Developing and Analyzing Deep Learning and Natural Language Processing Systems in the Context of Medical Information Processing

Victoria Nkemjika Emmanuel, Enyinnaya Chibunna Victor, Ezeoha Bright Uzoma and Ogbonna Tochukwu Loveday
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Victoria Nkemjika Emmanuel: Imo State University
Enyinnaya Chibunna Victor: Abia State College of Health Sciences and Management Technology
Ezeoha Bright Uzoma: Abia State Polytechnic
Ogbonna Tochukwu Loveday: Imo State University

International Journal of Research and Innovation in Applied Science, 2024, vol. 9, issue 8, 733-739

Abstract: This work aims to develop and analyze deep learning and natural language processing systems in the context of medical information processing. 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, doctors write down the patient’s medical encounter and send it to nurses and other medical departments for processing. Often, the doctor doesn’t 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 aforementioned issues. The suggested method extracts voice data from 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 analyze 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 allows for the taking of free text notes, which can drastically cut down on the amount of time medical staff spends on labor-in 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. The system’s results demonstrate that the deep learning algorithm enabled medical intelligence to be 96.7 percent accurate.

Date: 2024
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