[Skip to Content]
Access to paid content on this site is currently suspended due to excessive activity being detected from your IP address 34.204.191.0. Please contact the publisher to request reinstatement.
[Skip to Content Landing]
Views 116
Citations 0
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. Published online February 12, 2020. 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.

Limit 200 characters
Limit 25 characters
Conflicts of Interest Disclosure

Identify all potential conflicts of interest that might be relevant to your comment.

Conflicts of interest comprise financial interests, activities, and relationships within the past 3 years including but not limited to employment, affiliation, grants or funding, consultancies, honoraria or payment, speaker's bureaus, stock ownership or options, expert testimony, royalties, donation of medical equipment, or patents planned, pending, or issued.

Err on the side of full disclosure.

If you have no conflicts of interest, check "No potential conflicts of interest" in the box below. The information will be posted with your response.

Not all submitted comments are published. Please see our commenting policy for details.

Limit 140 characters
Limit 3600 characters or approximately 600 words
    ×