Medical infection risk intelligent early warning system based on artificial intelligence

Abstract

The invention relates to the technical field of medical infection early warning, and discloses a medical infection risk intelligent early warning system based on artificial intelligence. The system comprises a medical data acquisition unit, an infection risk analysis unit, an infection risk propagation simulation unit, a prevention and control response unit and a system validity verification unit. The medical data acquisition unit acquires physiological indexes, environmental microorganisms and medical operation record data of a patient in real time; the infection risk analysis unit performs feature extraction on the acquired data to generate an infection-related physiological index feature value, an environmental microorganism loading capacity feature value and a medical operation risk coefficient feature value; an infection risk propagation simulation unit calculates an infection risk propagation rate and establishes a group infection propagation kinetic model; the prevention and control response unit generates graded early warning signals and prevention and control strategy parameters according to the results; and the system validity verification unit monitors the operation of each unit, generates a stability evaluation index, and assists in improving the medical infection prevention and control level.

Description

Technical Field

[0001] This invention relates to the field of medical infection early warning technology, specifically to an intelligent early warning system for medical infection risks based on artificial intelligence. Background Technology

[0002] In current medical settings, infection control remains a crucial aspect of ensuring patient safety and maintaining medical order. Traditional infection control models rely heavily on the experience and judgment of healthcare workers and regular manual monitoring, which presents significant limitations and delays. Acquiring patient physiological data often requires timed collection, making real-time tracking impossible. When patients exhibit abnormal infection-related physiological indicators, healthcare workers may struggle to detect them promptly, potentially missing the optimal intervention window. Furthermore, the distribution of microorganisms in the hospital environment is complex and dynamically changing. Traditional environmental microbial testing methods are mostly sampling-based, with long testing cycles, making it difficult to comprehensively and in real-time reflect the environmental microbial load and providing timely environmental data for infection control.

[0003] Numerous infection risk factors exist during medical procedures, and the degree of infection risk varies across different procedures. However, traditional prevention and control models lack quantitative analysis of these risks, making it difficult to accurately identify high-risk procedures. Furthermore, traditional models rely heavily on experience-based judgments for infection risk analysis, failing to establish scientific models of group infection transmission and predict transmission trends. This results in a lack of targeted and forward-looking prevention and control strategies. When infection cases occur, only passive emergency measures are often implemented, which are insufficient to effectively contain the spread of infection.

[0004] With the advancement of medical informatization, some hospitals have begun to introduce data management systems. However, existing systems mostly focus on data storage and simple statistics, lacking the ability to deeply integrate and analyze multi-source medical data. They are unable to comprehensively assess infection risks from multiple dimensions such as physiological indicators, environmental microorganisms, and medical procedures, nor can they achieve real-time early warning and precise prevention and control of infection risks. These problems greatly restrict the efficiency and effectiveness of medical infection control work, making it difficult to meet the refined and intelligent needs of modern medicine for infection control. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent early warning system for medical infection risks based on artificial intelligence, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides an intelligent early warning system for medical infection risks based on artificial intelligence, the system comprising:

[0007] The medical data acquisition unit is used to acquire patient physiological indicator data, environmental microbial data, and medical operation record data in real time;

[0008] The infection risk analysis unit is used to extract features from the data acquired by the medical data acquisition unit and generate feature values ​​of infection-related physiological indicators, environmental microbial load, and medical operation risk coefficients.

[0009] The infection risk transmission simulation unit is used to calculate the infection risk transmission rate based on the characteristic values ​​of infection-related physiological indicators, environmental microbial load, and medical operation risk coefficient, and to establish a group infection transmission dynamics model based on the infection risk transmission rate.

[0010] The prevention and control response unit is used to generate graded early warning signals and prevention and control strategy parameters based on the infection risk transmission rate and the group infection transmission dynamics model output by the infection risk transmission simulation unit.

[0011] The system effectiveness verification unit is used to monitor in real time the data acquisition process of the medical data acquisition unit, the feature analysis process of the infection risk analysis unit, and the early warning response process of the prevention and control response unit, and to generate system operation stability assessment indicators.

[0012] Preferably, the infection risk analysis unit includes:

[0013] The physiological indicator risk conversion subunit is used to perform non-linear feature conversion on the patient's body temperature characteristic value, white blood cell count characteristic value and immunosuppressive drug concentration characteristic value, and output infection-related physiological indicator characteristic value.

[0014] The microbial load quantification subunit is used to normalize the characteristic values ​​of ambient air colony count, object surface colony density, and bacterial load on the hands of medical staff, and output the characteristic value of environmental microbial load.

[0015] The operation risk mapping subunit is used to map the characteristic values ​​of invasive operation frequency, aseptic operation violation records, and protective equipment missing duration to medical operation risk coefficients.

[0016] Preferably, the infection risk transmission simulation unit calculates the infection risk transmission rate in the following manner:

[0017] An individual susceptibility index is generated based on the product relationship between the characteristic values ​​of infection-related physiological indicators and the characteristic values ​​of environmental microbial load.

[0018] The rate of infection transmission is calculated based on the weighted fusion of the characteristic values ​​of the medical operation risk coefficient and the individual susceptibility index.

[0019] A dynamic model of group infection transmission driven by the rate of infection risk is used to output a heat map of transmission in the ward area and prediction data of transmission paths.

[0020] Preferably, the prevention and control response unit includes:

[0021] The early warning grading engine is used to compare the infection risk transmission rate with a preset threshold range. When the infection risk transmission rate exceeds the first threshold, a first-level early warning signal is generated; when it exceeds the second threshold, a second-level early warning signal is generated.

[0022] The strategy parameter generator is used to dynamically adjust the isolation bed allocation parameters, disinfection frequency parameters, and medical staff scheduling parameters based on the ward area transmission heat map output by the group infection transmission dynamics model.

[0023] Preferably, the physiological indicator risk conversion subunit performs:

[0024] Extract periodic fluctuation characteristics from continuous body temperature monitoring data of patients;

[0025] The importance of white blood cell count characteristics and lymphocyte subset proportion characteristics was assessed.

[0026] The feature values ​​of immunosuppressive drug concentration and immunoglobulin detection are integrated through a multi-layer feature fusion network.

[0027] Preferably, the microbial load quantification subunit performs:

[0028] Time decay weighting was applied to the characteristic values ​​of ambient air colony counts;

[0029] Spatial topological correlation analysis was performed on the characteristic values ​​of bacterial colony density on the surface of an object.

[0030] The bacterial load characteristics of medical staff's hands are convolved with the contact frequency characteristics.

[0031] Preferably, the operational risk mapping subunit performs:

[0032] The frequency of invasive procedures was correlated with the duration of the procedure and the infection coefficient at the procedure site.

[0033] Convert the feature values ​​of aseptic operation violation records into a violation weight index;

[0034] The exposure risk intensity value is derived by using the characteristic value of the duration of lack of protective equipment.

[0035] Preferably, the process of constructing the group infection transmission dynamics model includes:

[0036] Input the topological data of ward bed distribution and personnel flow path data into the spatial propagation network;

[0037] The propagation node weights are generated based on the individual susceptibility index;

[0038] The propagation link strength is calculated using contact frequency characteristic values ​​and exposure risk intensity values.

[0039] Preferably, the process of generating the ward area heat map includes:

[0040] Cluster analysis is performed on nodes in the spatial propagation network that exceed the propagation link strength threshold;

[0041] The thermal values ​​of high-risk areas are generated based on the weight distribution of propagation nodes;

[0042] Predict the direction of transmission and spread by combining medical staff scheduling parameters.

[0043] Preferably, the system validity verification unit performs:

[0044] Monitor the data update delay characteristics of the medical data acquisition unit;

[0045] Analyze the fluctuation characteristics of the feature values ​​output by the infection risk analysis unit;

[0046] The response interval characteristics of the early warning signals of the detection and control response unit;

[0047] When the data update delay characteristic exceeds the delay threshold, the characteristic value output fluctuation characteristic exceeds the fluctuation threshold, or the warning signal response interval characteristic exceeds the response threshold, the uncontrolled event frequency value in the system operation stability evaluation index is generated.

[0048] Compared with the prior art, the beneficial effects of the present invention are:

[0049] By setting up a medical data acquisition unit, it is possible to acquire patient physiological indicators, environmental microbial data, and medical operation record data in real time. This breaks through the lag and limitations of traditional data acquisition methods, and realizes comprehensive and real-time collection of multi-source medical data. This allows the system to grasp various key information related to infection risk in a timely manner, and avoid delays in the identification and intervention of infection risk due to untimely data acquisition.

[0050] The infection risk analysis unit can extract features from collected multi-source data, generating characteristic values ​​of infection-related physiological indicators, environmental microbial load, and medical procedure risk coefficients. This transforms previously scattered and disordered raw data into feature information with clear indications of infection risk, enabling precise quantitative analysis of infection risk factors. This quantitative analysis method helps healthcare professionals clearly understand the physiological risk status of individual patients, the degree of microbial contamination in the hospital environment, and the risk levels of different medical procedures, providing a precise data analysis foundation for subsequent infection risk assessment and prevention.

[0051] The infection risk transmission simulation unit calculates the infection risk transmission rate based on extracted feature values ​​and establishes a dynamic model of group infection transmission, changing the traditional reliance on experience-based judgment in infection transmission analysis. Through scientific model construction and rate calculation, it can accurately predict the transmission trend of infection risk, allowing healthcare workers to know in advance the possible scope and speed of infection spread. Instead of passively responding to infection spread, they can proactively develop targeted prevention and control plans based on the prediction results, actively preventing the spread of infection. This model can dynamically reflect the transmission patterns of infection within a group, providing a scientific trend prediction basis for the formulation of prevention and control strategies, making prevention and control work more forward-looking and scientific.

[0052] The prevention and control response unit generates tiered early warning signals and prevention and control strategy parameters based on the infection risk transmission rate and the dynamics of group infection transmission. It can issue corresponding early warning signals according to different levels of infection risk, allowing medical staff to quickly identify the urgency of the infection risk. Simultaneously, it provides specific prevention and control strategy parameters, offering clear guidance for the formulation of prevention and control measures. Different levels of early warning signals ensure the rational allocation of medical resources, allowing for timely deployment of more resources for focused prevention and control in high-risk situations and appropriate prevention and control measures for low-risk situations, avoiding resource waste. Specific prevention and control strategy parameters make prevention and control measures more targeted; for example, adjusting the frequency and scope of environmental disinfection based on environmental microbial load characteristics, and optimizing operational procedures based on medical operation risk coefficient characteristics, effectively improving the implementation effect of prevention and control measures.

[0053] The system effectiveness verification unit can monitor the operation of each unit in real time, generate system stability assessment indicators, and ensure that the system maintains a stable and reliable working state throughout long-term operation. Through real-time monitoring of the data acquisition process, feature analysis process, and early warning response process, it can promptly detect anomalies in system operation, such as data acquisition interruptions, feature extraction errors, and early warning signal delays. Adjustments and maintenance can be made promptly based on the assessment indicators, ensuring that the system can continuously and accurately perform its infection risk early warning and prevention functions. This avoids disruptions to infection control work due to system malfunctions, providing stable system support for the continuous advancement of medical infection control. Attached Figure Description

[0054] Figure 1 This is a timing diagram of the AI-based intelligent early warning system for medical infection risks described in this invention.

[0055] Figure 2 Flowchart for the infection risk analysis unit;

[0056] Figure 3 The flowchart for the physiological indicator risk transformation subunit;

[0057] Figure 4 The flowchart for the operation risk mapping subunit;

[0058] Figure 5 A flowchart for constructing a dynamic model of the spread of infection in a group. Detailed Implementation

[0059] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] Please see Figure 1 This invention provides an intelligent early warning system for medical infection risks based on artificial intelligence, the system comprising:

[0061] The medical data acquisition unit utilizes multi-source data acquisition, intelligent feature analysis, dynamic propagation simulation, and proactive prevention and control response functions. It acquires real-time physiological indicators such as patient temperature, heart rate, and blood oxygen saturation through an IoT sensor network deployed in the ward environment; collects environmental microbial data through air samplers and surface swab detection devices; and obtains medical operation records such as surgical records and nursing operation logs through the medical information system interface. All data is encrypted and transmitted to the central processing server. The data layer is the foundation for data fusion, requiring clearly defined standardized processing rules for multi-source data, spatiotemporal anchor alignment methods, and quality verification mechanisms to ensure data fusion capability.

[0062] Standardized processing rules (addressing the heterogeneity of three types of core data): Physiological indicator data (body temperature, white blood cell count, etc.): Outliers (such as body temperature > 42℃ or < 35℃) are removed using the "3σ criterion". Missing values ​​(missing duration ≤ 10 minutes) are filled using the "linear interpolation method of 5 adjacent sampling points", and finally, the results are processed using the "Z-score formula". , For the mean, The standard deviation is converted to a 0-1 range value to eliminate dimensional differences; Environmental microbial data (airborne colony count, surface bacterial load): "CFU / m³" and "CFU / cm²" are uniformly converted to "relative pollution intensity" according to the "Hospital Disinfection and Hygiene Standards" (e.g., airborne colony count ≤200 CFU / m³ corresponds to intensity 0.2, >500 CFU / m³ corresponds to intensity 0.8), and then the sensor fluctuation noise is filtered using the "5-minute sliding window averaging method"; Medical operation data (invasive operation records, aseptic violation records): Key parameters in unstructured text are extracted through "Natural Language Processing (NLP)" (e.g., "central venous catheterization 2 hours" is extracted as "operation type: catheterization, duration: 120 minutes"), and a time-series encoding vector is generated according to the "operation occurrence timestamp" (e.g., "1" represents occurrence, "0" represents non-occurrence).

[0063] Time alignment: Using "patient ID + second-level timestamp" as the anchor point, a "10-second fusion window" is constructed - physiological indicator data (sampling frequency 1 time / minute) is matched to the nearest window according to the timestamp, environmental data (sampling frequency 1 time / 2 seconds) takes the average value within the window, and operation data (event-triggered) marks whether the target operation has occurred within the window;

[0064] Spatial alignment: By associating data with "ward number codes" (such as "ward 301" corresponding to bedside temperature stickers, ward 301 air sampler, and ward 301 operation records), we ensure that the data is accurately matched with the physical space where the patient is located;

[0065] Execution node: All processing is completed at the "ward edge computing gateway" (using edge computing hardware, such as NVIDIA Jetson series), with single-window data processing time ≤50ms, avoiding cloud latency.

[0066] Quality verification mechanism: Aligned data must meet "triple verification" before proceeding to the next stage: ① Data integrity ≥ 95% (no more than 1 missing data item in a single window); ② Timestamp deviation ≤ 5 seconds; ③ Spatial coding consistency (device location matches the patient's ward); Data that fails verification is marked as "pending review" and triggers local device retransmission (retransmission count ≤ 3 times).

[0067] The infection risk analysis unit employs a distributed computing framework to process input data in parallel. Physiological indicator data is segmented using a sliding window to extract time-domain and frequency-domain features; environmental microbial data is converted into colony counts through PCR amplification; and medical operation records are parsed into structured risk events using natural language processing technology. The infection risk transmission simulation unit constructs transmission dynamics equations based on feature analysis results and uses a stochastic process model to simulate the transmission path of pathogens in the ward environment. The prevention and control response unit, through an early warning signal generation module, links with the hospital infection control management system to automatically trigger isolation plans or disinfection procedures.

[0068] Example 1: See Figure 2 In the infection risk analysis unit, the physiological indicator risk conversion subunit receives patient temperature waveform data from monitoring devices. These monitoring devices include wireless Bluetooth temperature patches deployed at the patient's bedside (sampling frequency 1 time / minute, transmission delay ≤10s, measurement accuracy ±0.1℃) and a bedside blood cell analyzer placed at the nurses' station (with real-time detection capabilities, white blood cell count and lymphocyte subset detection cycle ≤5 minutes, supporting automatic data push via RS485 interface). Both types of devices are connected to the local edge computing gateway in the ward via the HL7 FHIR V4.0 protocol (a common standard for medical data exchange) (deployment location ≤50m from the device, actual measured data transmission delay for a single device is 35ms-50ms). After noise reduction preprocessing of the raw data by the gateway, it is transmitted to the core analysis server via the hospital's 5G private network (end-to-end latency ≤100ms), ensuring that key physiological data such as body temperature and white blood cell count are entered into the feature extraction process in real time. This subunit uses signal decomposition technology to analyze the nonlinear fluctuation pattern of the body temperature curve and identify abnormal temperature rise trends and diurnal rhythm deviations. White blood cell count data and lymphocyte subset proportion data detected by flow cytometry are input into the feature selection module. By evaluating the discriminative power of each parameter for historical infection cases, the most discriminative feature combination is retained. Immunosuppressive drug blood concentration monitoring values ​​and serum immunoglobulin detection values ​​are input into a multilayer neural network structure. This network includes a pharmacokinetic feature extraction layer and an immune response correlation layer, and finally outputs standardized physiological indicator feature values.

[0069] The microbial load quantification subunit simultaneously processes three types of environmental data. Total colony count data collected every half hour by the air sampler is fed into the time-weighted module. The air sampler is an online environmental microbial sampler (detection limit ≤1 CFU / m³, supporting automatic colony count conversion and data upload once per hour). Surface detection data is transmitted back to the edge gateway via a wireless sensor network (transmission frequency 1 time / 2 minutes, latency ≤20ms). Colony detection data from healthcare workers' hands is linked in real-time to the nursing recorder (handheld PDA). The PDA uses an "upload upon completion of operation" mechanism (data upload latency ≤2 seconds) to ensure that the three types of environmental data simultaneously enter the time-weighted and spatial topology analysis processing stages within the time window, without data lag. Data from the past three hours are weighted according to attenuation coefficients of 0.6, 0.3, and 0.1. Surface detection data is transmitted via a wireless sensor network. The system constructs a spatial topology map with the hospital bed as the vertex and medical equipment as the edges to analyze the colony density propagation path on high-frequency contact surfaces. The data from the hand colony detection of medical staff are correlated with the data from the nursing recorder. The coupling characteristics between the amount of bacteria on the hands and the frequency of patient contact are identified through convolution operations. After normalization, the microbial load characteristic value is generated.

[0070] The operational risk mapping subunit integrates medical behavior data. The invasive procedure record database includes procedure types such as central venous catheterization and urinary catheterization, along with their durations. These records are synchronized to the system via a "real-time operation input interface" on a handheld PDA used by medical staff. The PDA and edge gateway utilize Bluetooth 5.0 Low Energy protocol (transmission distance ≤10m, single data transmission time ≤500ms). Aseptic procedure violations are verified using AI behavior recognition cameras deployed in the treatment area (25fps, violation recognition latency ≤1 second). The recognition results are cross-validated with the data entered by the PDA and then pushed to the operational risk mapping subunit in real time, ensuring the real-time nature and accuracy of medical behavior data and avoiding errors in risk coefficient calculation due to manual input delays. The system assigns an infection coefficient of 0.1 to 0.9 based on the integrity of the mucosal barrier at the operation site. Aseptic procedure violations are categorized using natural language processing technology, with violations such as missing hand disinfection and contaminated sterile sheets corresponding to weight indices of 0.3 to 0.9. Protective equipment status sensors monitor the wearing status of masks and isolation gowns in real time. Combined with data from environmental pathogen concentration probes, the exposure risk intensity value for the duration of protective absence is calculated. The three types of parameters are weighted and fused to output a characteristic value of the medical operation risk coefficient.

[0071] The infection coefficient for invasive procedures is determined based on the degree of damage to the human body barrier caused by the procedure and industry infection rate data. The specific values ​​are shown in Table 1 below.

[0072] Table 1: Standard Values ​​for Infection Coefficients of Invasive Procedures

[0073] Operation type Infection coefficient Basis for value selection (clinical logic) Central venous catheterization 0.9 Disruption of the vascular barrier leads to a postoperative infection rate of approximately 10%-15% (higher than other procedures). Endotracheal intubation / tracheostomy 0.8 Disruption of the respiratory barrier increases the risk of ventilator-associated pneumonia, with an infection rate of approximately 8%-12%. Urinary catheterization 0.6 Disruption of the urinary tract barrier leads to a urinary tract infection rate of approximately 5%-8%. Regular injection 0.1 Temporary disruption of the epidermal barrier, infection rate <1%.

[0074] The weighting criteria for aseptic violations are based on the degree of impact of the violation on infection, and the specific values ​​are shown in Table 2 below.

[0075] Table 2: Standard for Determining the Weight of Aseptic Violations

[0076] Types of violations Violation weight Basis for value selection Damaged sterile drape / instrument dropped 0.9 Direct contact with pathogens increases the risk of infection by more than 80%. Lack of hand disinfection 0.6 Indirect transmission of pathogens increases the risk of infection by 40%-60%. Improper wearing of protective equipment 0.3 Local exposure increases the risk of infection by 10%-30%.

[0077] The calculation logic for the exposure risk intensity value of protection deficiency is as follows: The formula is "Exposure risk intensity value = duration of protection deficiency (minutes) × 0.01 × concentration of environmental pathogens (CFU / m³) / 100", where "0.01" is the time effect coefficient (the risk increases by 0.01 for every minute of protection deficiency) and " / 100" is the concentration standardization coefficient (converting CFU / m³ to a value in the range of 0-1); the concentration of environmental pathogens comes from the real-time detection data of the online air microbial sampler (updated hourly).

[0078] The weighted fusion ratio is: invasive procedure risk (50%) + aseptic violation weight (30%) + risk of exposure due to lack of protection (20%), based on the clinical priority of "risk of the procedure itself > risk of violation > risk of lack of protection".

[0079] After the infection risk transmission simulation unit is activated, the individual susceptibility index is calculated first. This index is generated by multiplying the physiological indicator characteristic value and the microbial load characteristic value, and then transforming it through a nonlinear function to produce a quantitative value in the 0-1 range. The medical operation risk coefficient characteristic value and the individual susceptibility index are then fed into the feature fusion module. This module adopts an adaptive weight allocation mechanism, dynamically adjusting the contribution ratio of the two types of parameters based on historical infection data from different ward areas. The final infection risk transmission rate is determined by multiplying the fusion result by the real-time personnel density monitoring value. The group infection transmission dynamics model is constructed based on a 3D digital twin system of the ward. This model divides the ward area into a spatial grid with a precision of 0.5 meters. Each grid contains attributes such as bed coordinates, ventilation flow, and personnel movement frequency. Using the infection risk transmission rate as the core parameter, the model simulates the transmission process of pathogens between grids: when there is an intersection of personnel flow between the susceptible individual grid and the source grid, the transmission probability is calculated based on the contact duration and protective status. The system performs a Monte Carlo simulation every ten minutes, outputting a ward heat map containing the transmission probability distribution, and marking high-risk transmission links through a path tracing algorithm.

[0080] The specific expression of the nonlinear function is as follows: A piecewise Logistic function is adopted, and the expression is as follows: When (physiological indicator characteristic value × microbial load characteristic value) ≤ 0.3, the individual susceptibility index = 0.2 × (physiological indicator characteristic value × microbial load characteristic value) + 0.1; when 0.3 < (physiological indicator characteristic value × microbial load characteristic value) < 0.7, the individual susceptibility index = 1 / (1 + e^(-5 × (physiological indicator characteristic value × microbial load characteristic value) - 0.2)); when (physiological indicator characteristic value × microbial load characteristic value) ≥ 0.7, the individual susceptibility index = 0.8 × (physiological indicator characteristic value × microbial load characteristic value) + 0.15; (Note: The function parameters are based on the fitting of the "physiological indicator-microbial load-infection occurrence" correlation data of the applicant and 500 postoperative infection cases in a tertiary hospital from 2023 to 2024, ensuring that the index range of 0-1 is positively correlated with the actual infection probability).

[0081] The product weighting is based on the following criteria: physiological indicators account for 60% of the product weight, and microbial load accounts for 40%. This is in accordance with the clinical consensus that "abnormal physiological indicators (such as elevated white blood cell count) have a higher predictive contribution rate to infection than environmental microorganisms" (which conforms to the "dual judgment principle of etiology + signs" in the "Diagnostic Criteria for Hospital Infection").

[0082] 0-1 interval calibration method: The function parameters are fine-tuned monthly based on new clinical data (≥30 cases), and the "least square method" is used to correct the deviation to ensure that an index of 0.8 or above corresponds to an actual infection probability of ≥80%, and an index of 0.2 or below corresponds to an actual infection probability of ≤5%.

[0083] The feature layer is the core of the fusion process. It is necessary to clarify the independent extraction algorithms for the three types of data features, the cross-modal association modeling method, and the basis for dynamic weight allocation to avoid "simple splicing" of features.

[0084] Independent extraction of single-modal features (adapting to the characteristics of different data): Physiological indicator features are extracted into high-dimensional features through a Multilayer Perceptron (MLP) – inputting three basic features: "body temperature fluctuation cycle, white blood cell count change rate, and peak concentration of immunosuppressive drugs", which are processed through two hidden layers (16 neurons per layer) to output a 16-dimensional "physiological risk feature vector" (such as "intensity of inflammatory response" and "degree of immunosuppression"); Environmental microbial features are extracted into spatial distribution features through a Convolutional Neural Network (CNN) – inputting "relative pollution intensity of three sampling points in the ward", which is processed through one convolutional layer (3×1 kernel size) and one pooling layer to output an 8-dimensional "environmental risk feature vector" (such as "local pollution aggregation degree" and "diffusion trend"); Medical operation features are extracted into temporal features through a Recurrent Neural Network (RNN) – inputting "operation type sequence, duration sequence, and violation sequence within 24 hours", which is processed through one LSTM unit (12-dimensional hidden layer) to output a 12-dimensional "operation risk feature vector" (such as "frequency of high-risk operations" and "aseptic compliance rate").

[0085] Cross-modal association modeling (using a "lightweight attention mechanism fusion network"): A two-layer structure of "intramodal self-attention + intermodal mutual attention" is constructed: the self-attention module strengthens key features within a single modality (such as "white blood cell count change rate" in physiological features), while the mutual attention module captures cross-modal associations (such as the association strength between "central venous catheterization" and "physiological features related to vascular barrier disruption"). Unified feature dimensions: All three types of feature vectors are mapped to 20 dimensions through a fully connected layer, and the association strength is calculated using a "3×20-dimensional attention weight matrix," outputting a 20-dimensional "fusion feature vector." Dynamic weight allocation: Initial weights are pre-allocated based on "500 clinical infection cases" (physiological features 0.5, operational features 0.3, environmental features 0.2). Subsequent weights are updated every 7 days using "gradient descent" with "30 new case data," ensuring that the correlation between the weights and actual clinical risk is ≥0.85.

[0086] In a specific implementation example, a patient in bed 3 of a certain ward experienced postoperative temperature fluctuations. The patient's temperature fluctuation data was collected by a bedside wireless temperature patch at 14:02:15, preprocessed by an edge gateway (30ms), transmitted via a 5G private network (80ms), and entered the physiological indicator risk conversion subunit at 14:02:16. White blood cell count data was detected by a bedside analyzer at 14:05:00 and pushed to the subunit at 14:05:01. Both types of data were processed synchronously using a "5-second time window alignment strategy" (based on patient ID and timestamp matching). Feature importance assessment employed a lightweight random forest algorithm (40 decision trees, implemented using NVIDIA Jetson AGX). The Xavier edge computing card accelerated the computation (75ms), and the nonlinear feature transformation used a piecewise Sigmoid function (parameters 0.8 and -0.3 were fitted based on 500 postoperative infection cases from January 2023 to January 2024 in our hospital), with a computation time of 25ms. The final physiological indicator feature values ​​were output to the infection risk transmission simulation unit at 14:05:02. The entire process from data acquisition to feature output had a latency of 2 seconds, meeting the clinical "second-level response" requirement. The physiological indicator risk transformation subunit detected that the body temperature curve had lost its diurnal rhythm, the white blood cell count feature importance score reached 0.85, and the immunosuppressive drug feature vector showed an abnormal activation mode, outputting a physiological indicator feature value of 0.78. At the same time, the microbial load quantification subunit measured that the weighted value of the airborne colony count in the bed area exceeded the standard, the colony density on the bedside table surface showed a central diffusion trend in the topology map, and the convolution feature value of the bacterial load on the responsible nurse's hands reached 0.63, outputting a microbial load feature value of 0.71. The operation risk mapping subunit records that the patient underwent two catheterization procedures within 24 hours (infection coefficient 0.7), there was a violation of sterile strip contamination in the nursing record (weight 0.6), and the total duration of missing protective equipment was 42 minutes. The output medical operation risk coefficient feature value is 0.68.

[0087] The system calculated the patient's individual susceptibility index as 0.78 × 0.71 = 0.55. After weighting the medical operation risk coefficient using the feature fusion module, an infection risk transmission rate of 0.61 was generated. The transmission dynamics model used the patient's grid as the source of infection and combined it with personnel flow data in the ward to simulate and display the probability distribution of the risk of pathogens spreading to adjacent beds along the nursing path. The area around bed 3 in the heat map showed a red alert, and the path prediction showed that nurse station 6 was a critical transmission node. After the alert signal was generated, it was pushed to the nurse station terminal and the responsible nurse's mobile APP through the hospital intranet. The actual push delay was 90ms. The nurse station's audible and visual alarm device (directly connected to the prevention and control response unit) triggered a level 1 alert at 14:05:03. The delay from feature value output to alert triggering was 1 second. The total delay of the entire link (data acquisition-analysis-alert) was ≤3 seconds, which is 40% better than the traditional centralized cloud processing solution (delay ≥5 seconds), verifying the system's real-time alert capability.

[0088] Example 2: See Figure 3 The early warning grading engine in the prevention and control response unit continuously receives transmission rate data output by the infection risk transmission simulation unit. This engine maintains a historical transmission rate database, calculating the standard deviation using the average of the past 30 days as a baseline. When the real-time transmission rate exceeds 1.5 times the standard deviation of the baseline, the engine activates a Level 1 early warning protocol: automatically parsing the dominant strain information in the pathogen gene sequencing data, locating the coordinates of high-risk beds, generating a structured early warning message, and transmitting it to designated terminals via the hospital infection control network. The early warning message includes strain drug resistance analysis, a list of susceptible patients, and environmental sampling recommendations. If the transmission rate exceeds the 2.5 times standard deviation threshold, a Level 2 early warning protocol is additionally activated: the system calls the audible and visual alarm device to emit a three-short-two-long flashing signal at the nurses' station, and simultaneously pushes a personnel movement restriction plan to mobile nursing terminals, specifying the maximum on-duty density of medical staff in high-risk wards and the level of cross-area access permissions.

[0089] The strategy parameter generator interacts in real time with the group infection transmission dynamics model. When it receives a heat map of the ward area, the generator first identifies red warning areas with heat values ​​exceeding 0.7. For these areas, negative pressure isolation beds are automatically allocated: the system retrieves the status of vacant negative pressure beds in the ward and, combined with a patient transfer priority algorithm (considering age, underlying diseases, and transfer distance), generates a set of bed allocation instructions. Disinfection frequency parameters are dynamically adjusted based on the gradient distribution of the heat map: areas with heat values ​​of 0.7-0.8 undergo ultraviolet disinfection once per hour, while areas with heat values ​​of 0.5-0.7 undergo chlorine disinfection once every two hours. All parameters are directly written into the disinfection robot task queue through the hospital's equipment control system. Medical staff scheduling parameters are generated based on transmission path prediction data: the system marks high-risk transmission path intersections (such as nurse workstations and medication preparation rooms), prohibits personnel on the same shift from simultaneously handling nursing tasks in both red and clean areas, and automatically increases the quantity of protective equipment distributed to high-risk areas through an intelligent warehousing system (e.g., increasing the reserve of N95 masks to three times the normal level).

[0090] The physiological indicator risk transformation subunit implements refined feature processing. The patient body temperature data processing flow includes three steps: continuous monitoring data is decomposed into different frequency components through wavelet transform, and the amplitude variation coefficient within a 24-hour period is extracted; after removing abnormal fluctuations caused by drug interference, the data is input into a long short-term memory network to predict the trend of change in the next 6 hours; finally, a 0-1 standardized value representing the degree of body temperature abnormality is output. White blood cell count and lymphocyte subset analysis adopt a feature attribution algorithm: historical infection case datasets are input into a gradient boosting tree model, and the contribution of each parameter is calculated through Shapley values, retaining the top five key features such as absolute neutrophil count and CD4 / CD8 ratio. The fusion processing of immunosuppressive drug concentration and immunoglobulin is based on a graph neural network architecture: a heterogeneous graph structure is constructed with drug concentrations such as tacrolimus and cyclosporine as nodes and IgG and IgA detection values ​​as edges. The mapping relationship between drug concentration and immune response is learned through a graph attention mechanism, and a 128-dimensional feature vector is output to represent the immunosuppressive state.

[0091] The transmission rate in a surgical ward rose to 0.82 (baseline standard deviation 0.3). The early warning grading engine identified this value as exceeding twice the standard deviation and immediately activated a Level II early warning: Analysis of environmental sampling data identified MRSA as the dominant pathogen, marking beds 3, 7, and 12 as high-risk areas, and sending an early warning message containing vancomycin medication recommendations to the infection control department. Simultaneously, the nurses' station's audible and visual alarms were activated, and mobile terminals received personnel flow control measures: limiting the number of nurses working in the ward to no more than four at a time, and suspending family visits.

[0092] The strategy parameter generator responded with a heatmap (showing a heat value of 0.83 around bed 3): The patient in bed 3 was assigned to negative pressure ward 8. In the transfer priority calculation, the patient's age of 65 and history of diabetes received the highest score. The disinfection robot received new instructions: the frequency of ultraviolet disinfection in the bed 3 area was increased to once per hour, and the frequency of chlorine disinfection in adjacent beds was adjusted to once every 90 minutes. The medical staff scheduling module marked nurse station 6 as a transmission node and automatically adjusted the shift schedule: the nursing team originally responsible for bed 3 no longer undertakes nursing tasks for beds 15-17, and the materials system provides this team with additional Level 3 protective equipment packages. The concurrent physiological indicator risk conversion subunit processed the patient data in bed 3: wavelet analysis of the body temperature curve showed the disappearance of the diurnal rhythm and an increase in amplitude of 0.3℃; the LSTM network predicted that it would reach 38.5℃ in 6 hours. In the white blood cell characteristic attribution, the absolute value of neutrophils contributed 0.91, and the lymphocyte subset analysis retained the key feature of a CD4 / CD8 ratio of 0.35. Immunograph network analysis showed a correlation between cyclosporine concentration of 182 ng / ml and abnormal IgG4 levels, with the output immunosuppressive feature vector marking a high-risk pattern. These analytical results provide multi-dimensional evidence for early warning decision-making.

[0093] The decision-making level is the final implementation stage of the integration, and it needs to clarify the correlation rules between the integration results and the propagation model, data conflict resolution strategies, and dynamic feedback calibration mechanisms. The result correlation rules (for infection risk propagation simulation) are as follows: Final infection risk score = (fusion feature vector × weight matrix) × 0.7 + (physiological risk value × 0.5 + operational risk value × 0.3 + environmental risk value × 0.2) × 0.3; where the "weight matrix" is predefined through "clinical expert consensus" (ensuring a score range of 0-1, positively correlated with the probability of infection). This score directly serves as the core input parameter for calculating the "propagation rate" in the "infection risk propagation simulation unit."

[0094] Data conflict resolution strategies (for scenarios where conclusions from different data sources contradict each other) are shown in Table 3 below.

[0095] Table 3: Data Conflict Resolution Strategies

[0096] Conflict scenarios Solution Rules Clinical basis Normal physiological indicators (risk value < 0.2) + abnormal environment / operation (risk value > 0.8) Based on physiological indicators, environmental / operational data are marked as "pending verification," triggering manual screening by medical staff (such as checking whether the environmental sampler is malfunctioning). Abnormal physiological indicators are direct signs of infection, while abnormal environmental / procedural factors are indirect risk factors. Abnormal physiological indicators (risk value > 0.8) + normal environment / operation (risk value < 0.2) Based on physiological indicators, increase the sampling frequency of physiological data (from once per minute to once every 30 seconds) to continuously monitor infection trends. The early warning value of a single direct risk factor is higher than that of multiple indirect safety factors. Abnormal environment (>0.8) + Abnormal operation (>0.8) + Normal physiology (<0.2) Calculate the "Risk Overlay Coefficient = Environmental Risk Value × Operational Risk Value". If the coefficient is greater than 0.5, a "Level 2 Warning" is triggered; if it is less than 0.5, it is recorded as a "Potential Risk". The combination of two indirect risks may create conditions for infection, requiring dynamic monitoring.

[0097] Dynamic feedback calibration mechanism: Collect “data on hospital-acquired infection cases (≥20 cases)” monthly and calculate the “area under the ROC curve (AUC)” of the fusion score; if AUC < 0.85, adjust the weights in the “outcome association rules” (e.g., increase the proportion of fusion feature vectors from 0.7 to 0.8) to ensure that the model adapts to clinical changes.

[0098] Example 3: See Figure 4 The microbial load quantification subunit processes three types of environmental monitoring data streams. Airborne colony sampling data undergoes time-decay weighting, employing an exponential decay model to enhance the contribution of recent data. The time-decay weighting calculation is completed through the edge gateway's "local computing module," which encapsulates the weighting algorithm operator in C++, with a computation time of ≤10ms for a single set of sampling data (3 time points). Spatial topology graph analysis uses a lightweight graph convolution algorithm (computation time ≤20ms when the number of nodes ≤50), avoiding data processing delays due to algorithm complexity. The convolution operation of bacterial load on medical staff's hands and contact frequency is reduced by a "sliding window cache" (window size 5 minutes), minimizing redundant calculations and ensuring a computation time of ≤15ms, guaranteeing real-time output of microbial load characteristic values ​​(updated every half hour, total update time ≤50ms). This processing follows the following calculation principles:

[0099]

[0100] in: This represents the weighted colony count at the current moment. For the first Second sampling colony count For the corresponding sampling time, The current system time. This is the decay rate coefficient (default value 0.1). Sampling data is stratified by time: data from the most recent hour retains its original value, data from 1-3 hours is multiplied by a decay factor of 0.6, and data from 3-6 hours is multiplied by a decay factor of 0.3. Expired data is cleared at midnight each day. Surface colony density detection data is transmitted in real-time via a wireless sensor network. The system constructs a spatial topology graph with the bed unit as the vertex and medical equipment as the edges. This graph structure includes three types of edge weights: bed-monitor edge weight is set to 0.7 (high-frequency contact), bed-IV stand edge weight is 0.4, and bed-bedside table edge weight is 0.9 (multiple items stored). The graph convolutional network calculates the spatial correlation of surface colony density based on this topological relationship, identifying risk paths such as transmission from the sink to the treatment cart. Data on the bacterial load on healthcare workers' hands is linked to nursing behavior records; colony values ​​sampled every five minutes are convolved with the frequency of contact events. The convolution kernel is set to a rectangular window function with a five-minute duration, activating the calculation when a patient contact event is detected. The ReLU function filters out negative interference and outputs standardized load characteristic values.

[0101] The operational risk mapping subunit performs multi-dimensional medical behavior analysis. The invasive operation database contains twelve types of operation codes, each associated with three risk parameters: a baseline infection coefficient set based on mucosal barrier integrity (e.g., endotracheal intubation coefficient 0.8, urinary catheterization coefficient 0.6), an operation duration factor accumulated at 0.01 per minute, and an operation site coefficient based on anatomical location (respiratory tract operation coefficient 1.0, skin operation coefficient 0.5). The final baseline operational risk value is calculated as: baseline infection coefficient × (1 + 0.01 × operation duration) × site coefficient. The operation duration data in the formula is automatically calculated based on the "operation start / end timestamp" uploaded in real time by the PDA (time difference calculation time ≤ 1ms); the site coefficient is matched in real time through the system's built-in "Medical Operation Infection Coefficient Comparison Table" (based on the "Hospital Infection Management Standards"), with matching time ≤ 5ms; the calculation of the aseptic violation weight index and the exposure risk intensity value of protective deficiency are both processed in parallel through edge nodes (two-way calculation is executed synchronously, with a total time ≤ 20ms), ensuring that the characteristic value of the medical operation risk coefficient is output within 1 second after the operation is completed, providing real-time parameter support for the subsequent calculation of the infection risk transmission rate. Aseptic operation violation records are parsed by a natural language processing engine, classifying nursing text records into seven violation types. Each type is assigned a fixed weight index: missing hand disinfection is weighted 0.3, damaged aseptic barrier is weighted 0.5, and contaminated instruments are weighted 0.9. The violation weight index is calculated cumulatively, with the same type of violation increasing by 1.2 times per day. The protective equipment monitoring system tracks the status of equipment such as masks and isolation gowns in real time through RFID sensors. The duration of protective equipment absence is linked to environmental pathogen concentration probes. The exposure risk intensity value is calculated using a piecewise function: for the first 30 minutes, it increases linearly by concentration × duration; after 30 minutes, an exponential growth mode is activated. Three types of parameters are weighted and fused to generate a medical operation risk coefficient feature value, with invasive operation weighted at 0.5, aseptic violation weighted at 0.3, and protective equipment absence weighted at 0.2.

[0102] An ICU ward experienced a morning nursing peak. The microbial load quantification subunit processed data from the 8:00-9:00 time period: the airborne colony sampling value sequence was [120, 150, 180] (sampling interval 20 minutes), and the weighted value at 9:00 was calculated according to the attenuation formula. Surface detection showed that the bacterial density of the handwashing sink exceeded the standard. Topological graph analysis showed that the edge weight between the sink and the respiratory therapy cart reached 0.8, and the graph convolutional network output a transmission risk characteristic value of 0.78 for this area. The hand sample value of the responsible nurse was 85 CFU, which was convolved with 6 patient contact records, and the ReLU output load characteristic value was 0.67.

[0103] The concurrent operation risk mapping subunit recorded three events: Patient A underwent endotracheal intubation (baseline coefficient 0.8, duration 25 minutes, airway coefficient 1.0), with a baseline risk value of 0.8 × (1 + 0.01 × 25) × 1.0 = 1.0. The nursing record analysis revealed a violation of "not changing contaminated gloves" (weighted 0.5). The protective clothing sensor on the nurse in bed 3 showed wrist exposure for 42 minutes, with an environmental concentration monitoring value of 0.8, resulting in an exposure risk intensity value of 0.8 × 30 + 0.8 × e^{0.1 × 12} = 30.6. The final characteristic value of the medical operation risk coefficient was 1.0 × 0.5 + 0.5 × 0.3 + 30.6 × 0.2 = 6.87.

[0104] Example 4: See Figure 5 The construction of the dynamic model of cluster infection transmission begins with the establishment of a spatial transmission network. The system imports 3D structural data of wards from the hospital's building information model, extracting topological information including bed coordinates, door and window locations, and ventilation duct layout. An ultra-wideband positioning system collects real-time movement trajectories of medical staff, patients, and equipment, forming a personnel flow path dataset. Based on this data, a directed weighted graph structure is constructed: nodes represent key locations such as beds, nursing workstations, and medical equipment, while edges represent the direction of personnel flow. Each node contains an attribute set, with typical attributes including bed type (general / isolation), current patient susceptibility index, and adjacent ventilation flow rate. Edge weights are determined by two types of parameters: the contact frequency parameter comes from the frequency of equipment use and the duration of personnel proximity recorded by the radio frequency identification system, and the exposure risk intensity value is inherited from the output of the operational risk mapping subunit. The model adopts an improved SEIR infectious disease framework, setting the incubation period parameter to 24 hours (based on common pathogen characteristics), and dynamically adjusting the infectious period parameter within the range of 48-72 hours (based on the virulence factors reported in the pathogen gene sequencing report).

[0105] The weighted fusion weights are based on: 40% for medical operation risk coefficient and 60% for individual susceptibility index, according to the epidemiological law that "the impact of individual susceptibility status on transmission is greater than the impact of operation risk" (e.g., even if a susceptible individual is exposed to low-risk operations, the probability of infection and transmission is still high); the weights are calibrated through "backtracking analysis of 100 ward transmission events" (data from an ICU of a hospital from January to March 2024) to ensure that the correlation between the fusion results and the actual transmission speed is ≥0.85.

[0106] Real-time personnel density quantification standard: The unit of quantification is "number of people per 10㎡". The number of people is counted in real time by infrared array sensors (deployed at the door of the ward / corridor). The specific classification is shown in Table 4 below.

[0107] Table 4: Real-time Personnel Density Quantification Standard Table

[0108] Personnel density (people / 10㎡) Quantized value Corresponding scenarios 0-1 0.2 Off-peak hours (such as early morning) 2-3 0.6 Routine period (e.g., daytime care) ≥4 1.0 Peak periods (such as treatment / visitation)

[0109] Transmission rate calculation and units: The formula is "Infection risk transmission rate = (Medical operation risk coefficient × 40% + Individual susceptibility index × 60%) × Personnel density quantification value", and the unit is "transmission probability / hour" (that is, the probability of a susceptible individual being infected within one hour). For example, the fusion result 0.5 × personnel density 0.6 = 0.3 (meaning that 30% of susceptible individuals may be infected per hour). The unit matches the clinical "infection outbreak cycle (hourly level)" to facilitate medical staff's understanding of the urgency of the risk.

[0110] The generation of a heat map of disease transmission in a ward area involves a multi-stage calculation process. First, a link strength scan of the spatial transmission network is performed to identify high-risk transmission paths exceeding a preset threshold of 0.8. Density clustering analysis is then performed on the nodes connected by these paths, automatically identifying high-risk node clusters and marking core transmission hubs. Subsequently, heat values ​​are calculated based on node weight distribution: taking a bed node as an example, its heat value is determined by a combination of patient susceptibility index (70% weight), adjacent equipment contamination index (20% weight), and poor ventilation index (10% weight). The calculation results are used to generate a continuous heat distribution surface using a kernel density estimation algorithm, with the bandwidth parameter set to 1.5 times the average distance between beds. The final stage combines the movement trajectories from medical staff scheduling parameters to predict the transmission direction: the system loads the three-hour shift schedule, calculates the shuttle paths of nursing staff between hot and clean zones, simulates the probability of pathogen spread along the personnel flow direction using a particle motion model, and outputs a six-hour risk spread prediction cloud map.

[0111] Table 5: Attributes of Transmission Nodes in the ICU Ward

[0112] Node number Node type Susceptibility Index Contact frequency Exposure risk Ventilation flow rate (m³ / h) Heat value B3 Isolation beds 0.83 0.91 0.76 120 0.88 N6 Nursing workstation 0.42 0.95 0.68 80 0.72 R12 Ventilator 0.67 0.82 0.59 60 0.65 W9 handwashing basin 0.38 0.96 0.81 40 0.78

[0113] Taking a hospital-acquired infection incident in an intensive care unit as an example, during the spatial transmission network construction phase: the system imported the ward's BIM model, identified 8 beds and 4 nursing station nodes, and ultra-wideband positioning showed a high-intensity flow path (average 3 people per minute) between nurse workstation N6 and beds B3 and B5 during the morning peak hours. In the node attribute configuration, the susceptibility index of the patient in bed B3 due to postoperative immunosuppression reached 0.83, and the risk intensity value of R12 exposure to ventilator was 0.59 (based on recent tracheotomy operation records).

[0114] Heatmap Generation Phase: The system detected an intensity of 0.85 (exceeding the threshold) for the N6-B3-R12 path and identified this triangular area as a high-risk cluster using a clustering algorithm. In the heat value calculation, bed B3 received a base heat value of 0.75 due to its high susceptibility index. This was further supplemented by a 0.08 contribution from the contamination index of the adjacent R12 ventilator and a 0.05 contribution from poor ventilation, resulting in a final heat value of 0.88. Kernel density estimation showed that this area formed a heat core zone with a diameter of 3 meters. After loading the scheduling data into the particle motion model, it was predicted that the early shift nurses moving from N6 to bed B7 would carry pathogens and spread them along the east-west corridor, forming a secondary hotspot at bed B7 (predicted heat value 0.72). Output Phase: The system generated a visual heat map. The area around bed B3 showed a dark red core zone (heat value 0.85-0.88), extending along the corridor to bed B7 to form an orange transition zone (heat value 0.70-0.75). The predicted cloud map showed that the high-risk area would expand to the adjacent bed B4 area within six hours. The real-time analysis results provide a spatial basis for the allocation of prevention and control resources.

[0115] Example 5: This example describes the operational monitoring mechanism of the system effectiveness verification unit, which evaluates the operational stability of the early warning system in real time through multi-dimensional performance indicators. This unit implements a three-layer monitoring architecture, continuously tracking key performance parameters for data acquisition, feature analysis, and early warning response. In the data acquisition layer monitoring, the system calculates the data update delay characteristics of the medical data acquisition unit using a timestamp comparison algorithm. A five-minute timeliness threshold is set for the data stream of vital sign monitoring devices. When the time interval between two consecutive uploads from devices such as ECG monitors and temperature sensors exceeds five minutes, the system records it as a vital sign data delay event. A fifteen-minute timeliness threshold is applied to environmental microbial sampling devices, including monitoring the transmission delay of devices such as air samplers and surface swab detectors. The data delay characteristic record includes fields such as the delay device number, delay duration, and affected data items. When a single device accumulates more than three delays in a single day or a single delay exceeds ten minutes, a data layer anomaly marker is triggered.

[0116] The feature analysis layer monitors the output stability of the infection risk analysis unit. The system uses a sliding window algorithm to analyze the fluctuation characteristics of feature values, setting a standard observation window of 30 minutes. In the monitoring of physiological indicator feature value fluctuations, the standard deviation within the window is calculated for body temperature conversion, white blood cell count, and immunosuppression features. A fluctuation event is recorded when the standard deviation of any feature value exceeds 0.2. The monitoring of environmental microbial load feature value fluctuations sets a threshold of 0.3, focusing on monitoring the coefficient of variation of airborne bacterial colony features, object surface features, and hand-borne bacterial load features. Fluctuation feature records include parameters such as feature value type, fluctuation amplitude, and duration. When a single feature value exceeds the standard for three consecutive window periods or the fluctuation amplitude exceeds 0.5, an anomaly marker is triggered in the analysis layer.

[0117] The early warning response layer monitors and tracks the execution time of instructions from the prevention and control response units. The system records the complete time chain from the generation of an early warning signal to the hospital infection control system returning a confirmed response, and calculates the early warning signal response interval characteristics. A three-minute response threshold is set for Level 1 early warnings, and a two-minute response threshold is set for Level 2 early warnings. The response interval record includes information such as the early warning level, response delay duration, and responsible terminal number. When a single response delay exceeds the threshold or a single terminal accumulates five delays in a single day, an anomaly flag is triggered at the response layer. The system synchronously monitors the execution status of strategy parameters, including derived parameters such as the delay in executing isolation bed allocation instructions and the lag time for disinfection frequency adjustments.

[0118] The frequency of out-of-control events is calculated by comprehensively analyzing data from three layers of monitoring. Data-layer out-of-control events are defined as: three consecutive data delays from the same acquisition device, or ten cumulative delays from the same device within a single day. Analysis-layer out-of-control events are judged by: single feature value fluctuations lasting for ten minutes without recovery, or multiple feature value fluctuations occurring concurrently for more than five minutes. Response-layer out-of-control events include: three cumulative delays in secondary warning responses, or two failures in executing key strategy parameters. The frequency value is calculated using a weighted cumulative method: data-layer event weight 0.4, analysis-layer weight 0.3, and response-layer weight 0.3. When the hourly frequency value exceeds 0.6, the system automatically initiates a self-check procedure: suspending warning signal output while maintaining data acquisition, sequentially diagnosing the operational status of the network transmission link, feature calculation engine, and command issuance interface, and generating a self-check report including fault module location.

[0119] During the operation of a hospital system, the data layer monitoring detected two consecutive data uploads from the temperature sensor in ward 3 with a seven-minute interval, marking a data delay event. The analysis layer detected that the microbial load characteristic value had a standard deviation of 0.35 within a 30-minute window, triggering a fluctuation event recording. The response layer recorded that the level 2 warning response took 2 minutes and 40 seconds, exceeding the threshold and recording a delay event. The system calculated the frequency of the current hour's data layer events (two with a weight of 0.8), analysis layer events (one with a weight of 0.3), and response layer events (one with a weight of 0.3), with a frequency value of (0.8 + 0.3 + 0.3) / (2.5 × 1) = 0.56. When a ventilator data delay occurred again, the frequency value rose to 0.64, triggering a self-check program. The diagnosis showed that network switch port congestion caused data transmission delays, the feature calculation engine fluctuated due to memory overflow, and the command interface experienced response delays due to expired certificates. After the self-check report was generated, the system automatically switched to a backup network channel, restarted the computing node, and updated the security certificate, restoring stable operation within 15 minutes.

[0120] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0121] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An artificial intelligence-based medical infection risk intelligent early warning system, characterized in that, The method comprises the following steps: a medical data acquisition unit for acquiring physiological index data of patients, environmental microbial data and medical operation record data in real time; an infection risk analysis unit for extracting features from the data acquired by the medical data acquisition unit, generating infection-related physiological index feature values, environmental microbial load feature values and medical operation risk coefficient feature values; an infection risk transmission simulation unit for calculating the infection risk transmission rate based on the infection-related physiological index feature values, the environmental microbial load feature values and the medical operation risk coefficient feature values, and establishing a group infection transmission dynamics model based on the infection risk transmission rate; a prevention and control response unit for generating a hierarchical early warning signal and a prevention and control strategy parameter according to the infection risk transmission rate output by the infection risk transmission simulation unit and the group infection transmission dynamics model; a system effectiveness verification unit for monitoring the data acquisition process of the medical data acquisition unit, the feature analysis process of the infection risk analysis unit and the early warning response process of the prevention and control response unit in real time, and generating a system operation stability evaluation index.

2. The artificial intelligence-based medical infection risk intelligent early warning system according to claim 1, characterized in that, The infection risk analysis unit comprises: a physiological index risk conversion subunit for performing nonlinear feature conversion on patient temperature feature values, white blood cell count feature values and immune suppressant drug concentration feature values, and outputting infection-related physiological index feature values; a microbial load quantification subunit for normalizing environmental air bacterial colony count feature values, object surface colony density feature values and medical staff hand bacterial load feature values, and outputting environmental microbial load feature values; an operation risk mapping subunit for mapping invasive operation frequency feature values, aseptic operation violation record feature values and protection equipment absence duration feature values to medical operation risk coefficient feature values. 3.The artificial intelligence-based medical infection risk intelligent early warning system according to claim 2, characterized in that, The infection risk transmission simulation unit calculates the infection risk transmission rate by the following methods: generate an individual susceptibility index according to the product relationship between the infection-related physiological index feature values and the environmental microbial load feature values; calculate the infection risk transmission rate based on the weighted fusion result of the medical operation risk coefficient feature values and the individual susceptibility index; drive the group infection transmission dynamics model with the infection risk transmission rate, and output ward area transmission heat maps and transmission path prediction data.

4. The artificial intelligence-based medical infection risk intelligent early warning system according to claim 3, characterized in that, The prevention and control response unit comprises: a warning classification engine for comparing the infection risk transmission rate with a preset threshold interval, generating a first-level early warning signal when the infection risk transmission rate exceeds a first threshold, and generating a second-level early warning signal when the infection risk transmission rate exceeds a second threshold; a strategy parameter generator for dynamically adjusting isolation bed allocation parameters, disinfection frequency parameters and medical staff scheduling parameters according to the ward area transmission heat map output by the group infection transmission dynamics model. 5.The artificial intelligence-based medical infection risk intelligent early warning system according to claim 2, characterized in that, The physiological index risk conversion subunit performs: extracting periodic fluctuation features from continuous patient temperature monitoring data; performing feature importance evaluation on white blood cell count feature values and lymphocyte subpopulation ratio feature values; integrating immune suppressant drug concentration feature values and immunoglobulin detection value feature values through a multi-layer feature fusion network. 6.The artificial intelligence-based medical infection risk intelligent early warning system according to claim 5, characterized in that, The microbial load quantification subunit performs: implementing time decay weighting on environmental air bacterial colony count feature values; Perform spatial topology correlation analysis on the colony density eigenvalue of the object surface; Convolve the eigenvalue of the amount of bacteria on the hands of medical staff with the eigenvalue of contact frequency.

7. The artificial intelligence-based medical infection risk intelligent early warning system according to claim 6, characterized in that, The operation risk mapping subunit performs: Correlate the operation duration eigenvalue with the operation site infection coefficient according to the invasive operation frequency eigenvalue; Convert the aseptic operation violation record eigenvalue into a violation weight index; Deduce the exposure risk intensity value through the protection equipment absence duration eigenvalue.

8. The artificial intelligence-based medical infection risk intelligent early warning system according to claim 3, characterized in that, The population infection transmission dynamics model construction process includes: Input the ward bed distribution topology data and personnel flow path data into the spatial transmission network; Generate transmission node weights according to individual susceptibility indices; Calculate transmission link strength using the contact frequency eigenvalue and the exposure risk intensity value. 9.The artificial intelligence-based medical infection risk intelligent early warning system according to claim 8, characterized in that, The ward area transmission heat map generation process includes: Perform cluster analysis on nodes in the spatial transmission network that exceed the transmission link strength threshold; Generate high-risk area heat values based on transmission node weight distribution; Combine medical staff scheduling parameters to predict transmission diffusion direction. 10.The artificial intelligence-based medical infection risk intelligent early warning system according to claim 1, wherein, The system effectiveness verification unit performs: Monitor the data update delay characteristics of the medical data acquisition unit; Analyze the eigenvalue output fluctuation characteristics of the infection risk analysis unit; Detect the warning signal response interval characteristics of the prevention and control response unit; When the data update delay characteristics exceed the delay threshold, the eigenvalue output fluctuation characteristics exceed the fluctuation threshold, or the warning signal response interval characteristics exceed the response threshold, generate a loss of control event frequency value in the system operation stability evaluation index.

Images