Commentary: Towards machine learning-enabled epidemiology.

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

Who cites it

8 citing papers in PubMed.

1. Reevaluating the role of geographical regional factors in subjective well-being.Scientific reports · 2026
   Article
2. Is artificial intelligence a friend or foe to epidemiology?Annals of epidemiology · 2026
   Review
3. Invited commentary: deep learning-methods to amplify epidemiologic data collection and analyses.American journal of epidemiology · 2025
   Article
4. Leveraging Random Forests explainability for predictive modeling of children's conduct problems: insights from individual and family factors.Frontiers in public health · 2025
   Article
5. UK research data resources based on primary care electronic health records: review and summary for potential users.BJGP open · 2023
   Article
6. Co-evolution of epidemiology and artificial intelligence: challenges and opportunities.International journal of epidemiology · 2023
   Article
7. Machine Learning in Nutrition Research.Advances in nutrition (Bethesda, Md.) · 2022
   Article
8. Using random forest to identify longitudinal predictors of health in a 30-year cohort study.Scientific reports · 2022
   Article

Corrections and comments

• Comment on
  Reflection on modern methods: when worlds collide-prediction, machine learning and causal inference.2021
• Comment on
  Ensemble modelling in descriptive epidemiology: burden of disease estimation.2021
• Comment on
  Intersections of machine learning and epidemiological methods for health services research.2021

Authors and funding

1 author.

Funding

No grant is acknowledged in the PubMed record.

Abstract

PubMed holds no abstract for this paper.

Indexed as