Abstract

This research project aims to develop novel machine learning approaches to process medical information. The amount of data created about patients in the healthcare system is always increasing. The human review of this enormous volume of data derived from numerous sources is expensive and takes a lot of time. Additionally, during a patient visit in developing countries like Nigeria, doctors write down the patients' medical encounters and send them to nurses and other medical departments for processing. Often, the doctor does not have enough time to record every observation made while examining the patient and asking about their medical history which takes time for a medical diagnosis to be made.

The manual review of this vast amount of data generated from multiple sources is costly and very time-consuming. It brings huge challenges while attempting to review this data meaningfully. Therefore, the goal of this research is to create a system that will address the issues. The suggested method extracts information from the voice data of medical encounters and converts it to text using Deep Learning (DL) and Natural Language Processing (NLP) techniques. More so, the system developed will improve medical intelligence processing by using deep learning to analyse medical datasets and produce results of a diagnosis, assisting medical professionals at various levels in making realistic, intelligent decisions in real time regarding crucial health issues.

The system was designed using the Object-Oriented Analysis and Design Methodology (OOADM), and the user interfaces were put into place utilizing Natural Language Processing techniques, particularly speech recognition and natural language comprehension. Speech recognition enables taking text notes, which can drastically cut down on the amount of time medical staff spends on the tensive clinical recording. By extracting different pieces of data for medical diagnosis and producing results in a matter of seconds, a deep learning algorithm demonstrates a significant capacity to construct clinical decision support systems. Deep learning solutions allow healthcare organizations to deliver personalised patients’ medical histories, symptoms, and tests. Natural language processing provides insights from free-text medical information for the most relevant medical treatments. Thus, the type of deep learning used in this dissertation is NLP. The system's results demonstrate that the deep learning algorithm enabled medical intelligence to be 96.7 per cent accurate.

Developing Novel Machine Learning Approaches to Process Medical Information