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Article

Using Domain Knowledge and Data-Driven Insights for Patient Similarity Analytics

1
Institute of Data Science, National University of Singapore, Singapore 117602, Singapore
2
SingHealth Polyclinics, SingHealth, Singapore 150167, Singapore
3
School of Computing, National University of Singapore, Singapore 117417, Singapore
*
Author to whom correspondence should be addressed.
Academic Editor: Enrico Capobianco
J. Pers. Med. 2021, 11(8), 699; https://doi.org/10.3390/jpm11080699
Received: 15 June 2021 / Revised: 15 July 2021 / Accepted: 21 July 2021 / Published: 22 July 2021
(This article belongs to the Section Omics/Informatics)
Patient similarity analytics has emerged as an essential tool to identify cohorts of patients who have similar clinical characteristics to some specific patient of interest. In this study, we propose a patient similarity measure called D3K that incorporates domain knowledge and data-driven insights. Using the electronic health records (EHRs) of 169,434 patients with either diabetes, hypertension or dyslipidaemia (DHL), we construct patient feature vectors containing demographics, vital signs, laboratory test results, and prescribed medications. We discretize the variables of interest into various bins based on domain knowledge and make the patient similarity computation to be aligned with clinical guidelines. Key findings from this study are: (1) D3K outperforms baseline approaches in all seven sub-cohorts; (2) our domain knowledge-based binning strategy outperformed the traditional percentile-based binning in all seven sub-cohorts; (3) there is substantial agreement between D3K and physicians (κ = 0.746), indicating that D3K can be applied to facilitate shared decision making. This is the first study to use patient similarity analytics on a cardiometabolic syndrome-related dataset sourced from medical institutions in Singapore. We consider patient similarity among patient cohorts with the same medical conditions to develop localized models for personalized decision support to improve the outcomes of a target patient. View Full-Text
Keywords: patient similarity; distance metric learning; diabetes; hypertension; dyslipidaemia patient similarity; distance metric learning; diabetes; hypertension; dyslipidaemia
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MDPI and ACS Style

Oei, R.W.; Fang, H.S.A.; Tan, W.-Y.; Hsu, W.; Lee, M.-L.; Tan, N.-C. Using Domain Knowledge and Data-Driven Insights for Patient Similarity Analytics. J. Pers. Med. 2021, 11, 699. https://doi.org/10.3390/jpm11080699

AMA Style

Oei RW, Fang HSA, Tan W-Y, Hsu W, Lee M-L, Tan N-C. Using Domain Knowledge and Data-Driven Insights for Patient Similarity Analytics. Journal of Personalized Medicine. 2021; 11(8):699. https://doi.org/10.3390/jpm11080699

Chicago/Turabian Style

Oei, Ronald W., Hao S.A. Fang, Wei-Ying Tan, Wynne Hsu, Mong-Li Lee, and Ngiap-Chuan Tan. 2021. "Using Domain Knowledge and Data-Driven Insights for Patient Similarity Analytics" Journal of Personalized Medicine 11, no. 8: 699. https://doi.org/10.3390/jpm11080699

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