Deep Learning for Epidemiologists: An Introduction to Neural Networks

TL;DR

This paper introduces epidemiologists to deep learning fundamentals, architectures, and evaluation methods, aiming to bridge the knowledge gap and foster interdisciplinary collaboration in medical applications.

Contribution

It provides an accessible overview of deep learning concepts and architectures tailored for epidemiologists, enhancing their ability to critically assess medical deep learning applications.

Findings

Clarifies core deep learning concepts for epidemiologists

Explains fundamental neural network architectures

Summarizes training and deployment practices

Abstract

Deep learning methods are increasingly being applied to problems in medicine and healthcare. However, few epidemiologists have received formal training in these methods. To bridge this gap, this article introduces to the fundamentals of deep learning from an epidemiological perspective. Specifically, this article reviews core concepts in machine learning (overfitting, regularization, hyperparameters), explains several fundamental deep learning architectures (convolutional neural networks, recurrent neural networks), and summarizes training, evaluation, and deployment of models. We aim to enable the reader to engage with and critically evaluate medical applications of deep learning, facilitating a dialogue between computer scientists and epidemiologists that will improve the safety and efficacy of applications of this technology.