Please use this identifier to cite or link to this item: https://doi.org/10.1186/s12911-021-01566-y
Title: Patient similarity analytics for explainable clinical risk prediction
Authors: Fang, Hao Sen Andrew
Tan, Ngiap Chuan
Tan, Wei Ying 
Oei, Ronald Wihal
Lee, Mong Li 
Hsu, Wynne 
Keywords: Clinical decision support tool
Explainable artificial intelligence
Interpretable
Patient similarity
Prediction models
Issue Date: 1-Jul-2021
Publisher: BioMed Central Ltd
Citation: Fang, Hao Sen Andrew, Tan, Ngiap Chuan, Tan, Wei Ying, Oei, Ronald Wihal, Lee, Mong Li, Hsu, Wynne (2021-07-01). Patient similarity analytics for explainable clinical risk prediction. BMC Medical Informatics and Decision Making 21 (1) : 207. ScholarBank@NUS Repository. https://doi.org/10.1186/s12911-021-01566-y
Rights: Attribution 4.0 International
Abstract: Background: Clinical risk prediction models (CRPMs) use patient characteristics to estimate the probability of having or developing a particular disease and/or outcome. While CRPMs are gaining in popularity, they have yet to be widely adopted in clinical practice. The lack of explainability and interpretability has limited their utility. Explainability is the extent of which a model’s prediction process can be described. Interpretability is the degree to which a user can understand the predictions made by a model. Methods: The study aimed to demonstrate utility of patient similarity analytics in developing an explainable and interpretable CRPM. Data was extracted from the electronic medical records of patients with type-2 diabetes mellitus, hypertension and dyslipidaemia in a Singapore public primary care clinic. We used modified K-nearest neighbour which incorporated expert input, to develop a patient similarity model on this real-world training dataset (n = 7,041) and validated it on a testing dataset (n = 3,018). The results were compared using logistic regression, random forest (RF) and support vector machine (SVM) models from the same dataset. The patient similarity model was then implemented in a prototype system to demonstrate the identification, explainability and interpretability of similar patients and the prediction process. Results: The patient similarity model (AUROC = 0.718) was comparable to the logistic regression (AUROC = 0.695), RF (AUROC = 0.764) and SVM models (AUROC = 0.766). We packaged the patient similarity model in a prototype web application. A proof of concept demonstrated how the application provided both quantitative and qualitative information, in the form of patient narratives. This information was used to better inform and influence clinical decision-making, such as getting a patient to agree to start insulin therapy. Conclusions: Patient similarity analytics is a feasible approach to develop an explainable and interpretable CRPM. While the approach is generalizable, it can be used to develop locally relevant information, based on the database it searches. Ultimately, such an approach can generate a more informative CRPMs which can be deployed as part of clinical decision support tools to better facilitate shared decision-making in clinical practice. © 2021, The Author(s).
Source Title: BMC Medical Informatics and Decision Making
URI: https://scholarbank.nus.edu.sg/handle/10635/232316
ISSN: 1472-6947
DOI: 10.1186/s12911-021-01566-y
Rights: Attribution 4.0 International
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