Using the distance between sets of hierarchical taxonomic clinical concepts to measure patient similarity

BMC Medical Informatics and Decision Making. 2019;19(1):1-11 DOI 10.1186/s12911-019-0807-y

 

Journal Homepage

Journal Title: BMC Medical Informatics and Decision Making

ISSN: 1472-6947 (Online)

Publisher: BMC

LCC Subject Category: Medicine: Medicine (General): Computer applications to medicine. Medical informatics

Country of publisher: United Kingdom

Language of fulltext: English

Full-text formats available: PDF, HTML

 

AUTHORS

Zheng Jia (College of Biomedical Engineering and Instrument Science, Zhejiang University)
Xudong Lu (College of Biomedical Engineering and Instrument Science, Zhejiang University)
Huilong Duan (College of Biomedical Engineering and Instrument Science, Zhejiang University)
Haomin Li (The Children’s Hospital, Zhejiang University School of Medicine)

EDITORIAL INFORMATION

Open peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 23 weeks

 

Abstract | Full Text

Abstract Background Many clinical concepts are standardized under a categorical and hierarchical taxonomy such as ICD-10, ATC, etc. These taxonomic clinical concepts provide insight into semantic meaning and similarity among clinical concepts and have been applied to patient similarity measures. However, the effects of diverse set sizes of taxonomic clinical concepts contributing to similarity at the patient level have not been well studied. Methods In this paper the most widely used taxonomic clinical concepts system, ICD-10, was studied as a representative taxonomy. The distance between ICD-10-coded diagnosis sets is an integrated estimation of the information content of each concept, the similarity between each pairwise concepts and the similarity between the sets of concepts. We proposed a novel method at the set-level similarity to calculate the distance between sets of hierarchical taxonomic clinical concepts to measure patient similarity. A real-world clinical dataset with ICD-10 coded diagnoses and hospital length of stay (HLOS) information was used to evaluate the performance of various algorithms and their combinations in predicting whether a patient need long-term hospitalization or not. Four subpopulation prototypes that were defined based on age and HLOS with different diagnoses set sizes were used as the target for similarity analysis. The F-score was used to evaluate the performance of different algorithms by controlling other factors. We also evaluated the effect of prototype set size on prediction precision. Results The results identified the strengths and weaknesses of different algorithms to compute information content, code-level similarity and set-level similarity under different contexts, such as set size and concept set background. The minimum weighted bipartite matching approach, which has not been fully recognized previously showed unique advantages in measuring the concepts-based patient similarity. Conclusions This study provides a systematic benchmark evaluation of previous algorithms and novel algorithms used in taxonomic concepts-based patient similarity, and it provides the basis for selecting appropriate methods under different clinical scenarios.