Disease diagnosis from unstructured medical texts using machine learning techniques

Modern machine learning methods open new opportunities for analyzing medical texts. The use of unstructured data enables improved clinical decision support and the development of personalized patient treatment approaches. The aim of the study: to develop an optimal algorithm for disease prediction using multi-label classification based on medical texts from selected patient treatment cases. Materials and methods. The study utilized anonymized electronic medical records of 387 590 patients. Textual data were processed using lemmatization and vectorization based on a pretrained FastText model. A multi-label classification model was developed to predict 156 diagnostic categories grouped by major disease classes. Neural network architectures and decision tree ensembles were applied for model building. Results. The proposed models demonstrated high effectiveness. The use of various text vector aggregation methods improved prediction quality. The model showed stability and clinical interpretability, supporting its applicability in real-world medical practice. Conclusion. The developed approach to analyzing unstructured medical texts using machine learning methods is a promising tool for disease diagnosis support. Further research will focus on improving model interpretability and adapting models to diverse clinical data sources.