Deep Learning for Epidemiologists: An Introduction to Neural Networks

Area of Science:

• Epidemiology
• Computer Science
• Medical Informatics

Background:

• Deep learning (DL) methods are increasingly used in medicine and healthcare.
• Epidemiologists often lack formal training in DL techniques.
• A knowledge gap exists between DL advancements and epidemiological application.

Approach:

• Introduces fundamental DL concepts from an epidemiological perspective.
• Reviews core machine learning principles like overfitting, regularization, and hyperparameters.
• Explains key DL architectures: convolutional neural networks (CNNs) and recurrent neural networks (RNNs).

Key Points:

• Focuses on conceptual understanding of supervised learning algorithms.
• Summarizes model training, evaluation, and deployment processes.
• Provides an accessible introduction to DL terminology and concepts.

Conclusions:

• Aims to equip epidemiologists to read and assess DL in medical research.
• Facilitates communication between epidemiologists and computer scientists/machine learning engineers.
• Serves as a foundational step for integrating DL into epidemiological practice.