Prototype System for Supporting Medical Diagnosis Based on Voice Interviewing

Abstract

In this paper, we present the results of a study on the development of a system to support medical diagnoses based on voice-based medical interviews. The main objective is to develop a tool that improves the process of collecting information from patients, using natural language analysis to identify key diagnostic information. The system processes the collected data to create information vectors for a selected group of diseases, allowing for an initial assessment of possible disease entities. An analysis of data mining and selected machine learning methods was carried out to develop an effective diagnosis algorithm. The system is designed to optimise patient care by automating the initial phase of the medical interview, which can lead to a reduction in errors due to subjective assessments and reduce the workload on doctors. The solution presented in this paper is part of a broader research project to develop a Medical Interview system, and this paper is the first article in a series that describes the experiences and challenges of implementing this system. Further work is planned to develop the model into advanced medical decision support techniques and validate it in a clinical setting.

Keywords

• voice interviews
• natural language processing (NLP)
• medical interview system
• diagnostic support system