@inproceedings{a852ec5a0a3049a5bb10e9144cc231e8,
title = "Medical Diagnosis by Complaints of Patients and Machine Learning",
abstract = "Self-diagnose becomes an important research topic and hot web application. It relies on patients' own description about their conditions. Finding relationship between patients' complain and the possible diseases is the key. This paper reports our efforts on applying machine learning models to solve this problem. We firstly collected and build a dataset including 10,000 chief complaints from authoritative medical websites including haodf.com, and yyk.99.com and top Chinese hospitals. We then trained Support Vector Machine (SVM) and Bidirectional Long and Short-term Memory (BiLSTM) models using our collected dataset to verify our dataset and to test prediction models. The test shows the models trained with sample datasets have a stable performance with 75\% in accuracy, 81\% in precision and recall being 81\%.",
keywords = "BiLSTM, SVM, chief complaint corpus, disease diagnosis",
author = "Gangmin Li and Haowei Song and Liang, \{Hai Ning\} and Yuanying Qu and Lu Liu and Xuming Bai",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019 ; Conference date: 19-10-2019 Through 21-10-2019",
year = "2019",
month = oct,
doi = "10.1109/CISP-BMEI48845.2019.8965949",
language = "English",
series = "Proceedings - 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Qingli Li and Lipo Wang",
booktitle = "Proceedings - 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019",
}