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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
  • 1Université de Paris, Epidemiology and Statistics Research Center (CRESS), Institut National de la Santé et de la Recherche Médicale (INSERM), Institut National de la Recherche Agronomique (INRA), F-75004, Paris, France
  • 2Centre d’Épidémiologie Clinique, Assistance Publique-Hôpitaux de Paris (AP-HP), Hôpital Hôtel-Dieu, F-75004, Paris, France
JAMA Dermatol. 2020;156(4):472-473. doi:10.1001/jamadermatol.2019.4919

To the Editor We read with great interest the article by Wang et al.1 In their study, the authors applied deep learning techniques to predict 1-year risk of nonmelanoma skin cancer from clinical diagnostic information and medical records, including medication received. Their model showed an area under the receiver operating characteristic curve of 0.89 (95% CI, 0.87-0.91), which is impressive accuracy. However, the study may have important flaws in its design that are likely to have biased upward the estimation of the model’s accuracy.

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