Classification of medical documents and Opinions using machine learning

Abstract

In recent years, there has been increasing interest in the use of text mining (TM) and machine learning in healthcare. Document classi cation has been a common application, with many studies focusing on classifying medical reports from unstructured text data. However, there is also a need to utilize TM and machine learning for sentiment analysis of medical textual data in social networks and medical forums. In this thesis, the focus was on two TM applications in the medical domain: classifying autopsy reports to detect the manner of death in Wilaya of Tlemcen and analyzing patient and public opinions on healthcare and the COVID-19 pandemic using machine learning techniques. The experiments conducted in both studies showed that automated models for opinion analysis are task-speci c and that feature extraction and deep learning classi er architecture play important roles in the success of these models. The ndings could be useful for improving strategies related to drugs monitoring and COVID-19 surveillance. Future directions include exploring other types of deep learning techniques, using clinical documents for sentiment analysis, and analyzing Algerian health status based on machine learning and deep learning classi ers.