Toward generating domain-specific / Personalized problem lists from electronic medical records

Ching Huei Tsou, Murthy Devarakonda, Jennifer J. Liang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


An accurate problem list plays the key role of a problem-oriented medical record, which plays a significant role in improving patient care. However, die multi-author, multi-pur- pose nature of problem list makes it a challenge to maintain, and a single list is difficult, if not impossible, to satisfy all the needs of different practitioners. In this paper, we propose using machine generated problem list to assist a medical practitioner to review a patient's chart. The proposed system scans both structured and unstructured data in a patient's electronic medical record (EMR) and generates a ranked, recall-oriented problem list grouped by body systems. Details of each problem are readily available for the user to assess the correctness and relevance of the problem. The user can then provide feedback to the system on the trustworthiness of each evidence passage retrieved, as well as the validity of the problem as a whole. The user-specific feedback provides new information the system needs to perform active learning to learn the user's preference and produce personalized, and/or domain-specific problem lists.

Original languageEnglish (US)
Title of host publicationCognitive Assistance in Government and Public Sector Applications - Papers from the AAAI 2015 Fall Symposium, Technical Report
PublisherAI Access Foundation
Number of pages4
ISBN (Electronic)9781577357483
StatePublished - Jan 1 2015
Externally publishedYes
EventAAAI 2015 Fall Symposium - Arlington, United States
Duration: Nov 12 2015Nov 14 2015


OtherAAAI 2015 Fall Symposium
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Engineering(all)


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