ArticleBiomedical engineering online2019
Measurement and application of patient similarity in personalized predictive modeling based on electronic medical records.
Article in Biomedical engineering online, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Study on the semi-supervised learning-based patient similarity from heterogeneous electronic medical records.BMC medical informatics and decision making · 2021Trial
- Bayesian Counterfactual Machine Learning Individualizes Radiation Modality Selection to Mitigate Immunosuppression.JCO clinical cancer informatics · 2025Article
- A patient similarity-embedded Bayesian approach to prognostic biomarker inference with application to thoracic cancer immunity.Journal of the Royal Statistical Society. Series C, Applied statistics · 2025Article
- A Comparative Analysis of Patient Similarity Measures for Outcome Prediction.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Constructing a Clinical Patient Similarity Network of Gastric Cancer.Bioengineering (Basel, Switzerland) · 2024Article
- Article
- A Novel Patient Similarity Network (PSN) Framework Based on Multi-Model Deep Learning for Precision Medicine.Journal of personalized medicine · 2022Article
- Sequential Data-Based Patient Similarity Framework for Patient Outcome Prediction: Algorithm Development.Journal of medical Internet research · 2022Article
- Patient similarity analytics for explainable clinical risk prediction.BMC medical informatics and decision making · 2021Article
- A Neuro-ontology for the neurological examination.BMC medical informatics and decision making · 2020Article
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Authors and funding
7 authors.
Funding
Abstract
backgroundConventional risk prediction techniques may not be the most suitable approach for personalized prediction for individual patients. Therefore, individualized predictive modeling based on similar patients has emerged. This study aimed to propose a comprehensive measurement of patient similarity using real-world electronic medical records data, and evaluate the effectiveness of the individualized prediction of a patient's diabetes status based on the patient similarity.
resultsWhen using no more than 30% of the whole training sample, the personalized predictive models outperformed corresponding traditional models built on randomly selected training samples of the same size as the personalized models (P < 0.001 for all). With only the top 1000 (10%), 700 (7%) and 1400 (14%) similar samples, personalized random forest, k-nearest neighbor and logistic regression models reached the globally optimal performance with the area under the receiver-operating characteristic (ROC) curve of 0.90, 0.82 and 0.89, respectively.
conclusionsThe proposed patient similarity measurement was effective when developing personalized predictive models. The successful application of patient similarity in predicting a patient's diabetes status provided useful references for diagnostic decision-making support by investigating the evidence on similar patients.
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