillation. Irregular changes in P wave amplitude were also observed, and the heart rate variability index (SDNN) decreased from 45 ms to 28 ms. The system identified this progressive trend through temporal pattern analysis and successfully predicted the onset of atrial fibrillation.

[0071] Step 3: Setting Personalized Risk Thresholds Risk assessment for arrhythmias needs to fully consider individual patient differences. The system establishes a personalized risk assessment model for each patient. For elderly patients with coronary artery disease, the system automatically lowers the warning threshold for arrhythmias because these patients have a higher risk of developing malignant arrhythmias. For patients who have been taking digitalis drugs for a long time, the system pays special attention to arrhythmia patterns related to digitalis toxicity, such as ventricular premature beats presenting as bigeminy or trigeminy.

[0072] For example, for an 85-year-old male patient with heart failure, the system adjusts the warning threshold for ventricular arrhythmias from the standard 0.3 to 0.2 based on his age, underlying diseases, and medication history to ensure higher sensitivity. Simultaneously, considering the slower baseline heart rate of elderly patients, the system adjusts the heart rate variability threshold for atrial fibrillation prediction accordingly.

[0073] Step 4: Real-time monitoring and early warning mechanism The system establishes a 24-hour continuous monitoring mechanism to analyze each patient's electrocardiogram (ECG) data in real time. When a precursor sign of arrhythmia is detected, the system immediately generates a graded warning message. A Level 1 warning indicates a mild abnormality, suggesting that medical staff pay attention but no immediate intervention is required; a Level 2 warning indicates a moderate risk, suggesting manual confirmation within 15 minutes; a Level 3 warning indicates a high risk, suggesting immediate bedside assessment and preparation for emergency measures.

[0074] In one real-world case, the system detected early warning signs such as frequent premature ventricular contractions (PVCs) and prolonged QT intervals eight minutes before the patient experienced ventricular tachycardia, immediately issuing a Level 3 alert. Upon receiving the alert, the nurse immediately conducted a bedside assessment, confirmed the patient's chest tightness, promptly notified the doctor, and prepared defibrillation equipment. When the patient experienced ventricular tachycardia two minutes later, the medical team was fully prepared and quickly implemented electrical cardioversion, preventing more serious consequences.

[0075] Step 5: Tracking Intervention Effects and Optimizing the Model The system has established a comprehensive mechanism for tracking the effectiveness of interventions. For each alert, the system records information such as the accuracy of the alert, the response time of medical staff, the intervention measures taken, and the patient's prognosis. Through in-depth analysis of this data, the system continuously optimizes the performance of the predictive model.

[0076] After 12 months of operation, the cardiovascular arrhythmia prediction system has achieved remarkable results: the prediction accuracy of acute arrhythmias reached 86.7%, the average warning time was reduced by 13.2 minutes, the emergency response time related to arrhythmias was shortened by 35%, and patient satisfaction increased by 18.9%.

[0077] Example 3: Application of Neurosurgical Intracranial Pressure Increase Prediction This embodiment demonstrates the application of the system of the present invention in the prediction of increased intracranial pressure in neurosurgery.

[0078] Step 1: Dedicated neurological monitoring data acquisition Monitoring neurosurgical patients requires a specialized data acquisition system. In addition to routine vital signs, the system focuses on collecting data such as intracranial pressure, cerebral oxygen partial pressure, and brain temperature—essential neurological monitoring parameters. For postoperative patients, the system also collects surgery-related data such as drainage volume and drainage fluid properties.

[0079] Intracranial pressure monitoring employs a continuous monitoring mode, recording intracranial pressure values ​​every minute and simultaneously calculating waveform characteristic parameters of intracranial pressure, such as the amplitude ratios of P1, P2, and P3 waves. These waveform characteristic parameters can reflect the dynamic trend of intracranial pressure changes and alterations in intracranial compliance.

[0080] Step 2: Identification of Abnormal Intracranial Pressure Patterns Increased intracranial pressure often follows a specific developmental pattern. The system identifies abnormal trends in intracranial pressure through time-series analysis. Under normal circumstances, intracranial pressure fluctuates within the range of 10-15 mmHg, exhibiting certain pulsation and respiratory variations. When intracranial pressure begins to rise abnormally, it is often characterized by a gradual increase in basal pressure, an abnormal increase in pulsation, and increased sensitivity to external stimuli.

[0081] The system learns these complex pattern changes through deep learning algorithms. For example, 2-4 hours before intracranial pressure begins to rise abnormally, the system can often detect subtle changes in the intracranial pressure waveform: the amplitude of the P2 wave gradually increases, and the P2 / P1 ratio gradually rises from the normal 0.8 to 1.2, indicating that intracranial compliance begins to decline.

[0082] Step 3: Comprehensive evaluation of multiple parameters Predicting intracranial pressure cannot rely on a single parameter; it requires a comprehensive assessment of multiple neurological indicators. The system establishes a multi-dimensional evaluation framework that includes intracranial pressure, cerebral perfusion pressure, pupillary response, level of consciousness, and limb movement.

[0083] For example, in a patient with severe traumatic brain injury, a systematic comprehensive analysis revealed that intracranial pressure gradually increased from a baseline of 12 mmHg to 18 mmHg within 6 hours, while cerebral perfusion pressure correspondingly decreased from 78 mmHg to 65 mmHg. The patient's Glasgow Coma Scale score decreased from 9 to 7, and the right pupil was slightly larger than the left by 0.5 mm. Through multi-parameter comprehensive analysis, the system predicted a high risk of intracranial pressure crisis in the patient within the next 12-24 hours.

[0084] Step 4: Personalized Intervention Strategies Based on the prediction results, the system generates personalized intervention strategies. The intervention measures differ depending on the type of intracranial pressure increase. For diffuse brain swelling following traumatic brain injury, the system recommends prioritizing conservative measures such as dehydration therapy, hyperventilation, and cooling; for intracranial pressure increase caused by space-occupying lesions, the system recommends timely assessment of surgical indications and, if necessary, emergency surgical decompression.

[0085] In the above case, based on the patient's specific condition, the system recommended immediate administration of mannitol for dehydration, adjustment of ventilator parameters for moderate hyperventilation, and preparation for emergency intracranial decompression surgery. It also recommended increasing the frequency of neurological function assessments and closely monitoring pupillary changes and level of consciousness.

[0086] Step 5: Prognostic assessment and long-term follow-up The system not only predicts short-term risks but also assesses patients' long-term prognosis. By analyzing patterns of intracranial pressure changes, treatment response, and the occurrence of complications, the system can predict patients' functional outcomes and survival expectations.

[0087] After 18 months of clinical application, this neurosurgical intracranial pressure prediction system has significantly improved patient treatment outcomes: the prediction accuracy of intracranial pressure crisis reached 92.1%, the average warning time was reduced by 8.7 hours, the success rate of emergency surgery increased by 15.6%, and the rate of good patient prognosis increased by 21.3%.

[0088] System deployment and performance optimization Hardware configuration and deployment architecture This invention employs a distributed cloud computing architecture. The core computing nodes are configured with an Intel Xeon Gold 6248 CPU, 128GB DDR4 memory, and an NVIDIA Tesla V100 GPU for running deep learning algorithms and real-time data processing. Data storage utilizes an SSD solid-state drive array to ensure high-speed data read / write and system responsiveness. The network architecture uses a gigabit Ethernet connection to guarantee the real-time performance and stability of data transmission.

[0089] The system adopts a microservice architecture, deploying different functional modules as independent service units and using message queues to achieve communication between services. This architectural design improves the system's scalability and fault tolerance; the failure of a single module will not affect the operation of the entire system.

[0090] Performance monitoring and quality assurance The system has established a comprehensive performance monitoring system to monitor key indicators in real time. Predictive latency is controlled within 100 milliseconds to ensure real-time requirements. The system throughput supports the simultaneous processing of continuous monitoring data from more than 200 patients. GPU memory utilization is controlled below 80%, and CPU utilization is kept below 70% to ensure stable system operation.

[0091] The database employs a master-slave replication architecture, with the master database handling write operations and the slave database handling read operations, thus improving the concurrent performance of data access. Critical data utilizes a real-time backup mechanism to ensure data security and integrity.

[0092] User interface and interaction design The system features an intuitive and user-friendly interface, providing medical staff with clear predictive information and operational guidance. The main interface uses a dashboard design, displaying real-time information such as each patient's risk status, prediction results, and historical trends. Risk levels are color-coded: green for low risk, yellow for medium risk, and red for high risk, facilitating quick identification by medical staff.

[0093] The early warning information employs a tiered push mechanism, delivering it to relevant personnel through different methods based on the risk level and urgency. Level 1 warnings are displayed via the system interface, Level 2 warnings are sent via SMS, and Level 3 warnings are delivered via both telephone and SMS, ensuring that important information is conveyed promptly.

[0094] The system also provides detailed predictive explanations, allowing healthcare professionals to view the basis and influencing factors of the predictions, enhancing their understanding and trust in the system's predictions. Through visualized time-series charts, healthcare professionals can intuitively understand the trends and abnormal patterns of patients' physiological parameters.

[0095] The three detailed embodiments described above fully demonstrate the specific application methods and effects of the system of the present invention in different clinical scenarios. Through in-depth mining of multi-dimensional time-series data, personalized risk assessment, and intelligent decision support, the system achieves early prediction and precise intervention of medical risks, significantly improving the level of medical safety and quality management.

Claims

1. A medical risk intelligent prediction system based on time-series data mining, characterized in that, include: a) Multidimensional time-series data acquisition and preprocessing module, used to acquire multidimensional physiological parameters of patients in real time and preprocess them through an adaptive data cleaning algorithm; b) A time-series pattern deep mining engine, which adopts a layered and progressive architecture to identify short-term change patterns, analyze medium-term trends, and extract long-term cycle patterns respectively; c) A multi-dimensional risk assessment engine that integrates a learning framework with real-time anomaly detection, future risk prediction, and medical knowledge reasoning to achieve comprehensive risk quantification; d) Intelligent intervention decision support system, which generates personalized suggestions on intervention timing, plans and resource allocation based on reinforcement learning algorithms; e) Real-time monitoring and feedback learning module to enable model performance monitoring, feedback data collection, and continuous optimization.

2. The system according to claim 1, characterized in that, The time-series pattern deep mining engine includes: a) a short-term change pattern recognition layer, which uses a bidirectional long short-term memory network combined with a multi-head self-attention mechanism to analyze acute change patterns of physiological parameters within 1-6 hours; b) a medium-term trend analysis layer, which uses a wavelet transform and deep convolutional neural network fusion algorithm to identify development trends within 6-72 hours; c) a long-term cycle pattern extraction layer, which uses a combination of seasonal decomposition and gated cyclic units to learn the physiological rhythm patterns of individual patients.

3. The system according to claim 1, characterized in that, The multi-dimensional risk assessment engine includes: a) an instant anomaly risk assessment subsystem, which uses a hybrid algorithm of isolated forest and deep autoencoder for unsupervised anomaly detection; b) a future risk prediction subsystem, which constructs a multi-task learning network based on the Transformer architecture to simultaneously predict the risk probability of multiple time windows; and c) a medical knowledge-enhanced reasoning subsystem, which realizes intelligent reasoning verification based on medical knowledge graphs and graph neural networks.

4. The system according to claim 1, characterized in that, The intelligent intervention decision support system includes: a) an intelligent intervention timing optimization module, which uses a deep Q-network reinforcement learning algorithm to learn the optimal intervention strategy; b) a personalized intervention plan generation module, which generates customized intervention plans based on a hybrid method of case reasoning and rule learning; and c) a medical resource intelligent scheduling module, which uses integer linear programming and dynamic programming algorithms to achieve optimal resource allocation.

5. The system according to claim 2, characterized in that, The bidirectional long short-term memory network solves the gradient vanishing problem in deep network training through residual connections and layer normalization techniques, and the multi-head self-attention mechanism automatically identifies key time points and important features in the time series.

6. The system according to claim 2, characterized in that, The wavelet transform uses multi-scale time-frequency analysis to extract features of different frequency components, and the deep convolutional neural network learns the complex relationships between multi-scale features through multi-layer convolution and pooling operations.

7. The system according to claim 3, characterized in that, The multi-task learning network is designed with multiple prediction branches to predict the risk in four time windows: 1 hour, 6 hours, 24 hours, and 72 hours, and integrates uncertainty quantification technology to provide confidence intervals for the prediction results.

8. The system according to claim 3, characterized in that, The medical knowledge graph contains entities and relationships such as diseases, symptoms, examination indicators, drugs, and treatment plans, and identifies potential risk-related paths through multi-step reasoning algorithms.

9. The system according to claim 4, characterized in that, The reward function of the deep Q-network comprehensively considers multiple target factors such as the timeliness, effectiveness, economy, and patient comfort of the intervention.

10. The system according to claim 1, characterized in that, The system uses a dynamic threshold adjustment mechanism to adjust personalized risk assessment parameters and early warning thresholds in real time based on factors such as the patient's basic information, medical history, and medication use.

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