Deep Learning for Epidemiologists: An Introduction to Neural Networks

Abstract Deep learning methods are increasingly being applied to problems in medicine and health care. However, few epidemiologists have received formal training in these methods. To bridge this gap, this article introduces the fundamentals of deep learning from an epidemiologic perspective. Specifi...

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Bibliographic Details
Published in:American journal of epidemiology 2023-11, Vol.192 (11), p.1904-1916
Main Authors: Serghiou, Stylianos, Rough, Kathryn
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Deep learning
Epidemiology
Learning algorithms
Machine learning
Neural networks
Recurrent neural networks
Regularization
Supervised learning
Terminology
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Summary:Abstract Deep learning methods are increasingly being applied to problems in medicine and health care. However, few epidemiologists have received formal training in these methods. To bridge this gap, this article introduces the fundamentals of deep learning from an epidemiologic perspective. Specifically, this article reviews core concepts in machine learning (e.g., overfitting, regularization, and hyperparameters); explains several fundamental deep learning architectures (convolutional neural networks, recurrent neural networks); and summarizes training, evaluation, and deployment of models. Conceptual understanding of supervised learning algorithms is the focus of the article; instructions on the training of deep learning models and applications of deep learning to causal learning are out of this article’s scope. We aim to provide an accessible first step towards enabling the reader to read and assess research on the medical applications of deep learning and to familiarize readers with deep learning terminology and concepts to facilitate communication with computer scientists and machine learning engineers.
ISSN:0002-9262
1476-6256
DOI:10.1093/aje/kwad107