Medical Diagnosis by Complaints of Patients and Machine Learning
Proceedings - 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019, Page: 1-5
2019
- 2Citations
- 5Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
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%.
Bibliographic Details
Institute of Electrical and Electronics Engineers (IEEE)
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