Browsing by Author "Al Rabeyah, Abdullah Saleh"

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Medical Screening Assistant: A Chatbot to Help Nurses
(Saudi Digital Library, 2023-11-08) Al Rabeyah, Abdullah Saleh; Da Silva, Rogerio E; Goes, Fabricio
Over the last several years, Machine Learning has emerged as a key player in the healthcare industry. The use of chatbots is a notable application of artificial intelligence within the field of healthcare. The advent of the ChatGPT revolution represents a significant breakthrough in the realm of natural language processing, a fundamental aspect of chatbot programming. This development has simplified the implementation of GPT to engage in user communication and fulfill the objectives of the application. The objective of this project is to reduce the excessive workloads faced by healthcare professionals and enhance the efficiency of decision-making processes. This will be achieved via the development of an intelligent medical chatbot as a mobile application, specifically designed to support nurses in conducting early patient diagnoses by analyzing symptoms. The chatbot uses Swift programming language for the iOS front-end and Python with Flask for the backend. It incorporates the ChatGPT API and machine learning models to effectively comprehend and interpret user inquiries. This project uses a Kaggle dataset of 41 distinct diseases along with their corresponding symptoms. The model is trained using Logistic Regression to predict the prognosis. The responsibility of managing the dialogue between the user and the chatbot, leading up to the compilation of the definitive list of symptoms shown by the patient, lies with ChatGPT. The use of a Flask RESTful API facilitates direct interaction between the iOS application and the server-side infrastructure. Finally, the application will provide the nurse with the five most probable prognoses, along with the prediction confidence scores, depending on the symptoms supplied. Additionally, the application will offer a description of the disease and provide precautionary measures for the patient.
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