Abdelrahman Abdallah, Mahmoud Kasem, Mohamed A Hamada, and Shaymaa Sdeek
Reference code for the paper Automated Question-Answer Medical Model based on Deep Learning Technology. Abdelrahman Abdallah, Mahmoud Kasem, Mohamed A Hamada, and Shaymaa Sdeek, ICEMIS'20: Proceedings of the 6th International Conference on Engineering & MIS September 2020. If you use this code or our dataset, please cite our paper:
@inproceedings{10.1145/3410352.3410744,
author = {Abdallah, Abdelrahman and Kasem, Mahmoud and Hamada, Mohamed A. and Sdeek, Shaymaa},
title = {Automated Question-Answer Medical Model Based on Deep Learning Technology},
year = {2020},
isbn = {9781450377362},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3410352.3410744},
doi = {10.1145/3410352.3410744},
abstract = {Artificial intelligence can now provide more solutions for different problems, especially in the medical field. One of those problems is 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 to locate a similar case to your own question, also finding a satisfying accurate answer is difficult and timeconsuming task. This research will introduce a solution to these problems 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.},
booktitle = {Proceedings of the 6th International Conference on Engineering & MIS 2020},
articleno = {13},
numpages = {8},
keywords = {medical question answering, word vectors, neural networks, co-attention, memory nets, natural language processing, deep learning},
location = {Almaty, Kazakhstan},
series = {ICEMIS'20}
}
Artificial intelligence can now provide more solutions for different problems, especially in the medical field. One of those problems is 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 to locate a similar case to your own question, also finding a satisfying accurate answer is difficult and timeconsuming task. This research will introduce a solution to these problems 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.