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Comment & Response
February 12, 2020

Application of Basic Epidemiologic Principles and Electronic Health Records in a Deep Learning Prediction Model

Author Affiliations
  • 1Department of Dermatology, Seoul National University College of Medicine, Seoul, South Korea
  • 2Interdisciplinary Program for Biomedical Engineering, Seoul National University, Seoul, South Korea
JAMA Dermatol. Published online February 12, 2020. doi:10.1001/jamadermatol.2019.4922

To the Editor We found the study by Wang et al1 to be an interesting read. It raises important issues regarding the data-driven approach in the field of dermatology, and some obstacles should be discussed before applying it clinically.

Technically, the imbalances between study groups can cause overfitting problems. The patients in the study who had nonmelanoma skin cancer were much older than the control patients, which led to many more annual diagnostic code counts and medications per person. The patterns of diagnosis and medications in the 2 groups could definitely be tied to age difference. The k-fold cross-validation method can reduce but not completely remove overfitting, especially for small data sets.2 Performance results for each of the test and validation sets were not shown separately. Therefore, external validation should have been conducted to verify whether the model of Wang et al1 predicted skin cancer only in elderly patients or in patients in the general population.

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