A deep learning-based method and system for predicting acute exacerbation of respiratory failure

Abstract

The application provides a respiratory failure acute exacerbation prediction method and system based on multi-data fusion analysis and deep learning, comprising: updating the disease fluctuation variance according to local correction to output, adopting a support vector machine to classify the disease fluctuation variance level to determine a prediction step selection value; after obtaining the prediction step selection value, fusing historical cache features, if the prediction step selection value is a short step, adjusting the cache window setting to expand to a twelve-hour range to obtain fused window data; calculating and updating the data dimension for the fused window data, judging the key node trigger timing by random forest evaluation of the degree of multi-source information fusion; adopting a conflict resolution sequence to reset the cache window, obtaining the expanded window data to judge whether the window expansion requirement meets the global self-adaptive requirement to obtain the final prediction result; verifying the matching degree of the disease fluctuation variance and the prediction step selection through the final prediction result, and determining the confirmation signal that the overall execution conflict has been resolved.

Description

Technical Field

[0001] This invention relates to the field of information technology, specifically to medical data processing and respiratory failure monitoring technology, and more particularly to a method and system for predicting acute exacerbations of respiratory failure based on multi-data fusion analysis and deep learning. Background Technology

[0002] In the field of healthcare, real-time monitoring systems are of paramount importance for ensuring patient safety and enabling timely intervention.

[0003] Especially in large-scale patient monitoring scenarios, the system needs to predict the risk of acute exacerbations in patients and ensure that the prediction results can quickly respond to changes in the patient's condition.

[0004] This predictive ability is directly related to the rational allocation of medical resources and the safety of patients' lives. Therefore, it is particularly important to build an efficient mechanism for storing and updating prediction results.

[0005] However, some existing methods often face an imbalance between resource allocation and response speed when dealing with large-scale monitoring needs.

[0006] Many systems tend to frequently recalculate all predictions when patient data changes, which leads to a waste of computing resources, especially when the patient's condition is relatively stable, making repeated calculations redundant.

[0007] However, reducing the frequency of calculations may cause key changes to be missed, affecting the timeliness of predictions.

[0008] This contradiction between computational cost and real-time performance has become a pressing problem that needs to be solved.

[0009] The deeper technical challenge lies in designing an intelligent cache update strategy that ensures the system can both save resources and capture critical changes.

[0010] The core of the cache update strategy lies in determining which data changes require new calculations and which can continue to use previously stored prediction results.

[0011] If the importance of data changes cannot be accurately distinguished, the system will either waste resources by updating too frequently or fail to update sufficiently, thus missing risk signals.

[0012] The complexity of this judgment directly affects the system's adaptability in dynamic environments.

[0013] Therefore, how to rationally determine the timing and scope of updating cached prediction results when patients' physiological data change has become a critical issue that urgently needs to be addressed.

[0014] For example, when monitoring patients with respiratory failure, if a patient's respiratory rate fluctuates slightly but does not reach the danger threshold, should the system immediately update the cached prediction results? If an update is needed, is the data scope limited to respiratory parameters, or does it need to be adjusted in conjunction with other indicators? If we don't update the forecast, how can we guarantee that the prediction results will still be reliable? These problems frequently arise in actual operations, directly affecting the efficiency and accuracy of the system's assessment of patient risk.

[0015] This problem is further reflected in the fact that when the patient's condition fluctuates greatly, the system may need to refresh the cache at shorter intervals, but this will increase the computational burden. When the condition is stable, however, too short an update interval is redundant.

[0016] How to dynamically adjust the frequency and scope of cache updates based on the patient's condition to ensure a balance between resource utilization and risk prediction is the key issue to be addressed in this study.

[0017] Terminology Definition 1. Short step size: This refers to a prediction step size of ≤6 hours, which is used in scenarios where the patient's condition fluctuates significantly and requires high-frequency monitoring.

[0018] 2. Update Granularity Conflict Index: This index measures the consistency between the data update frequency and the historical average update frequency. It is calculated as "Conflict Index = |Current Update Frequency - Historical Average Update Frequency| / Historical Average Update Frequency", reflecting the degree of conflict in the update rhythm of multi-source data.

[0019] 3. Fusion quality assessment value: The fusion effect of multi-source information is evaluated from three dimensions: data integrity, dimensional consistency and temporal continuity using a random forest model. The value ranges from 0 to 1, with the value closer to 1 indicating a better fusion effect.

[0020] 4. Global Adaptive Requirements: The degree of fit between the cache window range, prediction step size and the variance of the patient's real-time condition fluctuation is ≥90%, and the computational resource utilization rate is ≤40% and the prediction response time is ≤1 second.

[0021] 5. Physiological indicator trigger thresholds: Based on clinical respiratory failure monitoring standards, specifically including: blood oxygen saturation change ≥3% / 5 minutes, heart rate fluctuation ≥20 beats / min, respiratory rate change ≥5 breaths / min, and support for personalized adjustment of ±10% according to patient age and underlying medical history. Summary of the Invention

[0022] This invention provides a method for predicting acute exacerbations of respiratory failure based on multi-data fusion analysis and deep learning, mainly including: The system acquires real-time data sequences from changes in physiological indicators of patients with respiratory failure. If the data sequence shows changes below a threshold, the incremental calculation module is activated to process cached prediction results and obtain a locally corrected update output. Based on the locally corrected update output, the variance of the patient's condition fluctuation is analyzed, and a support vector machine is used to classify the variance level to determine the prediction step size. After obtaining the prediction step size, historical cached features are fused. If the prediction step size is a short step, the cache window setting is adjusted and expanded to a twelve-hour range to obtain fused window data. The updated data dimension is calculated for the fused window data, and the degree of multi-source information fusion is evaluated using a random forest to determine the triggering timing of key nodes. Update granularity conflict indicators are extracted from the triggering timing of key nodes. If the update granularity conflict indicators are higher than a preset threshold, the incremental calculation module is activated to perform a secondary local correction update and fuse historical cached features to obtain a conflict mitigation sequence. The cache window is reset using the conflict mitigation sequence, and the expanded window data is obtained to determine whether the window expansion requirement meets the global adaptive requirement to obtain the final prediction result. The final prediction result is used to backtrack and verify the matching degree between the variance of the patient's condition fluctuation and the prediction step size selection, determining a confirmation signal that the overall execution conflict has been resolved.

[0023] This invention provides a respiratory failure acute exacerbation prediction system based on multi-data fusion analysis and deep learning, mainly comprising: a real-time data monitoring module, used to acquire real-time data sequences from changes in physiological indicators of respiratory failure patients through a monitoring system; if the data sequence shows changes that do not reach a threshold, an incremental calculation module is activated to process the cached prediction results to obtain a locally corrected update output; a disease fluctuation analysis module, used to analyze the disease fluctuation variance based on the locally corrected update output, and use a support vector machine to classify the disease fluctuation variance level to determine the prediction step size selection value; a window data fusion module, used to fuse historical cached features after obtaining the prediction step size selection value; if the prediction step size selection value is a short step size, the cache window setting is adjusted to expand to a twelve-hour range to obtain fused window data; and a key node touch... The system comprises the following modules: a release module for calculating updated data dimensions based on the fused window data, and a random forest module for evaluating the degree of multi-source information fusion to determine the triggering timing of key nodes; a conflict detection and correction module for extracting update granularity conflict indicators from the triggering timing of key nodes, and if the update granularity conflict indicators are higher than a preset threshold, an incremental calculation module is activated to perform secondary local correction and update the fused historical cache features to obtain a conflict mitigation sequence; a cache window optimization module for resetting the cache window using the conflict mitigation sequence, obtaining expanded window data to determine whether the window expansion requirement meets the global adaptive requirement, and obtaining the final prediction result; and a backtracking verification module for backtracking and verifying the matching degree between the variance of the disease fluctuation and the selected prediction step size through the final prediction result, and determining a confirmation signal that the overall execution conflict has been resolved. The technical solution provided by this invention can include the following beneficial effects: This invention discloses an intelligent analysis method for real-time monitoring and prediction of physiological indicators in patients with respiratory failure, aiming to solve comprehensive business problems related to step size selection, data fusion, and conflict mitigation in predicting disease fluctuations. This invention acquires real-time patient physiological data sequences, activates an incremental calculation module for local correction and updates when changes do not reach a threshold, and uses a support vector machine to classify the variance of disease fluctuations to determine the prediction step size. Subsequently, it integrates historical cache features to adjust the window data range, uses a random forest to evaluate the degree of multi-source information fusion, and determines the triggering timing of key nodes. If the update granularity conflict indicators exceed the limit, this invention initiates a secondary local correction and generates a conflict mitigation sequence, resets the cache window to meet global adaptive requirements, and finally verifies the matching degree between the prediction results and disease fluctuations through backtracking. The core innovation of this invention lies in its multi-level data fusion and dynamic conflict mitigation mechanism, which significantly improves prediction accuracy and system adaptability, providing reliable support for clinical decision-making. Attached Figure Description

[0024] Figure 1 This is a flowchart of the method for predicting acute exacerbations of respiratory failure based on multi-data fusion analysis and deep learning, as described in this invention.

[0025] Figure 2 This is a schematic diagram of a method for predicting acute exacerbations of respiratory failure based on multi-data fusion analysis and deep learning according to the present invention.

[0026] Figure 3 This is another schematic diagram of the method for predicting acute exacerbations of respiratory failure based on multi-data fusion analysis and deep learning according to the present invention.

[0027] Figure 4 This is a schematic diagram of the structure of the respiratory failure acute exacerbation prediction system based on multi-data fusion analysis and deep learning of the present invention.

[0028] Figure 5 This is a multi-dimensional comparison chart of the prediction accuracy of this invention.

[0029] Figure 6 This is a timing diagram for the dynamic adjustment of the cache window in this invention.

[0030] Figure 7 This is a classification curve of the variance of disease fluctuations in this invention.

[0031] Figure 8 This is a waveform acquisition diagram of the multi-source physiological signal of the present invention.

[0032] Figure 9 This is a data processing pipeline diagram for the deep learning prediction algorithm of this invention.

[0033] Figure 10 This is a connection diagram of the patient physiological monitoring device of the present invention.

[0034] Figure 11 This is a schematic diagram of the sensor layout of the multi-parameter physiological signal acquisition system of the present invention.

[0035] Figure 12 This is a structural diagram of a data acquisition terminal provided in an embodiment of the present invention.

[0036] Figure 13 This is a diagram showing the physical deployment of the monitoring system of this invention. Detailed Implementation

[0037] To further understand the content of this invention, a detailed description of the invention is provided in conjunction with the accompanying drawings and embodiments. The specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0038] like Figure 1 As shown, the method for predicting acute exacerbations of respiratory failure based on multi-data fusion analysis and deep learning provided by this invention includes the following steps: First, step S101 is executed to acquire real-time data sequences and activate the incremental calculation module to obtain local correction update output. This step realizes real-time acquisition and preliminary processing of the patient's physiological parameters. Then, step S102 is executed to analyze the variance of the patient's condition fluctuations, determine the prediction step size selection value through SVM classification, and adaptively adjust the prediction time window according to the severity of the condition changes. Step S103 is to fuse historical cache features. When it is determined to be a short step size, the window is expanded to 12 hours to ensure the sufficiency of feature information. Step S104 is to calculate the updated data dimension, use random forest to evaluate the degree of fusion and determine key nodes, and identify the core features that affect the accuracy of prediction. Step S105 is to extract update granularity conflict indicators and obtain conflict mitigation sequences through secondary local correction to solve the temporal conflict problem in the multi-source data fusion process. Step S106 is to reset the cache window and obtain the final prediction result after judging the global adaptive requirements. Finally, step S107 is to backtrack and verify the matching degree to determine the confirmation signal that the conflict has been resolved, completing the closed-loop verification of the entire prediction process.

[0039] like Figure 3As shown, the method for predicting acute exacerbations of respiratory failure according to the present invention includes the following processing flow: First, in the data input stage, the fused window data is passed as input to the core processing stage; the core processing stage includes multi-source information fusion evaluation using a random forest model, and judgment of key node triggering timing; then, the conflict handling stage is entered, in which conflict index extraction at the update granularity and conflict judgment threshold comparison are performed sequentially. When the conflict index exceeds the threshold, the incremental calculation module is started to perform secondary local correction and generate a conflict mitigation sequence. When the conflict index does not exceed the threshold, the output verification stage is directly entered; in the output verification stage, the global adaptive requirement is judged first. If the requirement is met, the final prediction result is output and backtracking verification is performed to confirm the matching degree. If the global adaptive requirement is not met, the conflict handling stage is returned to re-extract the conflict index; in addition, the backtracking verification module feeds the verification result back to the random forest model through a feedback loop to realize continuous optimization and adaptive adjustment of the prediction model.

[0040] like Figure 4 As shown, the acute exacerbation prediction system for respiratory failure of the present invention includes eight functional modules, which work collaboratively through data flow and control flow. The real-time data monitoring module 101 continuously collects physiological parameter data of the patient, including key indicators such as blood oxygen saturation, respiratory rate, and heart rate; the condition fluctuation analysis module 102 extracts fluctuation features and performs trend analysis on the collected data; the prediction step size selection module 103 dynamically adjusts the step size of the prediction time window according to the degree of condition fluctuation; the window data fusion module 104 fuses multi-source heterogeneous data within the time window; the key node triggering module 105 identifies key time nodes of condition changes and triggers an early warning mechanism; the conflict detection and correction module 106 detects and corrects logical conflicts in the prediction results; the cache window optimization module 107 dynamically optimizes the historical data caching strategy; and the backtracking verification module 108 backtracks and verifies the prediction results, feeding the verification results back to the real-time data monitoring module 101, forming a closed-loop optimization mechanism to continuously improve prediction accuracy.

[0041] like Figure 9 As shown, the deep learning prediction algorithm data processing pipeline of this invention comprises five main parts: an input layer, a preprocessing layer, a deep learning model layer, an attention mechanism layer, and an output layer. The input layer receives a multi-source data input matrix with a data dimension of N x T x F, where N represents the number of samples, T represents the time step, and F represents the feature dimension. A sliding time window method is used to segment the sequence data. The preprocessing layer sequentially performs three steps: data standardization, missing value imputation, and feature extraction. Data standardization uses the Z-score method to normalize the data to a standard distribution.

[0042] The deep learning model layer is the core processing area, employing a three-layer stacked LSTM network structure. Each LSTM unit contains three gating mechanisms: input gate i, forget gate f, and output gate o, with the hidden state h propagated between layers. The number of hidden units in the three LSTM layers is set to 128, 64, and 32 respectively, using a decreasing structure to achieve feature compression. A Dropout layer is configured after each LSTM layer, with a dropout rate p set to 0.3 to prevent overfitting. The attention mechanism layer uses a Query-Key-Value structure to calculate attention weights, and the features at different time steps are weighted and aggregated through the attention weight matrix. The output layer includes a fully connected layer and a Softmax activation function, ultimately outputting a four-class prediction probability distribution corresponding to four prediction categories: normal, mild, moderate, and severe. The probability bar chart visually displays the prediction confidence for each category.

[0043] Specifically, this embodiment of a method and system for predicting acute exacerbations of respiratory failure based on multi-data fusion analysis and deep learning may include: S101. Real-time data sequences are obtained from changes in physiological indicators of patients with respiratory failure through the monitoring system. If the data sequence shows that the changes have not reached the threshold, the incremental calculation module is activated to process the cached prediction results and obtain local correction and update output.

[0044] The monitoring system continuously collects physiological indicators of patients with respiratory failure, forming a real-time data sequence. For the collected real-time data sequence, pre-established analysis rules are used to determine whether the changes in the data sequence reach preset thresholds. If the changes in the data sequence do not reach the preset thresholds, the incremental calculation module is activated to extract relevant historical data from the cached predictions, obtaining preliminary processing results. Based on the preliminary processing results of the incremental calculation module and combined with the current real-time data sequence, a local correction operation is performed to determine the corrected predicted values. Using the corrected predicted values, the output content is updated, generating a description of the physiological indicator status at the current time point. The updated output content is obtained and combined with historical data sequences from the monitoring system, using a support vector machine model to predict future short-term trends, obtaining prediction results. If abnormal trends are detected in the prediction results, an early warning mechanism is triggered, automatically recording abnormal data and storing it in a designated database.

[0045] like Figure 8As shown, this invention acquires multi-source physiological signal data of patients with respiratory failure in real time through a multi-channel parallel acquisition system. The figure shows the waveform acquisition of four channels: Channel 1 is the electrocardiogram (ECG) signal with a sampling rate of 250Hz. The waveform clearly shows typical PQRST waveform characteristics, where the P wave represents atrial depolarization, the QRS complex represents ventricular depolarization, and the T wave represents ventricular repolarization. The marking of the RR interval and ST segment facilitates subsequent analysis of heart rate variability and myocardial ischemia. Channel 2 is the blood oxygen saturation (SpO2) signal with a sampling rate of 25Hz. The waveform shows photoplethysmography (PPG) characteristics, including the main wave and dicrotic wave, and the current value is 95%. Channel 3 is the respiratory waveform (RESP) with a sampling rate of 25Hz, showing periodic sinusoidal changes. The inspiratory and expiratory phases are clearly marked, and the current respiratory rate is 18 breaths / min. Channel 4 is the body temperature change curve (TEMP) with a sampling rate of 25Hz, showing relatively stable body temperature fluctuations. The current body temperature is 37.2 degrees Celsius. The area marked by the dashed box in the diagram is the anomaly detection area. The system will focus its analysis on the signals within this area to determine whether to trigger the subsequent incremental calculation module for local correction and updates. This multi-channel waveform acquisition scheme provides a complete raw data foundation for subsequent data fusion analysis and deep learning prediction.

[0046] like Figure 10 As shown, the patient physiological monitoring system of the present invention includes a patient 201, a pulse oximeter 202, electrocardiogram (ECG) monitoring electrodes 203, a respiratory monitoring band 204, a data acquisition box 205, a connecting cable 206, a wireless transmission module 207, and a monitoring terminal display screen 208. The patient 201 is the monitored object, and various physiological signal sensors are installed on their body. The pulse oximeter 202 is a finger clip sensor worn on the patient's finger, used to collect blood oxygen saturation and pulse signals in real time. The ECG monitoring electrodes 203 are attached to the patient's chest, collecting ECG signals through multiple electrode pads. The respiratory monitoring band 204 wraps around the patient's chest and abdomen, monitoring respiratory rate and depth by sensing the rise and fall of the chest and abdomen. The above sensors are connected to the data acquisition box 205 via the connecting cable 206. The data acquisition box 205 acts as a central data aggregation device, responsible for receiving and initially processing the physiological signals collected by each sensor. The data acquisition box 205 has a built-in wireless transmission module 207, which wirelessly transmits the processed data to the monitoring terminal display screen 208. The monitoring terminal display screen 208 is used to display the waveforms and values ​​of various physiological parameters of the patient in real time, which facilitates monitoring and analysis by medical staff.

[0047] like Figure 11As shown, the multi-parameter physiological signal acquisition system of this invention adopts a distributed sensor layout scheme. Sensors are arranged at multiple key physiological monitoring positions on the frontal contour 301 of the human body: a blood oxygen sensor is installed at the fingertips 302 for real-time monitoring of blood oxygen saturation; ECG electrodes V1 and V2 are respectively set on the left side of the chest 303 and the right side of the chest 304, forming a dual-lead ECG acquisition configuration for acquiring ECG signals; a breathing belt is arranged in the center of the abdomen 305 for monitoring respiratory movements and respiratory rate; and a temperature sensor is placed on the forehead 306 for body temperature monitoring. The physiological signals collected by each sensor are converged to the data aggregation node 307 through the data transmission path (shown by the dotted line in the figure). This layout scheme realizes the synchronous acquisition of four key physiological parameters: blood oxygen, ECG, respiration, and body temperature. The sensor positions are optimized to ensure signal acquisition quality while taking into account wearing comfort and convenience for daily activities.

[0048] Figure 12 This is a structural diagram of a data acquisition terminal provided in an embodiment of the present invention. Figure 12 As shown, the data acquisition terminal includes a housing 401, inside which is a main control board 402. The main control board 402 integrates a signal conditioning circuit 403, an analog-to-digital converter module 404, a data processing chip 405, and a storage module 406. The signal conditioning circuit 403 is located on the left side of the main control board 402 and is used to filter and amplify the sensor input signal. The analog-to-digital converter module 404 is located to the right of the signal conditioning circuit 403 and is used to convert analog signals into digital signals. The data processing chip 405 is located at the core of the main control board 402 and is used to execute data processing and prediction algorithms. The storage module 406 is located below the data processing chip 405 and is used to cache historical data and prediction results. The wireless communication module 407 is located on the right side of the main control board 402 and is used for wireless data transmission with the monitoring terminal. The power management unit 408 is located at the bottom of the housing 401 and provides stable power to all modules. The sensor interface 409 is located on the left outer edge of the housing 401 and is used to connect various physiological sensors; the indicator panel 410 is located on the top outer edge of the housing 401 and is used to display the working status of the device.

[0049] like Figure 13As shown, the monitoring system of this invention adopts a hierarchical physical deployment architecture. Multiple beds are set up in the ward area 501, each equipped with a bedside monitoring device 502 for real-time collection of patients' physiological parameter data. The bedside monitoring device 502 is connected to a ward data aggregator 503 located in the corridor or nurse station via a wired network connection 504, realizing local aggregation of monitoring data within the ward. The ward data aggregator 503 transmits the aggregated data to the hospital data center 505 via a backbone network 506. A predictive analysis server 507 is deployed in the hospital data center 505, responsible for running respiratory failure prediction algorithms and performing real-time analysis and risk assessment on the received monitoring data. The prediction results are distributed via network to the doctor's workstation 508 in the clinical area and mobile terminal devices 509 carried by medical staff, enabling clinicians to obtain timely risk warning information for patients. Simultaneously, a warning display screen 510 is set up in the corridor or nurse station to display the warning status of patients in the ward in real time, facilitating rapid response by nursing staff. This physical deployment scheme realizes a complete data flow link from bedside data collection, ward data aggregation, centralized predictive analysis to multi-terminal early warning push, ensuring the high reliability and low latency response of the monitoring system.

[0050] In one possible implementation, a monitoring system continuously collects physiological indicators of patients with respiratory failure, such as heart rate, blood oxygen saturation, and respiratory rate. These indicators are converted into a data sequence in real time, for example, heart rate values ​​are collected once per second, forming a continuous numerical stream. The system uses sensor devices such as pulse oximeters and respiratory monitors, connected to a central processing unit, to ensure the accuracy and continuity of data acquisition. This approach can promptly capture changes in the patient's condition, providing a basis for subsequent analysis.

[0051] The historical prediction results in the caching module are stored in JSON format, with fields including: time_stamp (time stamp, accurate to the second), indicator_name (indicator name), historical_prediction (historical prediction value), and confidence (prediction confidence level). The mechanism for connecting real-time data sequences and historical data is as follows: alignment is performed based on the timestamp, and automatic matching and fusion are performed when the timestamp deviation between real-time data and historical data is ≤2 seconds. Data format compatibility processing adopts standardized conversion, uniformly converting the output data of different sensors into floating-point values ​​(with precision retained to 2 decimal places).

[0052] For example, for the collected real-time data sequence, pre-established analysis rules are used to determine whether the changes have reached a preset threshold.

[0053] Specifically, the analysis rules might include a sliding window algorithm that calculates the difference between adjacent time points in the data sequence. If the blood oxygen saturation drops from 95% to 90% within 5 seconds and the change exceeds a threshold, such as 3%, it is considered to have reached the threshold; otherwise, monitoring continues. This rule sets thresholds based on clinical standards to help quickly identify acute exacerbations.

[0054] In one possible implementation, if the data sequence change does not reach a preset threshold, the incremental calculation module is activated to extract relevant historical data from the cached prediction and obtain preliminary processing results.

[0055] For example, the incremental calculation module is an efficient algorithm that only processes new data without recalculating the entire history. It retrieves the average blood oxygen data from the past hour from the cache, such as 92%, and combines it with the current sequence to calculate a preliminary prediction, such as expecting it to stabilize at 91% in the next minute. This module reduces the computational load and improves real-time performance.

[0056] For example, based on the preliminary processing results of the incremental calculation module and combined with the current real-time data sequence, a local correction operation is performed to determine the corrected predicted value.

[0057] Specifically, local corrections may use a weighted average method, combining the preliminary result (e.g., 91%) with the current data (e.g., 90.5%) to correct to 90.8%, considering weights such as 0.7 for historical data and 0.3 for current data to ensure the prediction more closely reflects actual changes. This operation adjusts for local fluctuations, avoiding overreaction. The mathematical model of the weighted average method is: Correction value = Historical predicted value × α + Current physiological indicator data × (1-α), where α is the weight of historical data, ranging from 0.6 to 0.8, dynamically adjusted according to the stability of the patient's condition (α=0.8 when the condition is stable, α=0.6 when the condition fluctuates slightly); outlier handling adopts the 3σ criterion, when the current physiological indicator data exceeds the mean ± 3σ range of the historical data sequence, the weight α is automatically adjusted to 0.3, prioritizing the reference to the current real-time data.

[0058] In one possible implementation, the output is updated using the corrected predicted values ​​to generate a description of the physiological indicators at the current time point.

[0059] For example, based on a correction value of 90.8%, the system generates a description such as "Current blood oxygen saturation is stable, and no acute risk is expected in the short term," and updates it to the display interface or report. This update provides an intuitive overview of the status, facilitating rapid decision-making by healthcare professionals.

[0060] For example, the updated output content can be obtained, combined with historical data sequences from the monitoring system, and a support vector machine model can be used to predict short-term trends in the future to obtain prediction results.

[0061] Specifically, Support Vector Machines (SVMs) are machine learning models that construct hyperplanes to separate data categories. They take historical sequences, such as heart rate data from the past 24 hours, as input and a current description, and after training, predict trends for the next 10 minutes, such as "heart rate may rise to 100 beats per minute." The model's principle is to maximize the margin to classify normal and abnormal trends. It is suitable for non-linear data and uses kernel functions, such as radial basis functions, to handle complex patterns. This predictive power enhances foresight and allows for early intervention in potential problems.

[0062] In one possible implementation, if an abnormal trend is detected in the prediction results, an early warning mechanism is triggered to automatically record the abnormal data and store it in a designated database.

[0063] For example, if the predicted heart rate rises above a threshold such as 110 beats per minute, the system triggers an alarm, records data such as timestamps, values, and trend descriptions, and stores this data in a cloud database for subsequent auditing and analysis. This mechanism ensures timely response to anomalies and improves patient safety.

[0064] S102. Based on the local correction and update output analysis, analyze the variance of disease fluctuation and use support vector machine to classify the variance level of disease fluctuation and determine the prediction step size selection value.

[0065] The current disease progression data is acquired and local corrections are performed to obtain the corrected sequence. The variance of each segment of the corrected sequence is calculated to obtain the fluctuation variance of each segment. The fluctuation variances of each segment are combined into a complete fluctuation variance sequence. The fluctuation variance sequence is classified using a support vector machine to obtain the level category of the variance. The corresponding step size mapping table is queried according to the level category of the variance. If the level category of the variance is low, the first preset step size value is selected. If the level category of the variance is medium, the second preset step size value is selected. If the level category of the variance is high, the third preset step size value is selected. The selected step size value is determined as the step size used for the current prediction. The step size value used for the current prediction is output to the subsequent prediction process.

[0066] like Figure 7 The figure shows the variation curve of the variance of disease fluctuations over a 24-hour period and its classification regions. The horizontal axis represents time (0-24 hours), and the vertical axis represents the variance value (0-20). Based on the variance value, the degree of disease fluctuation is divided into three regions: low-level region (variance value less than 5), medium-level region (variance value between 5 and 15), and high-level region (variance value greater than 15). The two dashed lines represent the low-level threshold (V=5) and the high-level threshold (V=15), respectively, used to distinguish different levels of fluctuation.

[0067] The graph marks five typical fluctuation points: the low fluctuation point (4h, 5.5) is located in the early morning, indicating a relatively stable condition; the rising point (7h, 12.0) shows that the fluctuation begins to increase in the morning; the peak point (13h, 14.8) is close to the high-level threshold, indicating that the fluctuation is more severe at noon; the medium point (17h, 6.0) shows that the fluctuation returns to a medium level in the afternoon; and the falling point (22h, 3.8) is located in the low-level area at night, indicating that the condition tends to stabilize. Different shapes of markers are used to distinguish the areas: circular markers represent points in the medium-level area, and square markers represent points in the low-level area.

[0068] This graph visually illustrates the periodic changes in the variance of the patient's condition over time, providing a quantitative basis for clinical medical staff to assess the stability of the patient's condition and helping to develop targeted nursing interventions.

[0069] In one possible implementation, when acquiring current disease progress data, physiological indicators such as heart rate, blood oxygen saturation, and respiratory rate can be extracted from the monitoring system of patients with respiratory failure. These data are usually recorded in the form of timestamps to form a continuous numerical sequence.

[0070] For example, when processing the course data of a critically ill patient, real-time monitoring values ​​over the past 24 hours are first collected, such as heart rate fluctuations from 80 beats / min to 120 beats / min. Then, local correction processing is performed, which involves using a sliding window algorithm to smooth out anomalies in the sequence.

[0071] Specifically, the local correction process involves identifying noise or missing values ​​in the sequence. For example, if blood oxygen saturation suddenly drops from 95% to 85%, but surrounding data remains stable at around 92%, a weighted average method is applied to correct the outlier to the weighted value of neighboring points, resulting in a smoother corrected sequence. This correction helps reduce the impact of data noise on subsequent analysis, ensuring the sequence reflects the true physiological trend. In business terms, this process improves prediction accuracy and avoids misjudgments caused by data errors. The process of calculating the variance segment by segment for the corrected sequence involves dividing the sequence into fixed-length segments, such as each containing 10 data points, and then calculating the variance of each segment.

[0072] For example, for a corrected heart rate sequence, assuming the first segment of data is [85, 88, 90, 92, 95, 93, 91, 89, 87, 86], variance calculation involves taking the average of these points and then averaging the sum of squared deviations point by point, resulting in a variance of approximately 8.25 for this segment. This segment-by-segment calculation captures the intensity of local fluctuations. To assemble a complete variance sequence from the variances of each segment, simply arrange the calculated variance values ​​in order to form a new sequence, such as [8.25, 12.4, 6.7, 15.3].

[0073] In one possible implementation, a Support Vector Machine (SVM) is used to classify the volatility variance sequence. First, it's important to understand that an SVM is a supervised learning model that separates data into different classes by finding the maximum margin hyperplane. In this scenario, the process of classifying the volatility variance sequence includes a training phase and a prediction phase.

[0074] For example, the model is pre-trained using historical patient data, and variance sequences are labeled as low, medium, and high-level categories. During training, input features such as the mean, maximum value, and trend slope of the sequence are used. After learning, the model can classify new sequences. The training data for the support vector machine classifier consists of 24-hour continuous physiological data (including heart rate, blood oxygen saturation, respiratory rate, and arterial blood gas parameters) from 1000 patients with respiratory failure. The training process uses a radial basis function as the kernel function, with a penalty coefficient C=1.0 and a gamma parameter=0.1. The classification thresholds for fluctuation levels are: low-level fluctuation variance <5, medium-level fluctuation variance 5-15, and high-level fluctuation variance >15. The corresponding prediction step size mapping table is: low level → first preset step size 8 hours, medium level → second preset step size 4 hours, and high level → third preset step size 2 hours.

[0075] Specifically, for a variance sequence [8.25, 12.4, 6.7, 15.3], the support vector machine calculates its position in the feature space. If it is located in a low-level region, it outputs a low-level category. This classification helps quantify the degree of volatility and provides a basis for subsequent decision-making. The process of querying the corresponding step size mapping table based on the variance's level category can be pre-defined, where low level corresponds to step size 1, medium level corresponds to step size 3, and high level corresponds to step size 5.

[0076] For example, if the classification result is at a medium level, a second preset step size value, such as 3, is directly selected from the table. If the variance belongs to a low level category, a first preset step size value is selected, for example, 1, which means using a smaller step size to capture subtle changes during prediction. If it is at a medium level, a second preset step size value, such as 3, is selected, suitable for scenarios with moderate fluctuations. If it is at a high level, a third preset step size value, such as 5, is selected to deal with drastic fluctuations.

[0077] In one possible implementation, the selected step size is determined as the step size used for the current prediction and then output to subsequent prediction processes. For example, a step size of 5 is passed to the time series prediction model to adjust the prediction window size. In business implementation, this dynamic step size selection can optimize prediction efficiency. For instance, using a larger step size during periods of high volatility allows for faster response to potential risks, enabling timely intervention in respiratory failure management and resulting in more accurate early warning effects.

[0078] S103. After obtaining the prediction step size selection value, fuse the historical cache features. If the prediction step size selection value is a short step size, adjust the cache window setting to expand to a twelve-hour range to obtain the fused window data.

[0079] The prediction step size selection module outputs the prediction step size value. Based on this value, it's determined whether the current step size is short. If so, a cache window expansion operation is triggered. Historical cached data within a twelve-hour range is read from the expanded cache window. A feature extraction process is performed on the read historical cached data to obtain historical feature vectors. These historical feature vectors are then concatenated and fused with the current prediction data. The resulting fused, complete window data is used by subsequent prediction models.

[0080] like Figure 2 As shown, the acute exacerbation prediction method for respiratory failure of this invention adopts a hierarchical data flow architecture. Starting from the data input layer, the system continuously receives real-time data sequences from patients with respiratory failure, including physiological indicators such as heart rate, blood oxygen saturation, and respiratory rate. After the real-time data enters the data processing layer, it is first locally corrected and updated by the incremental calculation module. This module processes only newly added and changed data in an incremental manner, avoiding the resource waste caused by full calculation. The output of the incremental calculation module is passed to the variance analysis of the patient's condition fluctuation, where a support vector machine classifier is used to classify the level of fluctuation variance. In the branch decision layer, the prediction step size selection module determines the prediction step size value based on the variance level classification result. When it is determined to be a short step size, the left branch is triggered, expanding the cache window to a twelve-hour range to obtain more complete historical data; when it is determined to be a long step size, the right dotted line branch is followed, maintaining the standard window configuration. Finally, the data fusion layer is responsible for integrating the historical data in the cache window with the current prediction data. The historical cache feature fusion module completes the data splicing process and outputs the fused complete window data for subsequent prediction models.

[0081] Specifically, the prediction step size selection module is a component used to dynamically adjust the prediction time span. It outputs an appropriate step size value by analyzing the volatility of disease data. For example, in a chronic disease monitoring system, this module calculates the step size based on the degree of variation of indicators such as the patient's recent blood pressure and heart rate. If the volatility is small, it outputs a longer prediction interval to reduce the computational burden.

[0082] For example, in a scenario where a heart patient is being managed, the system first obtains a step value, such as 6 hours, from the module, and then determines whether it falls into the short step range. Assuming that a short step is defined as less than or equal to 3 hours, when the step value is 2 hours, it is considered a short step. At this point, the system will automatically trigger a cache window expansion operation to include more historical data to improve prediction accuracy.

[0083] In one possible implementation, the cache window expansion operation involves extending the standard window from the original 6 hours to 12 hours or longer. For example, for blood glucose monitoring of a diabetic patient, the expanded window can cover the complete cycle of data back from the current time, thereby capturing subtle changes caused by diet or exercise and avoiding missing key patterns due to an overly narrow window.

[0084] For example, historical cached data within a twelve-hour range can be read from the expanded cache window. This includes extracting a patient's continuous blood glucose readings, insulin injection records, and activity logs over the past 12 hours. For instance, a patient's blood glucose curve data from 8 a.m. to 8 p.m. can be read. This data is stored in the database in time series format, and the system filters out the precise range by querying the timestamp to ensure the data is complete.

[0085] Specifically, a feature extraction process is performed on the read historical cache data to obtain historical feature vectors. The feature extraction process here refers to using statistical methods such as mean, standard deviation, or frequency domain analysis to extract the essence of the data. For example, the average, peak, and volatility of blood glucose data are calculated to form a multi-dimensional vector such as [average blood glucose 120 mg / dL, standard deviation 15, peak 150]. These vectors represent a condensed representation of historical data, which is convenient for subsequent processing.

[0086] For example, historical feature vectors are concatenated and fused with current prediction data. This step aims to integrate past and present information. For instance, historical vectors [120,15,150] are concatenated with current real-time data such as current blood glucose of 130 mg / dL to form [120,15,150,130]. ​​The fusion may involve weighted averaging or simple connections to form a unified input sequence, ensuring that the model can take continuity into account.

[0087] In one possible implementation, the fused complete window data is used by subsequent predictive models. This complete window data, such as an extended 12-hour sequence, can help models like neural networks better predict future blood glucose trends. For example, in patient management, this can help provide early warning of hypoglycemic events, thereby guiding timely intervention and adjustment of treatment plans.

[0088] S104. Calculate and update the data dimension for the fused window data, and use random forest to evaluate the degree of fusion of multi-source information to determine the timing of key node triggering.

[0089] The system acquires multi-source information data within the fusion window, calculates and updates the dimension set, inputs the dimension set into a random forest model, uses the random forest to obtain the fusion quality assessment value, determines whether the fusion quality assessment value reaches a preset threshold, and if the fusion quality assessment value reaches the preset threshold, then the current moment is determined as a key node, and trigger signals are output based on the determined key node to control the subsequent processing flow.

[0090] Specifically, this can involve acquiring multi-source information data within the fusion window; calculating an updated dimension set through data computation; inputting the updated dimension set into a random forest model; using the random forest model to calculate data integrity score, dimension consistency score, and temporal consistency score, and weighting them to obtain a fusion quality assessment value; determining whether the fusion quality assessment value reaches a preset threshold (0.8); if the fusion quality assessment value reaches the preset threshold, then determining the current moment as a key node; and outputting a trigger signal based on the determined key node (signal format is JSON: {"key_node_time":"YYYY-MM-DD HH:MM:SS","trigger_action":"start_conflict_detection"}) to control the subsequent processing flow.

[0091] In one possible implementation, the system first needs to acquire multi-source information data from a fusion window. This data may come from different sensors or databases. For example, in a smart grid system, the fusion window can be defined as time-series data from the most recent hour, including voltage fluctuation records, current intensity readings, and external weather influence parameters. The system then retrieves this multi-source information from distributed storage via a query interface, ensuring data timestamp alignment to avoid synchronization issues.

[0092] Specifically, assuming the fusion window is set to 60 minutes, the system will scan entries in the database labeled "Voltage Data Source A" and "Weather Data Source B," reading raw records such as voltage value sequences [220, 218, 225...] and wind speed sequences [5, 6, 4...]. Then, it will perform preliminary cleaning to remove noise points, thus forming a multidimensional dataset for subsequent calculations.

[0093] For example, the next step involves calculating the updated dimension set through data computation. This step involves feature engineering the acquired multi-source data. In power grid monitoring, principal component analysis (PCA) can be used to reduce and update dimensions. For instance, using the original voltage, current, and wind speed data as input, the covariance matrix is ​​calculated, and the first three principal components are extracted to form an updated dimension set such as {Dimension 1: Voltage-Wind Speed ​​Correlation, Dimension 2: Current Fluctuation Rate, Dimension 3: Time Decay Factor}. Specifically, this process involves first standardizing all data to a scale with a mean of 0 and a variance of 1, then calculating eigenvectors and selecting dimensions with a cumulative variance contribution rate exceeding 85%, thus obtaining a refined set and avoiding redundancy in the original data.

[0094] In one possible implementation, this set of updated dimensions is fed into a random forest model, where a random forest is an ensemble learning algorithm consisting of multiple decision trees, each trained independently and voting to produce the output.

[0095] Specifically, the principle of random forests is to use bootstrapping sampling to randomly select a subset from the updated dimension set. For example, 700 samples are selected from 1000 samples for training a single tree. Simultaneously, a subset of dimensions, such as using only dimensions 1 and 3, is randomly selected to construct tree node splits. This allows multiple trees (e.g., 500 trees) to collectively reduce the risk of overfitting. In the power grid scenario, the input updated dimension set is used by the model for classification or regression tasks to evaluate the quality of the fused data.

[0096] For example, the process of obtaining a fusion quality assessment value using random forests involves the model making predictions on the input set, such as each tree outputting a score, and then taking the average as the final assessment value. In practical applications, if the set of dimensions represents data consistency, the model might output a value between 0 and 1, such as 0.85, indicating high fusion quality. The calculation details of this assessment value include using Gini impurity or mean squared error as splitting criteria at tree nodes. For example, for regression tasks, the tree recursively splits dimensions until the minimum number of samples in the leaf nodes reaches 5, and then aggregates the predictions from all trees. The calculation rule for the fusion quality assessment value is as follows: Fusion quality assessment value = 0.4 × data integrity score + 0.3 × dimensional consistency score + 0.3 × temporal continuity score; where data integrity score = effective data volume / total data volume (effective data refers to data without missing or outliers), dimensional consistency score = mean correlation coefficient of multi-source data (absolute correlation coefficient ≥ 0.5 is considered consistent), and temporal continuity score = the proportion of data change rate at adjacent time points ≤ preset threshold (physiological indicator change rate ≤ 10% / minute); the preset threshold is set to 0.8, and when the fusion quality assessment value ≥ 0.8, it is determined that the fusion effect meets the standard.

[0097] In one possible implementation, it is determined whether the fusion quality assessment value reaches a preset threshold, for example, the threshold is set to 0.8. If the calculated value of 0.85 exceeds the threshold, then proceed to the next step.

[0098] Specifically, this judgment can be embedded in the if conditional logic, implemented in the code using comparison operators, and the result can be logged for auditing purposes.

[0099] For example, if the fusion quality assessment value reaches a preset threshold, the current moment is designated as a critical node. In a power grid system, this means marking the current timestamp, such as "2023-10-01 14:00," as critical for subsequent alarm triggering. This determination process updates a list of nodes, ensuring that only high-quality fusion moments are selected, thereby improving the reliability of predictions in business operations.

[0100] In one possible implementation, subsequent processing flows are controlled by triggering signals output from identified key nodes. For example, outputting a JSON-formatted signal {"node":"2023-10-01 14:00","action":"start_prediction"} would activate downstream prediction model modules. In power grid monitoring, this signal can control the process to shift towards real-time load prediction, thereby optimizing resource allocation and avoiding erroneous decisions caused by low-quality data.

[0101] S105. Extract update granularity conflict indicators from the key node triggering time. If the update granularity conflict indicators are higher than the preset threshold, start the incremental calculation module to perform secondary local correction update and merge historical cache features to obtain the conflict mitigation sequence.

[0102] Step 1: Obtain trigger timing data from key nodes. By analyzing changes in node states, determine the specific time point of the trigger timing. Step 2: For the trigger timing data, obtain relevant information on the update granularity. Use a hierarchical parsing method to determine the range and level of the update granularity. Step 3: Calculate the conflict index value based on the update granularity information. If the conflict index exceeds a preset threshold, initiate the incremental calculation module for subsequent processing. Step 4: Through the incremental calculation module, obtain the data range required for local correction. Combine the trigger timing and update granularity to determine the priority area for correction. Step 5: For the priority area for local correction, extract relevant data from the historical cache. Use data fusion technology to obtain the corrected intermediate result. Step 6: Based on the intermediate result, generate a conflict mitigation sequence. Through serialization processing, determine whether the mitigation sequence meets preset conditions and output the final sequence.

[0103] In one possible implementation, when acquiring trigger timing data from key nodes, historical state records of the nodes can be collected first. For example, in an intelligent transportation system, key nodes may correspond to traffic light switching points at intersections. By analyzing the change in the traffic light state from red to green, the specific time when the peak traffic flow occurs can be determined, such as 8:15 AM. This analysis process involves comparing the differences between the states before and after, ensuring that the timing data reflects real-time dynamics, thus providing an accurate basis for subsequent processing.

[0104] For example, when obtaining information on update granularity for these trigger timing data, a hierarchical parsing method is adopted. First, the data is divided into a macro layer and a micro layer. In a supply chain management system, the macro layer may involve the update range of the entire warehouse inventory, while the micro layer focuses on the batch level of a single product. By parsing layer by layer, the specific range and level of update granularity from the warehouse level to the product level can be determined, which helps to refine the accuracy of data processing.

[0105] In one possible implementation, the value of the conflict index is calculated based on the updated granularity information. For example, on a financial trading platform, if the updated granularity shows a hierarchical conflict in transaction records, such as a mismatch between the granularity of buy and sell orders, the conflict index is calculated to be 0.75. If it exceeds the preset threshold of 0.5, the incremental calculation module is activated. This module processes only the changed parts in an incremental manner, avoiding the waste of resources from full calculation, thereby improving the system response speed.

[0106] For example, when obtaining the data range required for local correction through the incremental calculation module, the triggering time and update granularity are combined. In a medical image analysis system, the triggering time may be the moment of anomaly detection, and the update granularity is at the image pixel level. Then, the priority area for correction is determined to be the focal area of ​​the lesion in the image. This combination ensures that the correction is highly targeted and reduces the interference of irrelevant data.

[0107] In one possible implementation, for priority areas of local correction, relevant data is extracted from historical cache and data fusion technology is used. For example, in meteorological forecasting, temperature and humidity data from the past 24 hours are extracted from the cache and fused with real-time observations of the current area to obtain corrected intermediate results such as fused temperature fields. The technical goal of this step is to improve data accuracy, which can bring more reliable forecast results in operations and reduce decision-making errors caused by errors.

[0108] For example, a conflict mitigation sequence is generated based on intermediate results. The sequence is then processed to determine whether it meets preset conditions. In network security monitoring, the sequence may include an order of alarms from high risk to low risk. After serialization, the total length of the sequence is checked to see if it is less than 10. If it is, the final sequence is output to guide the response team to prioritize high-risk items. This logical progression ensures the coherence and effectiveness of the overall process.

[0109] S106. Use the conflict mitigation sequence to reset the cache window, obtain the expanded window data, determine whether the window expansion requirement meets the global adaptive requirement, and obtain the final prediction result.

[0110] The initial state of the cache window is handled through a conflict mitigation mechanism to obtain adjusted window configuration data and determine the initial window range. Based on the adjusted window configuration data, a sequence adjustment method is used to optimize the cache window, resulting in a serialized window structure. For the serialized window structure, necessary data for window expansion is obtained, and it is determined whether the expanded data conforms to preset expansion standards. If the expanded data conforms to the preset expansion standards, the expanded window data is extracted through a data acquisition process to determine the expanded data set. Based on the expanded data set, a global adaptive algorithm is used to conditionally judge the window data, obtaining adaptive matching results. Using the adaptive matching results, the final prediction result data is obtained to determine whether the prediction result meets the global adaptive requirements.

[0111] like Figure 6 As shown, the dynamic adjustment process of the cache window in this invention is illustrated using a multi-channel timing diagram. This timing diagram contains five parallel channels: a data input layer, a prediction step size layer, a window state layer, a trigger event layer, and a system state layer. The timeline extends from T0 to T8, demonstrating the complete dynamic adjustment cycle. In the data input layer, real-time data is sampled in the form of irregularly spaced pulses, with data packets arriving at specific times. The prediction step size layer demonstrates the step size's step-wise changes, switching from 8 hours (long step size) to 4 hours (medium step size) and 2 hours (short step size), dynamically adjusting according to prediction requirements. The window state layer uses the height of rectangles to represent changes in window size, with the window expanding from 6 hours to 12 hours, 24 hours, and then shrinking back to 6 hours; the expansion and shrinkage directions are marked with arrows. The trigger event layer records four event types: threshold triggering, conflict detection, window expansion, and state stabilization, each with a priority label. The system state layer demonstrates the cyclical changes of the state machine: monitoring, prediction, correction, and verification. Vertical correlation lines connect the corresponding events at critical moments (T1, T2, T5, T6) in each layer. The labeled boxes explain the reasons for key state transitions such as step size shortening triggering window adjustment and threshold triggering causing the window to expand to 24 hours. This sequence diagram fully presents the linkage adjustment mechanism between the cache window and the prediction step size.

[0112] In one possible implementation, when handling the initial state of the cache window through a conflict mitigation mechanism, it is first necessary to understand the principle of the conflict mitigation mechanism. It is essentially a dynamic adjustment strategy used to detect and mitigate potential conflicts in the data cache caused by concurrent access or updates.

[0113] Specifically, in a real-time inventory management system of an e-commerce platform, when multiple users simultaneously query the inventory of the same product, the initial cache window may conflict due to data synchronization delays. In this case, the mechanism scans the start and end boundaries of the window, calculates the probability of conflict, and if the probability exceeds a threshold, automatically injects a buffer to isolate the source of conflict. This allows for the acquisition of adjusted window configuration data, such as expanding the window size from the default 10 seconds to 30 seconds to ensure data consistency and determine the initial window range, such as covering transaction records from the most recent hour. This step not only reduces data read errors but also improves system stability and avoids overselling of inventory due to conflicts.

[0114] For example, in the process of optimizing the cache window using the sequence adjustment method based on the adjusted window configuration data, the sequence adjustment method can be regarded as a time series-based optimization algorithm that minimizes redundancy by reordering the data elements in the window.

[0115] Specifically, in the real-time quote system of a financial trading platform, the adjusted configuration data includes the window's sampling frequency and data granularity. The method iterates through the quote points in the sequence one by one, applying a smoothing function to remove noise, such as a weighted average of continuous quote fluctuations, thus obtaining a serialized window structure, like an ordered list of quote sequences, where each element contains a timestamp and an adjusted price value. This optimization effectively compresses data volume and improves the efficiency of subsequent processing.

[0116] In one possible implementation, when obtaining the necessary data for window expansion for a serialized window structure, it is necessary to determine whether the expanded data meets the preset expansion criteria. Here, the expansion criteria usually include data correlation and integrity thresholds. For example, in medical and health monitoring applications, the serialized window structure stores the patient's heart rate sequence. The necessary data may involve external sensor inputs, such as blood pressure values. The judgment process will calculate the correlation coefficient. If the coefficient is greater than 0.8, it meets the criteria; otherwise, it is discarded to prevent the introduction of irrelevant noise.

[0117] For example, if the extended data conforms to the standard, the extended window data is extracted through the data acquisition process to determine the extended data set.

[0118] Specifically, in traffic flow prediction in intelligent transportation systems, the data acquisition process involves querying historical traffic records from a database and integrating them with the current sequence. For example, the vehicle count sequence of the original window can be expanded into a composite sequence that includes weather factors. The resulting dataset is like a multidimensional array that covers time, location, and external variables, thus providing a comprehensive foundation for subsequent analysis.

[0119] In one possible implementation, when using a global adaptive algorithm to make conditional judgments on window data based on the expanded dataset, the global adaptive algorithm is an iterative optimization framework that dynamically adjusts parameters through a feedback loop to match global conditions.

[0120] Specifically, in inventory forecasting on a supply chain management platform, the algorithm traverses the dataset, applies conditional rules such as increasing weights if inventory falls below a warning line, and gradually calculates matching scores to obtain adaptive matching results. For example, a score vector represents the fitness of each window segment, which helps identify potential supply chain bottlenecks.

[0121] For example, when obtaining the final prediction data through adaptive matching results, it is necessary to determine whether the prediction results meet the global adaptive requirements. In the production scheduling system of the manufacturing industry, the matching results are used as input to the prediction model, such as a simplified version of linear regression, to generate future output predictions. If the prediction deviation is less than 5%, the requirement is met; otherwise, iterative adjustments are made to ensure the accuracy of business decisions and to maximize resource utilization technically.

[0122] S107. By retrospectively verifying the matching degree between the variance of the disease fluctuation and the selected prediction step size through the final prediction results, a confirmation signal that the overall execution conflict has been resolved is determined.

[0123] By extracting historical data on disease fluctuations and employing time series analysis, key features of the fluctuation range are identified, resulting in a preliminary description of the fluctuation range. Based on this description, a retrospective analysis of the prediction results is conducted, using a pre-established variance change calculation model to determine the distribution of variance changes. If the variance change distribution exceeds a preset threshold, an adaptation test is performed on the prediction step size to obtain a reference for step size adjustment and determine the direction of the adjustment. Based on the direction of the step size adjustment and the selection of adaptation logic rules, a new prediction step size configuration is generated, resulting in an adapted step size scheme. For the adapted step size scheme, a simulation test of overall execution is performed to obtain preliminary feedback on conflict resolution and determine whether the conflict has been alleviated. If the feedback on conflict resolution meets preset standards, a final verification process is conducted, combining execution confirmation indicators to obtain a final judgment on the overall execution conflict resolution.

[0124] For example, in the field of healthcare monitoring, data on blood pressure fluctuations are extracted from patients' historical electronic medical records. These records include daily blood pressure measurements over the past year, along with related life event logs such as dietary changes or adjustments to exercise intensity. By employing time series analysis methods, such as using an autoregressive integral moving average model, these time-series data are processed to extract key features of the fluctuation range, such as peak blood pressure, trough values, and average deviation, thus obtaining a preliminary description of the fluctuation range. This description might show that the patient's blood pressure fluctuates more at night, with an average deviation reaching 15 mmHg, revealing a potential risk of nocturnal hypertension.

[0125] In one possible implementation, based on the above description of the fluctuation range, a retrospective analysis of previous blood pressure prediction results is performed. A pre-established variance change calculation model is used, which is based on statistical principles. By calculating the variance of data points in each time period, for example, dividing the data into weekly units, calculating the variance value of weekly blood pressure data, and observing its trend over time, the distribution of variance change is determined. For example, if the variance gradually increases from the initial low value and the distribution shows a positive skewness, it indicates that the prediction model may have a bias in the long-term trend and needs further adjustment.

[0126] For example, if the distribution of variance changes exceeds a preset threshold range, such as a threshold set to a variance growth rate of no more than 20%, but the actual calculation shows a growth rate of 35%, then an adaptation test is performed on the prediction step size. This test involves comparing the matching degree between the current step size, such as daily predictions, and historical data to obtain a reference basis for step size adjustment. For example, the root mean square value of the prediction error can be used to determine this. If the error is large, then the direction of step size adjustment is to shorten the step size to improve short-term accuracy.

[0127] In one possible implementation, by adjusting the step size direction, such as deciding to shorten the original daily step size to half-days, and combining this with the selection of appropriate logical rules, which may include conditional branches based on patient age and medical history, such as prioritizing conservative adjustments for elderly patients, a new predicted step size configuration is generated, resulting in an adapted step size scheme. This scheme ensures that the prediction is more in line with the individual fluctuation pattern, thereby improving the timeliness of personalized medical intervention in business operations.

[0128] For example, for the adapted step size scheme, a simulation test of the overall execution is performed. Historical data is input into a virtual environment to simulate blood pressure prediction for the next week and obtain preliminary feedback on conflict resolution. For example, it is checked whether there are still conflict points where the predicted value does not match the actual fluctuation, and whether the conflict has been alleviated. If the feedback shows that the conflict points have been reduced by 80%, it indicates that it is initially effective.

[0129] In one possible implementation, if the feedback on conflict mitigation meets the preset criteria, such as fewer than 5 conflict points, then through the final verification process, combined with the execution confirmation indicators, such as overall prediction accuracy and response time, a final judgment on the overall execution conflict resolution is obtained. This judgment helps to optimize the medical monitoring system, ensure that false alarms are reduced in actual deployment, and improve patient management efficiency.

[0130] This invention provides a respiratory failure acute exacerbation prediction system based on multi-data fusion analysis and deep learning, mainly comprising: a real-time data monitoring module, used to acquire real-time data sequences from changes in physiological indicators of respiratory failure patients through a monitoring system; if the data sequence shows changes that do not reach a threshold, an incremental calculation module is activated to process the cached prediction results and obtain a local correction update output; and a disease fluctuation analysis module, used to analyze the disease fluctuation variance based on the local correction update output, and use a support vector machine to classify the disease fluctuation variance level to determine the prediction step size selection value. The prediction step size selection module receives the variance level classification results output by the disease fluctuation analysis module, queries the preset step size mapping table, outputs the corresponding prediction step size value, and synchronizes the prediction step size value to the window data fusion module as a control signal for adjusting the cache window. The step size mapping table is stored in the system's local database and supports dynamic iterative optimization based on clinical data updates. The window data fusion module is used to fuse historical cache features after obtaining the selected prediction step size. If the selected prediction step size is a short step size, the cache window setting is adjusted to expand to a twelve-hour range to obtain fused window data. The key node triggering module is used to calculate the updated data dimension for the fused window data and use random forest to evaluate the degree of multi-source information fusion to determine the timing of key node triggering. The conflict detection and correction module is used to extract update granularity conflict indicators from the key node triggering timing. If the update granularity conflict indicators are higher than a preset threshold, the incremental calculation module is activated to perform secondary local correction and update the fused historical cache features to obtain a conflict mitigation sequence. The cache window optimization module is used to reset the cache window using the conflict mitigation sequence, obtain the expanded window data, determine whether the window expansion requirement meets the global adaptive requirement, and obtain the final prediction result. The backtracking verification module is used to backtrack and verify the matching degree between the variance of the disease fluctuation and the selected prediction step size through the final prediction result to determine the confirmation signal that the overall execution conflict has been resolved.

[0131] like Figure 5 As shown, this invention provides a multi-dimensional evaluation and comparison of prediction performance. Figure 5(a) Demonstrates the performance of the three schemes in four core metrics: prediction accuracy, sensitivity, specificity, and F1 score. The scheme of this invention achieves a prediction accuracy of 96.2%, sensitivity of 94.5%, specificity of 97.8%, and F1 score of 95.3%. All these metrics are superior to the fixed step size scheme (89.5%, 85.2%, 92.1%, and 88.4%, respectively) and comparable to or even slightly better than the full calculation scheme (95.8%, 93.2%, 97.1%, and 94.8%, respectively). Figure 5 (b) The false alarm rate comparison is shown. The false alarm rate of the present invention is only 3.2%, which is significantly lower than the 8.5% of the fixed step size scheme and basically the same as the 3.8% of the full calculation scheme. Figure 5 (c) This section shows a comparison of early warning times. The proposed solution can issue an early warning 45 minutes in advance, which is superior to the 25 minutes of the fixed step size solution and the 42 minutes of the full calculation solution. Overall, the proposed solution maintains high prediction accuracy while achieving a lower false alarm rate and earlier warning time, verifying the effectiveness of the adaptive sampling strategy.

[0132] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the concept of this application. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A method for predicting acute exacerbations of respiratory failure based on multi-data fusion analysis and deep learning, characterized in that, The method includes: The system acquires real-time data sequences from changes in physiological indicators of patients with respiratory failure. If the data sequence shows changes below a threshold, the incremental calculation module is activated to process cached prediction results and obtain a locally corrected update output. Based on the locally corrected update output, the variance of the patient's condition fluctuation is analyzed, and a support vector machine is used to classify the variance level to determine the prediction step size. After obtaining the prediction step size, historical cached features are fused. If the prediction step size is a short step, the cache window setting is adjusted and expanded to a twelve-hour range to obtain fused window data. The updated data dimension is calculated for the fused window data, and the degree of multi-source information fusion is evaluated using a random forest to determine the triggering timing of key nodes. Update granularity conflict indicators are extracted from the triggering timing of key nodes. If the update granularity conflict indicators are higher than a preset threshold, the incremental calculation module is activated to perform a secondary local correction update and fuse historical cached features to obtain a conflict mitigation sequence. The cache window is reset using the conflict mitigation sequence, and the expanded window data is obtained to determine whether the window expansion requirement meets the global adaptive requirement to obtain the final prediction result. The final prediction result is used to backtrack and verify the matching degree between the variance of the patient's condition fluctuation and the prediction step size selection, determining a confirmation signal that the overall execution conflict has been resolved.

2. The method according to claim 1, characterized in that, The system acquires real-time data sequences from changes in physiological indicators of patients with respiratory failure through a monitoring system. If the data sequence shows changes that do not reach a threshold, the incremental calculation module is activated to process the cached prediction results and obtain a locally corrected update output, including: The system monitors the physiological indicator data sequence of the respiratory failure patient in real time; determines whether the current change in the physiological indicator data sequence is less than a preset trigger threshold; if the current change in the physiological indicator data sequence is less than the trigger threshold, reads the existing historical prediction results from the cache module; inputs the historical prediction results into the incremental calculation module; applies the incremental influence of the current physiological indicator data to the historical prediction results through the incremental calculation module; calculates a local correction update output that better matches the current physiological state; and uses the local correction update output as the basis data for subsequent analysis of disease fluctuations.

3. The method according to claim 1, characterized in that, The step of analyzing the variance of disease fluctuation based on local correction and updating the output, and determining the prediction step size by using a support vector machine to classify the variance level of disease fluctuation, includes: Multiple feature parameters reflecting changes in patient status are extracted from the local correction update output; the variance of the multiple feature parameters within the most recent time window is calculated to obtain the variance of patient condition fluctuation; the variance of patient condition fluctuation is input into a support vector machine classifier; the fluctuation level to which the variance of patient condition fluctuation belongs is classified by the support vector machine classifier; the prediction step size selection value corresponding to the current fluctuation level is determined based on the classification result; the prediction step size selection value is used as the control parameter for subsequent cache window adjustment.

4. The method according to claim 1, characterized in that, After obtaining the prediction step size selection value, the historical cache features are fused. If the prediction step size selection value is a short step size, the cache window setting is adjusted to expand to a twelve-hour range to obtain the fused window data, including: The predicted step size value is output through the predicted step size selection module; Based on the predicted step size value, it is determined that the current step size is short. If it is a short step size, then a cache window expansion operation is triggered; Read historical cache data within a twelve-hour range from the expanded cache window; A feature extraction process is performed on the read historical cache data to obtain historical feature vectors; The historical feature vectors are concatenated and fused with the current prediction data; The fused, complete window data is then used by subsequent prediction models.

5. The method according to claim 1, characterized in that, The process of calculating and updating data dimensions for the fused window data, and determining the triggering timing of key nodes by evaluating the degree of multi-source information fusion using random forest, includes: Dimensional analysis is performed on the fused window data to calculate the number of information dimensions contained in the current data; the degree of fusion of multi-source information within the current window is evaluated using a random forest model; the relative importance ranking of each information source's contribution to the overall prediction is obtained; based on the relative importance ranking, it is determined whether the information fusion has reached a critical change point; when the information fusion reaches a critical change point, the current moment is determined as the critical node triggering time; the critical node triggering time is output as the triggering condition for subsequent conflict detection.

6. The method according to claim 1, characterized in that, The step involves extracting update granularity conflict indicators from the triggering timing of key nodes. If the update granularity conflict indicator is higher than a preset threshold, the incremental calculation module initiates a secondary local correction update to merge historical cache features and obtain a conflict mitigation sequence, including: Extract update granularity conflict indicators from the data points corresponding to the triggering time of the key node; compare the update granularity conflict indicators with a pre-set conflict judgment threshold; when the update granularity conflict indicators are higher than the conflict judgment threshold, reactivate the incremental calculation module; input the latest physiological indicator data and historical cache features into the incremental calculation module; perform a second round of local incremental correction calculation; fuse historical cache features with the current correction result; and generate a conflict mitigation sequence that can alleviate update granularity conflicts.

7. The method according to claim 1, characterized in that, The process of resetting the cache window using a conflict mitigation sequence, obtaining the expanded window data, determining whether the window expansion requirement meets the global adaptive requirement, and obtaining the final prediction result includes: The initial state of the cache window is handled through a conflict mitigation mechanism, the adjusted window configuration data is obtained, and the initial window range is determined. Based on the adjusted window configuration data, the cache window is optimized using the sequence adjustment method to obtain a serialized window structure; For the serialized window structure, obtain the necessary data for window expansion and determine whether the expansion data conforms to the preset expansion standard; If the extended data meets the preset extension criteria, the extended window data is extracted through the data acquisition process to determine the extended data set; Based on the expanded dataset, a global adaptive algorithm is used to perform conditional judgments on the window data to obtain adaptive matching results. By using the adaptive matching results, the final prediction result data is obtained, and it is determined whether the prediction result meets the global adaptive requirements.

8. The method according to claim 1, characterized in that, The process of backtracking and verifying the matching degree between the variance of the disease fluctuation and the selected prediction step size through the final prediction results to determine the confirmation signal that the overall execution conflict has been resolved includes: By obtaining historical data on the fluctuations in the patient's condition, and using time series analysis, key features of the fluctuation range were extracted to obtain a preliminary description of the fluctuation range. Based on the fluctuation range description, a retrospective analysis is performed on the prediction results, and the distribution of variance changes is determined using a pre-established variance change calculation model. If the distribution of variance changes exceeds the preset threshold range, an adaptation test is performed on the prediction step size to obtain a reference for step size adjustment and determine the direction of step size adjustment. By adjusting the direction of the step size and combining it with the logic rules for selection and adaptation, a new predicted step size configuration is generated, resulting in an adapted step size scheme. For the adapted step size scheme, perform a simulation test of the overall execution to obtain preliminary feedback on conflict resolution and determine whether the conflict has been alleviated; If the feedback on conflict mitigation meets the preset standards, the final judgment on the overall conflict resolution is obtained through the final verification process, combined with the indicators of execution confirmation.

9. A predictive system for acute exacerbations of respiratory failure based on multi-data fusion analysis and deep learning, characterized in that, The system includes: a real-time data monitoring module, used to acquire real-time data sequences from changes in physiological indicators of patients with respiratory failure through a monitoring system; if the data sequence shows changes that do not reach a threshold, the incremental calculation module is activated to process the cached prediction results and obtain a locally corrected update output; a disease fluctuation analysis module, used to analyze the variance of disease fluctuations based on the locally corrected update output, and use a support vector machine to classify the variance level of disease fluctuations to determine the selected prediction step size; a window data fusion module, used to fuse historical cached features after obtaining the selected prediction step size; if the selected prediction step size is a short step size, the cache window setting is adjusted to expand to a twelve-hour range to obtain fused window data; and a key node triggering module, used to calculate based on the fused window data. The system updates data dimensions and uses random forests to assess the degree of multi-source information fusion to determine the timing of key node triggers. A conflict detection and correction module extracts update-granularity conflict indicators from the key node trigger timings. If the update-granularity conflict indicator exceeds a preset threshold, the incremental calculation module initiates a secondary local correction update to fuse historical cache features and obtain a conflict mitigation sequence. A cache window optimization module resets the cache window using the conflict mitigation sequence, obtains expanded window data, determines whether the window expansion requirement meets global adaptive requirements, and obtains the final prediction result. A backtracking verification module verifies the matching degree between the variance of the disease fluctuation and the selected prediction step size using the final prediction result, confirming that the overall execution conflict has been resolved.

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