{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T18:39:05Z","timestamp":1770835145449,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>\n      \n        Online symptom checkers have been deployed by sites such as WebMD and Mayo Clinic to identify possible causes and treatments for diseases based on a patient\u2019s symptoms. Symptom checking first assesses a patient by asking a series of questions about their symptoms, then attempts to predict potential diseases. The two design goals of a symptom checker are to achieve high accuracy and intuitive interactions. In this paper we present our context-aware hierarchical reinforcement learning scheme, which significantly improves accuracy of symptom checking over traditional systems while also making a limited number of inquiries.\n      \n    <\/jats:p>","DOI":"10.1609\/aaai.v32i1.11902","type":"journal-article","created":{"date-parts":[[2022,6,24]],"date-time":"2022-06-24T21:33:48Z","timestamp":1656106428000},"source":"Crossref","is-referenced-by-count":55,"title":["Context-Aware Symptom Checking for Disease Diagnosis Using Hierarchical Reinforcement Learning"],"prefix":"10.1609","volume":"32","author":[{"given":"Hao-Cheng","family":"Kao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai-Fu","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Edward","family":"Chang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"9382","published-online":{"date-parts":[[2018,4,26]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/11902\/11761","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/11902\/11761","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T18:47:08Z","timestamp":1667846828000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/11902"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,4,26]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2018,2,8]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v32i1.11902","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2018,4,26]]}}}