Pneumoconiosis database establishment system and method

Abstract

The invention discloses a pneumoconiosis database establishment system and method. A database system comprises a data entry module, a data storage module and a data sharing module, and an intelligent processing system comprises a data management module, a diagnostic analysis module and an intelligent assistant. And the database system is matched with the intelligent processing system to realize sharing and multiple purposes of the database. The method has the following beneficial effects: 1, the accuracy, integrity and consistency of data are improved; 2, the burden of doctors and researchers is relieved; 3, multi-party cooperative communication is supported; 4, personalized diagnosis and treatment are realized through data analysis assistance; 5, the consistency and accuracy of the information are ensured; 6, performing real-time processing and optimized storage of the data by using cloud computing and artificial intelligence technologies; and 7, deeply mining data from different angles by using a statistical analysis tool.

Description

Technical Field

[0001] The present invention belongs to the technical field of pneumoconiosis database, and in particular relates to a system and method for establishing a pneumoconiosis database. Background Art

[0002] Pneumoconiosis is a general term for a type of occupational lung disease characterized by diffuse pulmonary fibrosis caused by long-term inhalation of pathogenic mineral dusts of varying degrees during occupational activities and their subsequent retention in the lungs. Pneumoconiosis remains the most serious and common occupational disease in my country. As of the end of 2021, my country's occupational disease reporting system alone had reported 915,000 cases of pneumoconiosis, with approximately 450,000 active cases, primarily silicosis and coal workers' pneumoconiosis.

[0003] The database was established by collecting the case's demographic information (name, gender, age, ID number, home address, mobile phone number, etc.), medical history, occupational history information (dust exposure years, employer information, type of dust exposure), pulmonary function tests, single or multiple high-kilovolt DR chest X-ray images, chest high-resolution CT or energy spectrum CT images, diagnosis time and diagnosis information, hospitalization information, pathological diagnosis results, etc.

[0004] To provide a basis for the prevention, disease assessment, health management diagnosis, differential diagnosis and treatment of pneumoconiosis, to provide support for scientific research on pneumoconiosis, and to provide a communication platform for the research on pneumoconiosis. Summary of the Invention

[0005] The present invention provides a basis for the prevention, disease assessment, health management, diagnosis and differential diagnosis, and treatment of pneumoconiosis by collecting detailed information, occupational history, and health status of pneumoconiosis patients, supports scientific research, promotes research on the prevention and treatment of pneumoconiosis, and provides a support platform for multi-center collaboration. The system and method for establishing a pneumoconiosis database include a database system, an intelligent processing system, and a data visualization module. The database system includes a data entry module, a data storage module, and a data sharing module, and the intelligent processing system includes a data management module, a diagnosis and analysis module, and an intelligent assistant.

[0006] The data entry module is used by medical personnel to enter patients' personal information, occupational history, and health examination reports. It uses identity authentication, facial recognition combined with passwords to prevent unauthorized users from logging in.

[0007] The data storage module is used to store the patient's demographic information, including but not limited to name, gender, age, ID number, home address, and contact information. The module ensures data normalization through a unified standard format. It also has a knowledge base and precise search function to enable online teaching and learning of pneumoconiosis diagnosis.

[0008] The data sharing module builds a decentralized electronic medical record database based on blockchain technology to ensure data security and immutability. It supports hospitals at all levels to store patients' medical records in blockchain network nodes and provide cross-institutional medical record sharing services; it supports cross-institutional data sharing, transmission and real-time updates;

[0009] The data management module embeds commonly used medical statistical models to implement online scientific statistics, and is used to organize, classify and archive data, and to adjust and archive stored data according to actual needs;

[0010] Diagnostic analysis module: Use statistical analysis methods (such as the Z-score method and box plot method) and data visualization methods (drawing scatter plots, box plots, etc.) to identify outliers. Further analysis should be conducted on outliers to determine whether they are caused by data collection or entry errors or are true extreme values. If they are caused by errors, they should be corrected or deleted; if they are true values, they can be processed separately or retained but marked to avoid misleading the data analysis results. Data analysis tools and support vector machines or machine learning technologies are then used to analyze and compare data from different centers to assist in diagnosis and treatment decisions; data analysis can support the implementation of personalized diagnosis and treatment, thereby improving patient treatment outcomes.

[0011] Intelligent assistant, integrated into the intelligent processing system, provides guidance to users based on their needs and assists in completing daily operations;

[0012] The data visualization module is a liquid crystal display that supports the graphical display of analysis results.

[0013] Furthermore, the data entry module uses HL7 or FHIR as the data standard to ensure data consistency and shareability across different medical centers.

[0014] Furthermore, the demographic information includes name, gender, age, ID number, home address, mobile phone number, etc.

[0015] Furthermore, the diagnostic analysis module performs real-time data processing through cloud computing and artificial intelligence technologies.

[0016] Furthermore, the diagnostic analysis module compares the data with the data in the data storage module after analysis, prompts abnormal data, and optimizes storage.

[0017] In summary, the present invention has the following beneficial effects: 1. Improving the accuracy, completeness and consistency of data and reducing data redundancy; 2. Reducing the burden on doctors and researchers, speeding up the statistical analysis process, and making investigations more efficient; 3. Supporting scientific research, providing a multi-party collaborative communication platform, and promoting the prevention and treatment research and technical application of pneumoconiosis; 4. Assisting in personalized diagnosis and treatment through data analysis, thereby improving the treatment effect of patients; 5. Collecting patient information and occupational history data through standardized data forms to ensure the consistency and accuracy of information; 6. Using cloud computing and artificial intelligence technologies for real-time data processing and optimized storage, thereby improving the efficiency and flexibility of data management; 7. Using statistical analysis tools to deeply explore data from different angles and discover the correlation and regularity therein. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 Flowchart of the present invention. DETAILED DESCRIPTION

[0019] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments:

[0020] Example 1

[0021] In specific operations:

[0022] 1. Collect demographic information of the case;

[0023] 2. Collect medical and occupational history information, including length of time exposed to dust, employer information, type of dust exposure, specific time of dust exposure, time of medical consultation, time of physical examination, and physical examination results;

[0024] 3. Enter the results of various functional tests, high-kilovolt DR chest radiographs, and high-resolution chest CT or spectral CT images and examination time in the data entry module;

[0025] 4. Record the diagnosis time and information, diagnostic basis and results, and the doctor's experience combined with the results of the diagnostic analysis module to formulate a treatment plan, post-diagnosis condition assessment information, hospitalization information, and treatment results;

[0026] 5. Enter the pathological diagnosis results and follow-up information in the data entry module again. The above entered information will be retained in the data storage module;

[0027] 6. Based on the data storage module, the data management module calls commonly used medical statistical models to organize, classify and archive data;

[0028] 7. Combined with the diagnostic analysis module to generate overall data, enriching the data can improve the accuracy of diagnostic analysis;

[0029] 8. Through the data sharing module, case sharing and communication between different medical institutions can be realized;

[0030] 9. The intelligent assistant provides guidance to users according to their needs and assists in completing daily operations.

[0031] This specific embodiment is merely an explanation of the present invention and is not intended to limit the present invention. After reading this specification, those skilled in the art may make non-creative modifications to this embodiment as needed. However, as long as such modifications are within the scope of the claims of the present invention, they are protected by patent law.

Claims

1. A system and method for establishing a pneumoconiosis database, characterized in that: It includes database system, intelligent processing system and data visualization module; the database system includes data entry module, data storage module and data sharing module, and the intelligent processing system includes data management module and diagnosis and analysis module; The data entry module uses identity authentication, facial recognition combined with passwords, for medical staff to enter patients' demographic information, occupational history and health examination reports; Data storage module: used to store patient input information, ensuring data standardization through a unified standard format, and equipped with a knowledge base; The data sharing module builds a decentralized electronic medical record database based on blockchain technology to ensure data security and immutability. It supports hospitals at all levels to store patients' medical records in blockchain network nodes and provides cross-institutional medical record sharing services. Data management module, which embeds commonly used medical statistical models and is used to organize, classify and archive data; The diagnostic analysis module uses statistical analysis methods and data visualization to identify outliers and utilizes data analysis tools and learning techniques to analyze and compare data from different centers to assist in diagnosis and treatment decision-making. The data visualization module is an LCD display that supports the graphical display of analysis results and is embedded with an intelligent assistant. The intelligent assistant is integrated into the intelligent processing system to provide guidance to users according to their needs.

2. The pneumoconiosis database establishment system according to claim 1, characterized in that: The data entry module uses HL7 or FHIR as the data standard.

3. The pneumoconiosis database establishment system according to claim 1, characterized in that: The demographic information includes but is not limited to name, gender, age, ID number, home address and contact information.

4. The pneumoconiosis database establishment system according to claim 1, characterized in that: The diagnostic analysis module performs real-time data processing through cloud computing and artificial intelligence technology.

5. The pneumoconiosis database establishment system according to claim 1, characterized in that: After analysis, the diagnostic analysis module compares the data with the data in the data storage module and prompts abnormal data.

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