Temporal Segmentation for Capturing Snapshots of Patient Histories in Korean Clinical Narrative.
10.4258/hir.2018.24.3.179
- Author:
Wangjin LEE
1
;
Jinwook CHOI
Author Information
1. Interdisciplinary Program for Bioengineering, Graduate School, Seoul National University, Seoul, Korea. jinchoi@snu.ac.kr
- Publication Type:Original Article
- Keywords:
Natural Language Processing;
Electronic Health Records;
Automated Pattern Recognition;
Rheumatic Diseases
- MeSH:
Delivery of Health Care;
Electronic Health Records;
Humans;
Methods;
Natural Language Processing;
Pattern Recognition, Automated;
Rheumatic Diseases
- From:Healthcare Informatics Research
2018;24(3):179-186
- CountryRepublic of Korea
- Language:English
-
Abstract:
OBJECTIVES: Clinical discharge summaries provide valuable information about patients' clinical history, which is helpful for the realization of intelligent healthcare applications. The documents tend to take the form of separate segments based on temporal or topical information. If a patient's clinical history can be seen as a consecutive sequence of clinical events, then each temporal segment can be seen as a snapshot, providing a certain clinical context at a specific moment. This study aimed to demonstrate a temporal segmentation method of Korean clinical narratives for identifying textual snapshots of patient history as a proof-of-a-concept. METHODS: Our method uses pattern-based segmentation to approximate human recognition of the temporal or topical shifts in clinical documents. We utilized rheumatic patients' discharge summaries and transformed them into sequences of constituent chunks. We built 97 single pattern functions to denote whether a certain chunk has attributes that indicate that it can be a segment boundary. We manually defined the relationships between the pattern functions to resolve multiple pattern matchings and to make a final decision. RESULTS: The algorithm segmented 30 discharge summaries and processed 1,849 decision points. Three human judges were asked whether they agreed with the algorithm's prediction, and the agreement percentage on the judges' majority opinion was 89.61%. CONCLUSIONS: Although this method is based on manually constructed rules, our findings demonstrate that the proposed algorithm can achieve fairly good segmentation results, and it may be the basis for methodological improvement in the future.
