Effective Identification of Similar Patients Through Sequential Matching over ICD Code Embedding
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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.
KeywordsCode embedding Word2Vec Sequential matching Patient similarity matching Cancer
This work is partially supported by the Telstra-Deakin Centre of Excellence (CoE) in Big Data and Machine Learning. Dinh Phung gratefully acknowledges the partial support from the Australian Research Council (ARC).
Compliance with Ethical Standards
Conflict of Interest
The authors have no conflict of interest to declare.
Ethics approval was obtained from the New South Wales Population and Health Services Research Ethics Committee (AU RED Reference: HREC/15/CIPHS/1).
This study is a secondary analysis of routinely collected data, and the consent had been obtained by the original data guarantor.
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