A Deep Learning-Based AI-Powered Intelligent Early Warning and Data Management Method and System for Hospital Infections

Abstract

This invention belongs to the technical field of smart healthcare management, specifically relating to a deep learning-based AI-powered intelligent early warning and data management method and system for hospital infection control. The method includes collecting multi-source medical data from patients, constructing structured input features, training an infection risk prediction model, and performing real-time inference, spatial comparison, and risk path analysis of patient infection risks. The system includes a data acquisition module, a model training module, a model inference module, a risk comparison module, a multi-level assessment module, and an infection control business management module. These modules operate collaboratively to achieve the prediction, identification, and graph-based management of infection risks. This invention improves the intelligence and visualization level of hospital infection early warning, possessing advantages such as strong real-time performance, clear data structure, and good traceability.

Description

Technical Field

[0001] This invention belongs to the technical field of smart healthcare management, specifically relating to a deep learning-based AI-powered intelligent early warning and data management method and system for hospital infection control. Background Technology

[0002] Hospital infection control has always been a core issue in the construction of a medical safety system. With the continuous improvement of medical standards and the increasing complexity of diagnostic and treatment methods, the risks of hospital infections are becoming more diversified and insidious, especially in high-risk areas such as intensive care units and surgical departments, where the transmission routes of microorganisms are difficult to completely block. To protect patient health and improve the quality of medical care, establishing an efficient, accurate, and timely infection monitoring and early warning system has become an urgent need.

[0003] Current hospital infection control typically relies on manual review of electronic medical records, laboratory reports, and nursing records, combined with empirical rules to set static thresholds for daily monitoring of key departments, key populations, and key pathogens. While this model has been integrated into most Hospital Information Systems (HIS), Laboratory Information Systems (LIS), and infection control systems, it has several limitations. First, the rule base is outdated, failing to cover complex and ever-changing clinical infection pathways, easily leading to missed or false reports. Second, data sources are scattered, with low levels of structure, resulting in a heavy workload for manual review, prominent information silos, and difficulty in achieving real-time integration and analysis of cross-system data. Furthermore, traditional statistical methods are insufficient in identifying dynamic trends, potential transmission chains, and abnormal clustering events, often failing to achieve early intervention and multi-dimensional risk modeling. These problems severely restrict the improvement of hospital infection control levels and also affect the effective implementation of intelligent methods in real-world scenarios.

[0004] Against the backdrop of the continuous integration of medical informatics and artificial intelligence technologies, there is an urgent need for an intelligent method that can accurately identify infection risks, achieve dynamic trend analysis and multi-dimensional data collaboration, in order to build a new paradigm for hospital infection early warning and management that covers the entire infection control process. Summary of the Invention

[0005] To address the aforementioned problems, the present invention aims to propose a deep learning-based AI-powered intelligent early warning and data management method for hospital infection control, comprising the following steps:

[0006] S1. Construct a deep learning model for hospital infection risk based on structured historical data:

[0007] A deep learning model for hospital infection risk is established by acquiring structured historical data containing patient identity information, disease type, test results, strain infection information, antibiotic use records, nursing records, and bed information. The structured data is used as input features to supervise the learning of deterministic results of hospital infection. The structured data is input into the neural network for training, and the weight parameters obtained from the training are stored on a local server.

[0008] S2. Collect multi-source medical data and generate model input features in a unified format:

[0009] Multi-source medical data is collected in real time in the hospital information system to form input features in a unified format, and then input into the deep learning model for infection risk prediction.

[0010] S3. Split the training dataset and perform normalization training and fitting monitoring of the infection risk model:

[0011] The structured data was divided into training, validation and test sets. The patient's antibiotic use information, strain distribution, nursing operation frequency and bed change data were normalized. The model was trained in combination with labeled data. The convergence process of the network loss function was recorded. The loss value and accuracy index during the model fitting process were monitored and the risk prediction results were output.

[0012] S4. Output patient infection risk prediction results and complete spatial domain data comparison and platform reporting:

[0013] Anonymized structured output files are generated based on the current patient infection risk prediction values ​​and uploaded to the central server. The data is then verified against real-time data and existing data, and compared with the prediction values ​​of patients in adjacent beds and on the same floor. The infection risk prediction values ​​of high-risk patients are reported to the infection risk early warning platform.

[0014] S5. Implement ward-level infection risk early warning and coordinated response based on cross-departmental risk association structure:

[0015] Based on the construction of a cross-departmental risk association structure, it responds to high-risk prediction values, outputs and displays risk alerts, provides data support for hospital infection prevention and control, locates the scope of infection outbreak risk to the ward level, and pushes traceable structured risk records to the hospital infection management department, realizing real-time assessment and intelligent early warning of hospital infection risk;

[0016] S6. Output structured risk labels and support infection control measures decisions.

[0017] As a preferred technical solution, in step S1, the structured historical data obtained comes from the pathogen data of the medical institution's data governance department, which guides multiple hospital infection management departments in the region to form unified data collection and data governance specifications, and outputs shared structured data through unified standards.

[0018] As a preferred technical solution, step S3, which involves training the model using labeled data, includes:

[0019] S31. Obtain the characteristic information of infected patients based on historical infection tracing data, and automatically generate structured data labels for infection tracing as labeled data for model training;

[0020] S32. Using ward datasets of different medical care levels as a control, analyze the patterns of antibiotic use, strain distribution, bacterial negative / positive markers in nursing records, and the proportion of intubated patients in the ICU area. Combined with the risk list for each patient, assess the infection risk level of each clinical pathway.

[0021] S33. In the cross-departmental risk association map, the infection risk of all patients within the ward area is distributed to identify potential high-risk sources of infection and target patient groups.

[0022] As a preferred technical solution, step S5 specifically includes:

[0023] S51. Use the infection risk results predicted by the neural network to construct an anonymous data output document oriented towards infection risk, automatically generate an infection risk association directory of the current patient and historical samples, and record the infection risk and bed change path according to risk level and time partition.

[0024] S52. Associate the predicted value of patient infection risk with spatial location and upload it to the central server for data verification. Generate bidirectional structured data in real time, map the predicted value with the bed structure, construct a verification vector based on actual data and model prediction results, and cross-validate it with the structured data uploaded by patients in adjacent wards.

[0025] S53. Create a structured risk catalog in areas at risk of infection outbreaks, including ward names, patient infection risk levels, and records of anti-infection measures implemented. Display an anonymized infection risk correlation diagram on the platform and push the structured risk records to the infection control department to achieve real-time intelligent early warning of nosocomial infection risks.

[0026] As a preferred technical solution, step S51 specifically includes:

[0027] S511. Extract risk evolution data nodes:

[0028] The current patient's predicted infection risk value is time-series-marked with their bed number, nursing operation records and test results during their hospital stay. Key data nodes related to changes in infection risk are extracted to form an infection risk evolution sequence on the time axis.

[0029] S512. Generate risk path relationship pairs:

[0030] Based on the infection risk evolution sequence, the mapping relationship between bed numbers and corresponding risk levels in different time periods is identified, and risk path relationship pairs with "bed number-risk level-time point" as the basic unit are constructed to form a structured risk trajectory basic dataset.

[0031] S513. Constructing a multidimensional infection risk map:

[0032] Using the aforementioned risk path relationship pairs, combined with ward floor structure and bed layout data, an infection risk map based on time segment and spatial location dimensions is generated. Map nodes represent the infection risk status of patients at a specific time point, and edges represent bed relocation or ward conversion.

[0033] As a preferred technical solution, step S51 further includes:

[0034] S514. Establish a spatiotemporal indexing mechanism:

[0035] The generated infection risk map is indexed and encoded using both time sequence and spatial numbering to construct a risk map index library that can be used for subsequent retrieval, comparison and source tracing analysis. Each map path can be traced back through time window and spatial location.

[0036] S515, Record the chain of risk level changes:

[0037] In the infection risk association directory, the high-risk transition trajectory of patients during their hospitalization period is recorded according to the trend of risk level changes, forming a "low-medium-high" or "high-medium-low" type level change chain, which is used by the infection control system for trend analysis and early warning activation;

[0038] S516, Output risk propagation code:

[0039] Based on the map index results and the risk level change chain, a unique risk transmission code is generated for each patient. This code consists of a timestamp, ward number, and risk level sequence, and is used to identify the transmission chain of infection risk events within the ward, realizing data-level risk path expression.

[0040] As a preferred technical solution, the patient's antibiotic use duration, strain distribution, nursing operation records and bed change data are normalized, and the predicted values ​​of the current model are compared with the risk labels. The output is a structured risk value that includes risk type, risk value and the ward to which it belongs, and the risk of strain infection is calculated in combination with the bed number.

[0041] This invention also provides a deep learning-based AI-powered intelligent early warning and data management system for hospital infections, used to implement the method, including:

[0042] The data acquisition module is used to integrate electronic medical records, laboratory data, medication records, nursing logs and bed adjustment information to generate structured data in a unified format for model training and risk prediction.

[0043] The model training module is configured with a neural network structure with a gradient descent optimizer and activation function. It has functions such as building an infection sample list, generating labeled data, performing risk classification and evaluating training results. It is used to normalize and train structured data to generate a hospital infection risk prediction model.

[0044] The model reasoning module has a risk assessment mechanism and a label recognition mechanism to determine the infection risk level of an individual patient and output structured infection risk prediction results.

[0045] The risk comparison module is configured with an interoperable comparison structure and risk level output structure for spatial vectorization processing. It compares and integrates the patient's infection risk level with the risk values ​​of neighboring patients to form a structured risk value, which is automatically uploaded and entered into the hospital infection information platform.

[0046] The multi-level assessment module combines structured risk values, timestamp information, and spatial vectors to form an infection risk zone map based on multiple time points for a single person, generating real-time risk alerts and infection risk transmission path maps for medical staff and infection control managers.

[0047] As a preferred technical solution, the system also includes an infection prevention and control business management module, which includes a record registration submodule, a risk warning submodule, a quality supervision submodule, a risk assessment submodule, a training and assessment submodule, and a personal center submodule.

[0048] The system includes the following modules: a registration module for entering infection case records and disinfection records; a risk warning module for real-time monitoring and generating warning alerts based on the infection risk prediction results from the model reasoning module; a quality supervision module for generating supervision plans and managing the supervision execution process and results; a risk assessment module for determining risk levels based on structured risk values ​​and generating control measure records; a training and assessment module for managing infection control knowledge training tasks and assessment records; and a personal center module for managing user information and system settings.

[0049] Beneficial effects:

[0050] This invention deeply integrates deep learning models with multi-source structured data from hospitals to construct an infection risk perception system with three-dimensional analysis capabilities across time, space, and departments. By constructing a neural network model to jointly model information such as historical bacterial infections, antibiotic use, bed relocation, and nursing logs, it breaks through the existing static risk assessment model based on single factors, and realizes fine-grained infection risk prediction based on dynamic ward information, providing highly sensitive feedforward signals for early warning and deployment of infectious disease prevention and control strategies.

[0051] This invention establishes a clear trajectory of infection risk evolution for patients across time and space by constructing "infection risk pathway relationships" and "risk transmission maps." This mechanism not only identifies high-risk patients but also inverts potential transmission pathways and locates possible secondary transmission sources, providing a stronger data foundation for infection source tracing analysis and precise containment strategies. Furthermore, the introduction of a structured risk index and a unique transmission coding mechanism enables risk events to be traceable and digitally identified throughout their entire process, providing primary evidence for subsequent clinical review, quality control tracking, and hospital infection control research.

[0052] At the system level, the multi-module collaborative structure designed in this invention realizes a closed-loop response process from data acquisition, model training, predictive inference, spatial comparison to multi-level evaluation. The modules are interconnected through structured data, avoiding information fragmentation and response delays common in manual intervention. This system is particularly suitable for key areas with highly dynamic infection risks, such as ICUs, infectious disease departments, and geriatric wards. It can complete spatial prediction and risk layer output before infection spreads, demonstrating significant scene adaptability and intelligent response capabilities. Attached Figure Description

[0053] Figure 1 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0054] To enhance understanding of the present invention, the present invention will be further described in detail below with reference to embodiments. These embodiments are only used to explain the present invention and do not constitute a limitation on the scope of protection of the present invention.

[0055] Example 1

[0056] according to Figure 1 As shown in the figure, this embodiment provides a method for intelligent early warning of hospital infection risk based on deep learning. It is suitable for integration into hospital information systems or infection risk early warning platforms, and can predict and intervene in potential hospital infection risks in real time while ensuring patient privacy and security.

[0057] S1. Construct a deep learning model for hospital infection risk based on structured historical data:

[0058] In this step, the hospital's data governance center first retrieves structured inpatient data related to patients from the past three years. Data fields include, but are not limited to: basic patient information (such as age, gender, and hospital number), disease diagnosis codes, laboratory reports, bacterial culture results, antibiotic usage records, nursing logs, bed adjustment information, and transfer records. All data fields are anonymized and uniformly mapped to the hospital's unified data standard system.

[0059] Subsequently, the aforementioned multi-source heterogeneous data were uniformly formatted to construct an input feature matrix. Among them, antibiotic usage data were coded with daily dosage and antimicrobial spectrum range, strain information was classified according to microbial species and drug resistance spectrum, daily operation frequency and key high-risk operations (such as urinary catheterization and endotracheal intubation) were extracted from nursing records and marked and coded, and bed adjustment information was converted into time series format and marked with spatial information such as specific department, floor and bed number.

[0060] The neural network model employs a three-layer fully connected structure plus an embedding layer, a normalization layer, and a Dropout mechanism. The input feature dimension is 128, the activation function is ReLU, and the output layer uses Softmax logistic regression to output the current infection risk level (low, medium, high). Model training utilizes supervised learning, with labels generated from real infection results in the hospital infection reporting system (e.g., whether a specific type of infection occurred during hospitalization).

[0061] All trained model parameters and weights are stored on a local hospital server or multi-center encrypted nodes for easy model updates and deployment.

[0062] S2. Collect multi-source medical data and generate model input features in a unified format:

[0063] In actual operation, the model connects to the real-time data streams of hospital business systems such as HIS (Hospital Information System), LIS (Laboratory System), and EMR (Electronic Medical Record), and collects data in a standardized manner through the data platform interface to ensure that the input data fields are consistent with those in the training phase.

[0064] For example, on the day a patient is admitted to the hospital, the system captures the patient's disease diagnosis code, antibiotic use plan, initial nursing record, initial bed allocation information, etc. in real time, converts them into standard input vectors according to the format required by the model, and inputs them into the deep learning model deployed on the hospital's server to complete the preliminary prediction of infection risk.

[0065] During this process, the model responds to daily data updates, achieving dynamic prediction capabilities for "daily dimension" updates, and providing a continuous risk monitoring view for nursing and infection control departments.

[0066] S3. Split the training dataset and perform normalization training and fitting monitoring of the infection risk model:

[0067] To ensure the model's generalization ability, the structured historical data was divided into three datasets before training: 70% for training, 15% for validation, and 15% for testing. All numerical fields were normalized to 0-1, categorical variables were One-Hot encoded, time fields were converted to time difference quantization features, and spatial fields (bed numbers) were constructed using floor codes and bed numbers to create spatial embedding vectors.

[0068] During training, cross-entropy loss was used as the loss function, and gradient descent was performed using the Adam optimizer. The initial learning rate was set to 0.001 and decayed with each training epoch. The validation set accuracy and loss value were recorded every 5 epochs during the training cycle, and the model fitting curve was plotted to observe whether overfitting or underfitting occurred.

[0069] During training, weighted sensitivity analysis was conducted on key dimensions such as antibiotic use data, bacterial strain distribution maps, and nursing logs for each patient to further identify the model's dependence on key factors and optimize the selection of input dimensions. Ultimately, an infection risk prediction accuracy of approximately 88.4% was achieved on the test set, with good discrimination across different risk levels (low / medium / high).

[0070] S4. Output patient infection risk prediction results and complete spatial domain data comparison and platform reporting:

[0071] After each model completes its inference on a patient's infection risk, the system generates a structured prediction document containing: a unique patient identifier (after anonymization), predicted risk level, corresponding bed number, current timestamp, and key contribution factor scores. All output documents do not contain directly identified information to ensure data privacy.

[0072] The structured document is simultaneously uploaded to the hospital infection risk early warning platform, triggering the spatial domain risk comparison mechanism within the platform. Based on spatial information such as the patient's bed, department, and floor, the system retrieves historical and current infection risk prediction values ​​for adjacent beds (±2 beds) and patients on the same floor to construct a spatial neighborhood risk view, which is then displayed as a heatmap on the infection control platform interface.

[0073] If the predicted value exceeds the set threshold (e.g., a probability value higher than 0.85 indicates high risk), the system will automatically trigger an early warning signal for the infection control department and provide a highlighted prompt in a pop-up window on the screen, supporting rapid intervention.

[0074] S5. Implement ward-level infection risk early warning and coordinated response based on cross-departmental risk association structure:

[0075] In the context of large hospital wards, risk warnings from a single department are often insufficient to reflect the full picture of infection transmission. Therefore, this invention introduces a "cross-departmental risk association structure" mechanism to establish patient flow maps and risk transfer maps between departmental levels within the platform.

[0076] The platform accumulates and statistically analyzes the duration of each patient's stay in different wards and the corresponding risk values, constructing risk transfer relationships along pathways such as from the ICU to general wards and from surgery to rehabilitation. If a ward has a concentration of high-risk patients and a strong inter-departmental flow path, the system automatically identifies the area as a "high-risk area for secondary infection outbreaks" and highlights it on the visualization platform.

[0077] This collaborative mechanism not only prompts infection control departments to conduct inspections of key areas, but also supports the retrospective investigation of "suspected transmission paths," such as the time difference and spatial overlap between the initial case and secondary cases, providing a basis for accountability and early warning plan development.

[0078] S51. Construct an infection risk association directory and a transmission route analysis mechanism:

[0079] This sub-step involves linking the patient's historical risk records with bed change trajectories. The system will automatically generate a patient risk trajectory file based on the daily model prediction results, recording information such as the risk value, bed location, and contact records (such as patients in the same ward) for each day.

[0080] Simultaneously, a triplet based on "bed number - risk level - timestamp" is constructed, and the system sequentially links these triplet groups according to the time sequence to form an infection transmission path chain. For example, if a high-risk patient stays in the ICU for two days and is then transferred to the surgical ward, and subsequently, patients in adjacent beds also show an increased risk, then this path is identified by the system as a suspected transmission path.

[0081] The platform encodes the above paths into a transmission graph, where nodes represent patients, edges represent the probability of transmission, and weights are the product of contact overlap and risk level. The final output is a "transmission chain graph" for the hospital infection control department to perform visualization analysis.

[0082] S52. Constructing a spatiotemporal indexing mechanism and propagation coding system:

[0083] All patient infection pathways are stored in the database and indexed bidirectionally according to time windows (e.g., day, week) and spatial windows (e.g., floor, department, bed). Each pathway generates a unique transmission code, consisting of the following fields: time period number, ward number, and patient pathway hash value.

[0084] When a suspected hospital-acquired infection outbreak occurs, staff can enter the key time period, the initial patient ID, or the ward. The system will quickly retrieve all relevant paths and highlight the paths with the highest probability of transmission to assist in decision-making.

[0085] S53. Construct a directory and visualization map of hospital infection risks to achieve layered management of transmission:

[0086] After the system completes the spatiotemporal path analysis of high-risk patients, this invention further introduces a hospital infection risk map construction mechanism to structurally summarize the infection transmission processes that have occurred and may occur within the ward. The core of this step is to organize the scattered patient infection risk information into an "infection risk catalog" according to time, space, and risk level, and to construct a visual transmission map based on this catalog to support intuitive monitoring and intervention decisions.

[0087] The system first statistically analyzes all wards with infection risk, extracting multi-dimensional indicators including ward number, cumulative number of high-risk patients, anti-infection intervention status, and bed occupancy density within the ward. Then, based on risk level and patient transfer routes, the system establishes a transmission correlation graph between wards, with edge weights representing the superposition of patient flow density and risk value.

[0088] The platform interface displays a risk map containing the following elements: nodes represent wards, beds, or patients; edges represent transmission paths or contact chains; node colors represent risk levels (e.g., green for low, yellow for medium, and red for high); and edge colors reflect transmission intensity. Clicking on any node allows viewing the corresponding infection risk association directory, including patient number, risk label, and timeline information. The map can be played on a timeline to display the dynamic evolution of the transmission process.

[0089] The map also pushes traceable structured layer files (JSON or XML format) to infection control departments, which can be easily imported into third-party platforms for further modeling analysis, decision management, or historical review.

[0090] S6. Output structured risk labels and support infection control measure decisions:

[0091] Ultimately, each patient's daily infection risk value during their hospital stay will be stored in a structured format, with tags including: risk level, top 5 impact factors, spatial location (ward, floor, bed), timestamp, and transmission chain ID.

[0092] If the risk level is medium to high for two consecutive days, the platform will mark the patient as a "watchlist" and display a notification suggesting isolation. When the risk level rises and the transmission chain includes multiple adjacent patients, the system will issue an "infection control activation recommendation" for administrators to decide whether to implement comprehensive intervention measures, such as temporary closure of the area, enhanced protection levels, and bed rearrangement.

[0093] In summary, this embodiment fully demonstrates the entire process from data acquisition, model building, dynamic inference, spatial comparison to infection path analysis. In particular, by using structured mapping and transmission coding systems, it elevates traditional predictive models into infection control management tools with prevention and control capabilities. This process has been simulated and verified in the information system of a tertiary-level Class A hospital, demonstrating portability and scalability.

[0094] Example 2

[0095] This embodiment provides a "Deep Learning-Based AI Intelligent Early Warning and Data Management System for Hospital Infections" applied to medical scenarios. Through the collaboration of modules such as multi-source medical data collection, deep learning model construction and inference, spatial comparison and multi-dimensional visualization, the system realizes automatic identification of hospital infection risks, early warning push and transmission path management.

[0096] The system is deployed in the hospital's local area network server environment or interconnected with the hospital's information system through a cloud platform, serving the infection control department, information department and clinical departments, and is especially suitable for key infection control areas such as ICU, operating room, and geriatric ward.

[0097] The overall system structure includes, but is not limited to, the following five core modules: data acquisition module, model training module, model inference module, risk comparison module, and multi-level assessment module. These modules communicate and interact through standardized interfaces, forming a closed-loop intelligent early warning and management platform.

[0098] 1. Data Acquisition Module:

[0099] This module is responsible for the system's data entry and unified preprocessing. Its main function is to extract raw medical data from multiple business systems in the hospital, perform standardized processing, and finally output structured data in a unified format for the model to call.

[0100] The module consists of three parts: a data access unit, a data cleaning unit, and a feature construction unit.

[0101] The data access unit interfaces with the electronic medical record (EMR), laboratory system (LIS), medical order system (CPOE), nursing system (NIS), and bed management system to obtain raw data through API or data platform interface protocol, including but not limited to: patient basic information, medical records, strain detection reports, antibiotic usage list, nursing operation records, bed number and change time.

[0102] The data cleaning unit performs operations such as unified encoding (e.g., ICD10, LOINC, ATC), missing value completion, and logical consistency verification on the raw data to eliminate invalid or abnormal data and improve the quality of model training.

[0103] The feature building unit transforms the cleaned data into a model input format, for example:

[0104] Antibiotic records are converted into a code that includes "duration of use + drug level + antibacterial spectrum";

[0105] High-risk procedures (such as intubation and urinary catheterization) are extracted from nursing logs and time stamps are generated;

[0106] Bed change records are converted into "time-location" spatial trajectory vectors.

[0107] The final output is a structured feature table updated daily, with uniform fields and a fixed format, for use by the subsequent model training and inference modules.

[0108] 2. Model Training Module:

[0109] This module is responsible for training a hospital infection risk prediction model. Its core task is to build a deep neural network structure and perform supervised training on structured data.

[0110] The module includes a model building unit, a label generation unit, a training engine unit, and a performance evaluation unit.

[0111] The model building unit supports custom neural network architectures, using a three-layer fully connected network (FCN) by default. Optional integration of CNN or LSTM can enhance temporal feature processing capabilities. The model input dimension is a 128-dimensional vector, the activation function is ReLU, the output layer is Softmax, and the output is a three-class label (low, medium, and high risk).

[0112] The label generation unit extracts confirmed infection records from historical infection cases in the hospital, such as positive bacterial detection and infectious disease consultation reports, and aligns them with the time dimension of the data acquisition module to construct a labeled dataset, supporting positive and negative sample balance control.

[0113] The training engine unit is configured with a gradient descent optimizer (such as Adam) and uses the cross-entropy loss function to train the model with GPU support. The training process records key metrics such as training loss, validation accuracy, AUC curve, and learning rate dynamics.

[0114] After training, the performance evaluation unit evaluates the model's performance on the test set, outputting the confusion matrix, F1 score, PR curve, and Top-N accuracy. It also interprets and analyzes the contribution of each input feature using SHAP values, providing a basis for subsequent model tuning.

[0115] The trained model is saved in the model library as a binary file, along with metadata such as version number, training time, and participating fields.

[0116] 3. Model Inference Module:

[0117] The model inference module is used to input the structured features collected daily into the trained model, output individualized infection risk prediction results, and generate structured risk documents.

[0118] This module includes a model loading unit, an inference execution unit, a risk scoring generation unit, and a result output unit.

[0119] The model loading unit can automatically identify the latest stable version in the model library and load the corresponding parameters and architecture, supporting dynamic switching and rollback.

[0120] The inference execution unit receives feature vectors from the data acquisition module, inputs them into the model, and completes the prediction of the infection risk level. The prediction result is one of three labels, along with the prediction confidence level.

[0121] The risk score generation unit further calculates the influence of each feature on the prediction results and generates a list of "Top 5 Contributing Factors", including the indicator name, direction of influence and intensity value, for result interpretation and clinical guidance.

[0122] The output unit encapsulates the prediction results into a JSON-formatted structured document, which includes patient ID (de-identified), predicted risk level, predicted probability, Top 5 impact factors, current bed information, timestamp, and other information, and then transmits it to the risk comparison module and the hospital infection platform.

[0123] This module supports batch inference tasks, such as performing unified inference on all inpatients in the hospital every day to generate an overview map of hospital infection risk for that day.

[0124] 4. Risk Comparison Module:

[0125] The risk comparison module aims to place individualized prediction results in spatial and group environments for horizontal comparison, and identify phenomena such as spatial clustering of high-risk areas and abnormal change trajectories.

[0126] The module includes a spatial vector construction unit, a nearby patient retrieval unit, a structured risk fusion unit, and a platform interaction unit.

[0127] The spatial vector construction unit converts bed numbers into physical coordinates, establishing a location mapping between the hospital ward floor plan and the bed layout. Each patient will have a "spatial vector" identifying their physical location.

[0128] The neighboring patient retrieval unit retrieves other patients whose spatial distance is less than a specified threshold (such as those within 3 meters or in the same ward) based on spatial distance and ward logic, and constructs a comparison group.

[0129] The structured risk fusion unit integrates and analyzes the risk levels of the target patient and its neighboring patients, identifies situations where risk differences are significant or both are simultaneously elevated, and automatically generates a "spatial risk heat map" and a "group risk index".

[0130] The platform interaction unit sends the comparison results to the hospital infection platform in the form of charts and interfaces to achieve real-time visualization, and pushes warnings of high-risk spatial locations to the hospital infection control department.

[0131] 5. Multi-level evaluation module:

[0132] This module is the upper-level application module of the system, providing medical staff and hospital infection control managers with visualization map and transmission path analysis functions, supporting trend identification, outbreak prediction and source tracing.

[0133] The module includes a risk mapping construction unit, a path reconstruction unit, a risk propagation coding unit, and a multi-dimensional display unit.

[0134] The risk mapping construction unit constructs a three-dimensional "time-space-risk" map based on daily prediction results and bed change records. Nodes represent the patient's risk status at a specific time, and edges represent bed migration and contact relationships.

[0135] The path reconstruction unit is based on graph analysis of infection path chains, such as identifying high-risk transmission paths from ICU to internal medicine, and supports tracing path evolution by ward, patient, and bed.

[0136] The risk propagation coding unit generates a unique propagation code for each path, with the structure of "ward number-time stamp-risk sequence", which is used for log storage, subsequent analysis and accountability tracking.

[0137] The multi-dimensional display unit provides a web-based interactive interface that supports map-style ward distribution, path animation playback, filtering and focusing, and report export, helping administrators make precise intervention decisions.

[0138] 6. Infection Control and Prevention Business Management Module:

[0139] This module is used to map the results of risk prediction, spatial comparison, and map analysis to infection management business processes, and to form traceable business records and management objects. The module includes:

[0140] (1) Record Registration Submodule: Used to enter data such as infection case records and disinfection records, and to generate timestamps, department / ward identifiers and associated patient anonymity identifiers for the records;

[0141] (2) Risk warning submodule: used to receive the predicted risk level and prediction confidence output by the model inference module, and generate warning reminder records by combining the spatial aggregation information output by the risk comparison module;

[0142] (3) Quality Supervision Submodule: Used to maintain the elements of the supervision plan and record the supervision execution process and supervision results. The supervision plan shall include at least the supervision object, supervision frequency, supervision items and the responsible person identification.

[0143] (4) Risk assessment submodule: used to generate risk level records based on structured risk values ​​and generate control measure records corresponding to the risk levels. The control measure records shall include at least the measure type, triggering conditions, implementing department and implementation time window.

[0144] (5) Training and assessment sub-module: used to maintain infection control knowledge training tasks, trainee identification, assessment question bank identification and assessment result records;

[0145] (6) Personal Center Submodule: Used to maintain user information, permission configuration and system setting parameters. The system setting parameters include at least warning threshold parameters, supervision frequency parameters and message push parameters.

[0146] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A deep learning-based AI-powered intelligent early warning and data management method for hospital infection control, characterized in that, Includes the following steps: S1. Construct a deep learning model for hospital infection risk based on structured historical data: A deep learning model for hospital infection risk was established. Structured historical data containing patient identity information, disease type, test results, strain infection information, antibiotic use records, nursing records and bed information was obtained. The structured data was used as input features to supervise the learning of deterministic results of hospital infection. The structured data was input into the neural network for training, and the weight parameters obtained from the training were stored on the local server. S2. Collect multi-source medical data and generate model input features in a unified format: Multi-source medical data is collected in real time in the hospital information system to form input features in a unified format, and then input into the deep learning model for infection risk prediction. S3. Split the training dataset and perform normalization training and fitting monitoring of the infection risk model: The structured data was divided into training, validation and test sets. The patient's antibiotic use information, strain distribution, nursing operation frequency and bed change data were normalized. The model was trained in combination with labeled data. The convergence process of the network loss function was recorded. The loss value and accuracy index during the model fitting process were monitored and the risk prediction results were output. The steps for training a model using labeled data include: S31. Obtain the characteristic information of infected patients based on historical infection tracing data, and automatically generate structured data labels for infection tracing as labeled data for model training; S32. Using ward datasets of different medical care levels as a control, analyze the patterns of antibiotic use, strain distribution, bacterial negative / positive markers in nursing records, and the proportion of intubated patients in the ICU area. Combined with the risk list for each patient, assess the infection risk level of each clinical pathway. S33. In the cross-departmental risk association map, the infection risk of all patients within the ward area is distributed to identify potential high-risk sources of infection and target patient groups; S4. Output patient infection risk prediction results and complete spatial domain data comparison and platform reporting: Anonymized structured output files are generated based on the current patient infection risk prediction values ​​and uploaded to the central server. The data is then verified against real-time data and existing data, and compared with the prediction values ​​of patients in adjacent beds and on the same floor. The infection risk prediction values ​​of high-risk patients are reported to the infection risk early warning platform. S5. Implement ward-level infection risk early warning and coordinated response based on cross-departmental risk association structure: Specifically, it includes: S51. Use the infection risk results predicted by the neural network to construct an anonymous data output document oriented towards infection risk, automatically generate an infection risk association directory of the current patient and historical samples, and record the infection risk and bed change path according to risk level and time partition. S52. Associate the predicted value of patient infection risk with spatial location and upload it to the central server for data verification. Generate bidirectional structured data in real time, map the predicted value with the bed structure, construct a verification vector based on actual data and model prediction results, and cross-validate it with the structured data uploaded by patients in adjacent wards. S53. Create a structured risk catalog in areas at risk of infection outbreaks, including ward names, patient infection risk levels and records of anti-infection measures, and display an anonymized infection risk correlation diagram on the platform. Push the structured risk records to the infection control department to achieve real-time intelligent early warning of nosocomial infection risks. S6. Output structured risk labels and support infection control measures decisions.

2. The AI-based intelligent early warning and data management method for hospital infection based on deep learning according to claim 1, characterized in that: In step S1, the acquired structured historical data comes from pathogen data from the data governance department of medical institutions, which guides multiple hospital infection control departments in the region to form unified data collection and data governance standards, and outputs shared structured data through unified standards.

3. The AI-based intelligent early warning and data management method for hospital infection based on deep learning according to claim 1, characterized in that: Step S51 specifically includes: S511. Extract risk evolution data nodes: The current patient's predicted infection risk value is time-series-marked with their bed number, nursing operation records and test results during their hospital stay. Key data nodes related to changes in infection risk are extracted to form an infection risk evolution sequence on the time axis. S512. Generate risk path relationship pairs: Based on the infection risk evolution sequence, the mapping relationship between bed numbers and corresponding risk levels in different time periods is identified, and risk path relationship pairs with "bed number-risk level-time point" as the basic unit are constructed to form a structured risk trajectory basic dataset; S513. Constructing a multidimensional infection risk map: Using the aforementioned risk path relationship pairs, combined with ward floor structure and bed layout data, an infection risk map based on time segment and spatial location dimensions is generated. Map nodes represent the infection risk status of patients at a specific time point, and edges represent bed relocation or ward conversion.

4. The AI-based intelligent early warning and data management method for hospital infection based on deep learning according to claim 1, characterized in that: Step S51 also includes: S514. Establish a spatiotemporal indexing mechanism: The generated infection risk map is indexed and encoded using both time sequence and spatial numbering to construct a risk map index library that can be used for subsequent retrieval, comparison and source tracing analysis. Each map path can be traced back through time window and spatial location. S515, Record the chain of risk level changes: In the infection risk association directory, the high-risk transition trajectory of patients during their hospitalization period is recorded according to the trend of risk level changes, forming a "low-medium-high" or "high-medium-low" type level change chain, which is used by the infection control system for trend analysis and early warning activation; S516, Output risk propagation code: Based on the map index results and the risk level change chain, a unique risk transmission code is generated for each patient. This code consists of a timestamp, ward number, and risk level sequence, and is used to identify the transmission chain of infection risk events within the ward, realizing data-level risk path expression.

5. The AI-based intelligent early warning and data management method for hospital infection based on deep learning according to claim 1, characterized in that: The data on the duration of antibiotic use, strain distribution, nursing operation records, and bed change were normalized. Based on the risk label, the predicted values ​​of the current model were compared, and a structured risk value containing the risk type, risk value, and ward was output. The risk of strain infection was calculated in combination with the bed number.

6. A hospital infection AI-based intelligent early warning and data management system based on deep learning, characterized in that, For implementing the method as described in any one of claims 1-5, comprising: The data acquisition module is used to integrate electronic medical records, laboratory data, medication records, nursing logs and bed adjustment information to generate structured data in a unified format for model training and risk prediction. The model training module is configured with a neural network structure with a gradient descent optimizer and activation function. It has functions such as building an infection sample list, generating labeled data, performing risk classification and evaluating training results. It is used to normalize and train structured data to generate a hospital infection risk prediction model. The model reasoning module has a risk assessment mechanism and a label recognition mechanism to determine the infection risk level of an individual patient and output structured infection risk prediction results. The risk comparison module is configured with an interoperable comparison structure and risk level output structure for spatial vectorization processing. It compares and integrates the patient's infection risk level with the risk values ​​of neighboring patients to form a structured risk value, which is automatically uploaded and entered into the hospital infection information platform. The multi-level assessment module combines structured risk values, timestamp information, and spatial vectors to form an infection risk zone map based on multiple time points for a single person, generating real-time risk alerts and infection risk transmission path maps for medical staff and infection control managers.

7. The hospital infection AI intelligent early warning and data management system based on deep learning according to claim 6, characterized in that: The system also includes an infection control business management module, which includes a record registration submodule, a risk warning submodule, a quality supervision submodule, a risk assessment submodule, a training and assessment submodule, and a personal center submodule. The system includes the following modules: a registration module for entering infection case records and disinfection records; a risk warning module for real-time monitoring and generating warning alerts based on the infection risk prediction results from the model reasoning module; a quality supervision module for generating supervision plans and managing the supervision execution process and results; a risk assessment module for determining risk levels based on structured risk values ​​and generating control measure records; a training and assessment module for managing infection control knowledge training tasks and assessment records; and a personal center module for managing user information and system settings.

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