Extracting and Processing of Russian Unstructured Clinical Texts for a Medical Decision Support System

Engineering Proceedings

article

Irina Bolodurina¹  Alexander Shukhman¹  Leonid Legashev¹  Lyubov Grishina¹  Arthur Zhigalov¹ 

[1] Research Institute of Digital Intelligent Technologies, Orenburg State University;Department of Public Health and Healthcare, Orenburg State Medical University

关键词: electronic health records;    medical decision support system;    natural language processing;    BERT;    logistic regression;   

DOI  :  10.3390/engproc2023033041

来源: mdpi

【 摘 要 】

The rapid growth in the volume of medical data is pushing the development and implementation of artificial intelligence (AI) tools. One of the directions of the application of AI in the field of healthcare is the use of natural language processing methods to build medical decision support systems based on electronic medical record (EMC) data. As a result of this study, a module for the extraction and pretreatment of patients’ EMC was developed. In addition, an approach was implemented to extract features from the unstructured textual information of patient admission protocols, with the formation of an appropriate vector representation of data. Predictive models for the diagnosis of groups of diseases based on the logistic regression model and BERT were developed. The highest efficiency in the experiments was shown by the logistic regression model, with a F1-score of 0.81 and Matthews correlation coefficient of 0.75. The obtained results have been posted for public access based on the django framework and can be used for preliminary assessment of patient health status, as well as integrated into existing medical decision support systems.

【 授权许可】

Unknown