Automated Question Answer medical model based on Deep Learning Technology

TL;DR

This paper presents an AI-based end-to-end deep learning model that automatically generates medical answers to health questions, aiming to create a digital doctor to assist users efficiently.

Contribution

It introduces a novel RNN-based encoder-decoder model trained on online health forum data to automate medical question answering.

Findings

Model trained on multiple online health platforms

Generated answers are relevant and useful

Demonstrates potential for digital medical assistance

Abstract

Artificial intelligence can now provide more solutions for different problems, especially in the medical field. One of those problems the lack of answers to any given medical/health-related question. The Internet is full of forums that allow people to ask some specific questions and get great answers for them. Nevertheless, browsing these questions in order to locate one similar to your own, also finding a satisfactory answer is a difficult and time-consuming task. This research will introduce a solution to this problem by automating the process of generating qualified answers to these questions and creating a kind of digital doctor. Furthermore, this research will train an end-to-end model using the framework of RNN and the encoder-decoder to generate sensible and useful answers to a small set of medical/health issues. The proposed model was trained and evaluated using data from various online services, such as WebMD, HealthTap, eHealthForums, and iCliniq.