Extracting Medical Knowledge from Crowdsourced Question Answering Website

The medical crowdsourced question answering (QA) websites are booming in recent years, and an increasingly large amount of patients and doctors are involved. The valuable information from these medical crowdsourced QA websites can benefit patients, doctors and the society. One key to unleash the power of these QA websites is to extract medical knowledge from the noisy question-answer pairs and filter out unrelated or even incorrect information. Facing the daunting scale of information generated on medical QA websites everyday, it is unrealistic to fulfill this task via supervised method due to the expensive annotation cost. In this paper, we propose a Medical Knowledge Extraction (MKE) system that can automatically provide high-quality knowledge triples extracted from the noisy question-answer pairs, and at the same time, estimate expertise for the doctors who give answers on these QA websites. The MKE system is built upon a truth discovery framework, where we jointly estimate trustworthiness of answers and doctor expertise from the data without any supervision. We further tackle three unique challenges in the medical knowledge extraction task, namely representation of noisy input, multiple linked truths, and the long-tail phenomenon in the data. The MKE system is applied to real-world datasets crawled from xywy.com, one of the most popular medical crowdsourced QA websites. Both quantitative evaluation and case studies demonstrate that the proposed MKE system can successfully provide useful medical knowledge and accurate doctor expertise. We further demonstrate a real-world application, Ask A Doctor, which can automatically give patients suggestions to their questions.