• DOI:10.1142/9789811215636_0045
Corpus ID: 202888555

Enhancing Model Interpretability and Accuracy for Disease Progression Prediction via Phenotype-Based Patient Similarity Learning

@article{Wang2020EnhancingMI,
  title={Enhancing Model Interpretability and Accuracy for Disease Progression Prediction via Phenotype-Based Patient Similarity Learning},
  author={Yue Wang and Tong Wu and Yunlong Wang and Gao Wang},
  journal={Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing},
  year={2020},
  volume={25},
  pages={
          511-522
        }
}

Yue Wang, Tong Wu, +1 author Gao Wang

Published 2020

Computer Science, Medicine, Mathematics

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

Models have been proposed to extract temporal patterns from longitudinal electronic health records (EHR) for clinical predictive models. However, the common relations among patients (e.g., receiving the same medical treatments) were rarely considered. In this paper, we propose to learn patient similarity features as phenotypes from the aggregated patient-medical service matrix using non-negative matrix factorization. On real-world medical claim data, we show that the learned phenotypes are… CONTINUE READING

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