@inproceedings{edd2b4501ad342a1af85400d30c0d37d,
title = "A problem-action relation extraction based on causality patterns of clinical events in discharge summaries",
abstract = "Medical knowledge extraction has great potential to improve the treatment quality of hospitals. In this paper, we propose a clinical problem-action relation extraction method. It is based on clinical semantic units and event causality patterns in order to present a chronological view of a patient's problem and a physician's action. Based on our observation, a clinical semantic unit is defined as a conceptual medical knowledge for a problem and/or action. Since a clinical event is a basic concept of the problem-action relation, events are detected from clinical texts based on conditional random fields. A clinical semantic unit is segmented from a sentence based on time expressions and inherent structure of events. Then, a clinical semantic unit is classified into a problem and/or action relation based on event causality features in support vector machines. The experimental result on Korean medical collection shows 78.8\% in F-measure when given the answer of clinical events. This result shows that the proposed method is effective for extracting clinical problem-action relations.",
keywords = "Causal relationship, Clinical semantic unit, Problem-action relation, Relation extraction",
author = "Seol, \{Jae Wook\} and Jo, \{Seung Hyeon\} and Wangjin Yi and Jinwook Choi and Lee, \{Kyung Soon\}",
year = "2014",
month = nov,
day = "3",
doi = "10.1145/2661829.2662080",
language = "English",
series = "CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management",
publisher = "Association for Computing Machinery",
pages = "1971--1974",
booktitle = "CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management",
note = "23rd ACM International Conference on Information and Knowledge Management, CIKM 2014 ; Conference date: 03-11-2014 Through 07-11-2014",
}