HIV / AIDS patient intelligent monitoring and early warning system based on multi-modal data fusion

Abstract

The invention provides an HIV / AIDS patient intelligent monitoring and early warning system based on multi-modal data fusion, and belongs to the technical field of medical data processing. The system is composed of a data acquisition system, a data processing system, a multi-modal data fusion system and an intelligent detection and early warning system. By integrating multi-source heterogeneous medical data, establishing an accurate early warning model, providing personalized treatment suggestions, enhancing patient remote monitoring and enhancing data privacy protection, the technical problem of an existing HIV / AIDS patient monitoring and early warning system is solved. According to the scheme, the data utilization efficiency is improved, the early warning accuracy is enhanced, personalized treatment is optimized, patient management is enhanced, meanwhile, data security and privacy are protected, more comprehensive, accurate and personalized medical support is provided for HIV / AIDS patients, and the treatment effect and life quality are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of medical data processing, and in particular relates to an intelligent monitoring and early warning system for HIV / AIDS patients based on multimodal data fusion. Background Art

[0002] HIV infection / acquired immunodeficiency syndrome (HIV / AIDS) is a chronic infectious disease whose management requires long-term monitoring and intervention. However, existing HIV / AIDS disease management systems are relatively limited in functionality, primarily focusing on recording patient diagnosis and treatment information, such as laboratory test results, medication records, and follow-up information. This passive recording approach lacks the ability to proactively manage and intervene with patients, making it difficult to meet their increasingly diverse and personalized health needs. For example, existing systems fail to fully integrate multi-dimensional information such as patients' lifestyles, psychological states, and social support, resulting in limited management effectiveness and an inability to effectively respond to complex changes in the disease.

[0003] Existing technologies for disease management of HIV / AIDS patients primarily rely on basic SMS reminder systems, mobile applications, and electronic medical record systems. However, these existing technologies suffer from numerous deficiencies and fail to fundamentally address the lack of initiative and comprehensiveness in disease management. Summary of the Invention

[0004] In order to solve the problem of insufficient initiative and comprehensiveness of existing technologies in disease management, the present invention introduces multimodal data fusion technology and intelligent early warning algorithms to achieve comprehensive monitoring, dynamic analysis and accurate early warning of patients' health status, thereby improving the scientificity, efficiency and effectiveness of disease management, improving patients' quality of life and reducing the workload of medical staff.

[0005] The purpose of the present invention is to provide an intelligent monitoring and early warning system for HIV / AIDS patients based on multimodal data fusion.

[0006] The present invention provides an intelligent monitoring and early warning system for HIV / AIDS patients based on multimodal data fusion, comprising:

[0007] A data acquisition system for collecting multimodal data of patients;

[0008] A data processing system is used to pre-process the multimodal data collected by the data acquisition system;

[0009] The multimodal data fusion system, based on deep learning and machine learning technology, fuses the pre-processed multimodal data to generate a unified feature vector;

[0010] The intelligent detection and early warning system performs intelligent detection on the feature vectors input by the multimodal data fusion system and generates a data visualization health report; or, generates data visualization abnormality warnings and intervention suggestions.

[0011] Furthermore, in the data collection system, the multimodal data collection method includes laboratory testing, electronic medical records, psychological assessment and patient terminal data input.

[0012] Furthermore, in the data acquisition system, the multimodal data includes laboratory test data, clinical text records, imaging data and mental health assessment data.

[0013] Furthermore, in the data processing system, the preprocessing includes data cleaning, standardization and feature extraction.

[0014] Furthermore, in the multimodal data fusion system, the deep learning technology is selected from Transformer or LSTM; the machine learning technology is selected from multimodal neural network or attention mechanism.

[0015] Furthermore, in the intelligent detection and early warning system, the intelligent detection includes comparing the feature vector with a normal range value in a knowledge base.

[0016] The present invention has achieved the following beneficial effects:

[0017] The key technical points of the present invention include multimodal data fusion technology based on deep learning, which realizes the precise integration of multimodal data through modal encoding and dynamic weight allocation of attention mechanism; intelligent monitoring and dynamic early warning technology, which combines anomaly detection algorithm and personalized threshold setting to identify patients' health status risks in real time and generate accurate early warnings; personalized intervention decision support, which relies on the disease management knowledge base based on knowledge graph, and uses rule reasoning and machine learning to generate optimized intervention plans; efficient data processing and encryption storage technology, which supports real-time data cleaning, standardization and distributed encrypted storage to ensure data security and efficiency; visual interaction and multi-user support, which displays health status, early warning results and intervention suggestions through role authority management and a friendly graphical user interface to meet the usage needs of patients, medical staff and system administrators; and modular and scalable design, which supports flexible expansion of functional modules and the access of new data modalities, comprehensively improving the intelligence, precision and applicability of the system.

[0018] Obviously, based on the above contents of the present invention, according to common technical knowledge and customary means in this field, without departing from the above basic technical ideas of the present invention, other various forms of modifications, replacements or changes can be made.

[0019] The following is a further detailed description of the present invention through specific embodiments in the form of examples. However, this should not be construed as limiting the scope of the present invention to the following examples. All technologies implemented based on the above-mentioned content of the present invention fall within the scope of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is the composition of the system of the present invention.

[0021] Figure 2 4 is a flowchart of the working process of the system of the present invention. DETAILED DESCRIPTION

[0022] It should be noted that the algorithms for data collection, transmission, storage and processing steps not specifically described in the embodiments, as well as the hardware structures, circuit connections, etc. not specifically described can all be implemented through the disclosed content of the prior art.

[0023] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for which protection is claimed, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0024] Example 1

[0025] The intelligent monitoring and early warning system for HIV / AIDS patients based on multimodal data fusion of the present invention is composed of four parts: a data acquisition system 10, a data processing system 20, a multimodal data fusion system 30 and an intelligent detection and early warning system 40 ( Figure 1 ).

[0026] 1. Data acquisition system 10

[0027] Used to collect multimodal data from patients, including but not limited to: laboratory test data; clinical text records (such as medical record summaries, medical advice, etc.); imaging data and mental health assessment data (such as questionnaire scores, voice emotion analysis results, etc.).

[0028] 2. Data processing system 20

[0029] Used to preprocess the collected multimodal data, including data cleaning, standardization and feature extraction.

[0030] 3. Multimodal data fusion system 30

[0031] Based on deep learning and machine learning technologies (such as multimodal neural networks, attention mechanisms, etc.), data from different modalities are fused to generate a unified patient health feature vector.

[0032] 4. Intelligent detection and early warning system 40

[0033] The system analyzes the patient's health status characteristics in real time and compares them with the normal thresholds and dynamic models in the disease management knowledge base. When it detects that the patient's health status may deteriorate, the system automatically triggers an early warning and generates targeted intervention recommendations.

[0034] The steps of the system of the present invention when working include: Figure 2 ):

[0035] (1) Users log in to the system to input data, including laboratory tests, electronic medical records, psychological assessments, and patient terminal data input.

[0036] (2) Preprocessing of multimodal data, including data cleaning, standardization and feature extraction.

[0037] (3) Encode the multimodal data features based on a deep learning model (such as Transformer or LSTM), use the attention mechanism to perform weighted fusion on the data of each modality, and generate the patient health feature vector.

[0038] (4) The health feature vector is compared with the normal range value in the knowledge base to generate a health status report. When an abnormality is detected, an early warning signal is generated based on the machine learning algorithm and intervention measures are recommended.

[0039] (5) The system displays patient health status trend charts, early warning information, and personalized intervention recommendations to medical staff through an interactive interface.

[0040] (6) The corresponding node releases computing resources and the process ends.

[0041] In summary, the present invention provides an intelligent monitoring and early warning system for HIV / AIDS patients based on multimodal data fusion. The system comprises a data acquisition system, a data processing system, a multimodal data fusion system, and an intelligent detection and early warning system. By integrating multi-source heterogeneous medical data, establishing a precise early warning model, providing personalized treatment recommendations, strengthening remote patient monitoring, and enhancing data privacy protection, the present invention addresses the technical challenges of existing HIV / AIDS patient monitoring and early warning systems. This solution improves data utilization efficiency, enhances early warning accuracy, optimizes personalized treatment, strengthens patient management, and protects data security and privacy. This provides HIV / AIDS patients with more comprehensive, precise, and personalized medical support, improving treatment outcomes and quality of life.

Claims

1. An intelligent monitoring and early warning system for HIV / AIDS patients based on multimodal data fusion, characterized by: include: A data acquisition system for collecting multimodal data of patients; A data processing system is used to pre-process the multimodal data collected by the data acquisition system; The multimodal data fusion system, based on deep learning and machine learning technology, fuses the pre-processed multimodal data to generate a unified feature vector; The intelligent detection and early warning system performs intelligent detection on the feature vectors input by the multimodal data fusion system and generates a data visualization health report; or, generates data visualization abnormality warnings and intervention suggestions.

2. The intelligent monitoring and early warning system for HIV / AIDS patients according to claim 1, characterized in that: In the data acquisition system, the multimodal data acquisition methods include laboratory testing, electronic medical records, psychological assessment and patient terminal data input.

3. The intelligent monitoring and early warning system for HIV / AIDS patients according to claim 1, characterized in that: In the data acquisition system, the multimodal data includes laboratory test data, clinical text records, imaging data and mental health assessment data.

4. The intelligent monitoring and early warning system for HIV / AIDS patients according to claim 1, characterized in that: In the data processing system, the preprocessing includes data cleaning, standardization and feature extraction.

5. The intelligent monitoring and early warning system for HIV / AIDS patients according to claim 1, characterized in that: In the multimodal data fusion system, the deep learning technology is selected from Transformer or LSTM; the machine learning technology is selected from multimodal neural network or attention mechanism.

6. The intelligent monitoring and early warning system for HIV / AIDS patients according to claim 1, characterized in that: In the intelligent detection and early warning system, the intelligent detection includes comparing the feature vector with the normal range value in the knowledge base.

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