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Effective identification of similar patients through sequential matching over ICD code embedding

journal contribution
posted on 2018-05-01, 00:00 authored by Dang Pham Hai Nguyen, Wei LuoWei Luo, Svetha VenkateshSvetha Venkatesh, Quoc-Dinh Phung
Evidence-based medicine often involves the identification of patients with similar conditions, which are often captured in ICD (International Classification of Diseases (World Health Organization 2013)) code sequences. With no satisfying prior solutions for matching ICD-10 code sequences, this paper presents a method which effectively captures the clinical similarity among routine patients who have multiple comorbidities and complex care needs. Our method leverages the recent progress in representation learning of individual ICD-10 codes, and it explicitly uses the sequential order of codes for matching. Empirical evaluation on a state-wide cancer data collection shows that our proposed method achieves significantly higher matching performance compared with state-of-the-art methods ignoring the sequential order. Our method better identifies similar patients in a number of clinical outcomes including readmission and mortality outlook. Although this paper focuses on ICD-10 diagnosis code sequences, our method can be adapted to work with other codified sequence data.

History

Journal

Journal of medical systems

Volume

42

Issue

94

Pagination

1 - 13

Publisher

Springer

Location

New York, N.Y.

ISSN

0148-5598

eISSN

1573-689X

Language

eng

Publication classification

C Journal article; C1 Refereed article in a scholarly journal

Copyright notice

2018, Springer Science+Business Media, LLC, part of Springer Nature