Invited commentary: deep learning-methods to amplify epidemiologic data collection and analyses.

Area of Science:

• Epidemiology
• Artificial Intelligence
• Machine Learning
• Deep Learning

Background:

• Deep learning, utilizing neural networks and attention algorithms, is increasingly applied across various data types (text, audio, images, video).
• A primer by Serghiou and Rough (Am J Epidemiol. 2023) introduces deep learning models to epidemiologists.
• Epidemiologists traditionally use statistical software, but deep learning presents new analytical avenues.

Purpose of the Study:

• To provide epidemiologists with an understanding of deep learning models.
• To highlight the opportunities deep learning offers for epidemiological research.
• To emphasize the continued relevance of core epidemiologic principles when using deep learning.

Main Methods:

• The abstract discusses deep learning models, a subfield of artificial intelligence and machine learning.
• It references a primer on deep learning for epidemiologists.
• It touches upon the application of these models in data collection and analysis.

Main Results:

• Deep learning models can significantly expand research reach, subject numbers, and the ability to handle large, high-dimensional data.
• Implementation tools for deep learning are less ubiquitous than traditional statistical methods.
• Interdisciplinary collaboration with deep learning experts is encouraged.

Conclusions:

• Deep learning presents substantial opportunities for advancing epidemiological research.
• While novel, the application of deep learning in epidemiology necessitates adherence to fundamental principles like bias assessment and study design.
• Collaboration is key to leveraging these advanced computational tools effectively.