Machine Learning Based Text Mining in Electronic Health Records: Cardiovascular Patient Cases

This article presents the approach and experimental study results of machine learning based text mining methods with application for EHR analysis. It is shown how the application of ML-based text mining methods to identify classes and features correlation to increases the possibility of prediction models. The analysis of the data in EHR has significant importance because it contains valuable information that is crucial for the decision-making process during patient treatment. The preprocessing of EHR using regular expressions and the means of vectorization and clustering medical texts data is shown. The correlation analysis confirms the dependence between the found classes of diagnosis and individual characteristics of patients and episodes. The medical interpretation of the findings is also presented with the support of physicians from the specialized medical center, which confirms the effectiveness of the shown approach.