The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
What is Machine Learning? A Primer for the Epidemiologist
Abstract
Machine learning is a branch of computer science that has the potential to transform epidemiologic sciences. Amid a growing focus on “Big Data,” it offers epidemiologists new tools to tackle problems for which classical methods are not well-suited. In order to critically evaluate the value of integrating machine learning algorithms and existing methods, however, it is essential to address language and technical barriers between the two fields that can make it difficult for epidemiologists to read and assess machine learning studies. Here, we provide an overview of the concepts and terminology used in machine learning literature, which encompasses a diverse set of tools with goals ranging from prediction to classification to clustering. We provide a brief introduction to 5 common machine learning algorithms and 4 ensemble-based approaches. We then summarize epidemiologic applications of machine learning techniques in the published literature. We recommend approaches to incorporate machine learning in epidemiologic research and discuss opportunities and challenges for integrating machine learning and existing epidemiologic research methods.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Gaussian Processes for Machine Learning
Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705
The global distribution and burden of dengue
Bhatt, S., Gething, P. W., Brady, O. J., Messina, J. P., Farlow, A. W., Moyes, C. L., Drake, J. M., Brownstein, J. S., Hoen, A. G., Sankoh, O., et al. (2013) · Nature · doi:10.1038/nature12060 · Cited by 10,037
Cited by
Andaur Navarro, C. L., Damen, J. A. A., Takada, T., Nijman, S. W. J., Dhiman, P., Ma, J., Collins, G. S., Bajpai, R., Riley, R. D., Moons, K. G. M., et al. (2021) · BMJ · doi:10.1136/bmj.n2281 · Cited by 415
A Scoping Review of Machine Learning in Psychotherapy Research
Aafjes-van Doorn, K., Kamsteeg, C., Bate, J., & Aafjes, M. (2021) · Psychotherapy Research · doi:10.1080/10503307.2020.1808729 · Cited by 179
Radiomics Improves Cancer Screening and Early Detection
Gillies, R. J., & Schabath, M. B. (2020) · Cancer Epidemiology, Biomarkers & Prevention · doi:10.1158/1055-9965.epi-20-0075 · Cited by 159
Syrowatka, A., Kuznetsova, M., Alsubai, A., Beckman, A. L., Bain, P. A., Craig, K. J. T., Hu, J., Jackson, G. P., Rhee, K., & Bates, D. W. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00459-8 · Cited by 130
Cierco Jimenez, R., Lee, T., Rosillo, N., Cordova, R., Cree, I. A., Gonzalez, A., & Indave Ruiz, B. I. (2022) · BMC Medical Research Methodology · doi:10.1186/s12874-022-01805-4 · Cited by 85
Metadata
- Authors
- Bi, Q., Goodman, K. E., Kaminsky, J., & Lessler, J.
- Venue
- American Journal of Epidemiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-10-21
- Volume
- 188
- Issue
- 12
- Pages
- 2222-2239
- ISSN
- 0002-9262 (ISSN-L)
- 1476-6256
- OpenAlex
- W2973032093
- Cited by
- 496
- References
- 27
- FWCI
- 38.94
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: What is Machine Learning? A Primer for the Epidemiologist. https://app.cochat.ai/papers/10.1093/aje/kwz189
- The paper
- Bi, Q., Goodman, K. E., Kaminsky, J., & Lessler, J. (2019). What is Machine Learning? A Primer for the Epidemiologist. American Journal of Epidemiology. https://doi.org/10.1093/aje/kwz189
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
