The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
Machine learning for precision medicine
Open accessAbstract
Precision medicine is an emerging approach to clinical research and patient care that focuses on understanding and treating disease by integrating multi-modal or multi-omics data from an individual to make patient-tailored decisions. With the large and complex datasets generated using precision medicine diagnostic approaches, novel techniques to process and understand these complex data were needed. At the same time, computer science has progressed rapidly to develop techniques that enable the storage, processing, and analysis of these complex datasets, a feat that traditional statistics and early computing technologies could not accomplish. Machine learning, a branch of artificial intelligence, is a computer science methodology that aims to identify complex patterns in data that can be used to make predictions or classifications on new unseen data or for advanced exploratory data analysis. Machine learning analysis of precision medicine’s multi-modal data allows for broad analysis of large datasets and ultimately a greater understanding of human health and disease. This review focuses on machine learning utilization for precision medicine’s “big data”, in the context of genetics, genomics, and beyond.
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Cites
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Background
Cited by
Developing an Artificial Intelligence Tool to Predict Vocal Cord Pathology in Primary Care Settings
Compton, E. C., Cruz, T., Andreassen, M., Beveridge, S., Bosch, D., Randall, D. R., & Livingstone, D. (2023) · The Laryngoscope · doi:10.1002/lary.30432 · Cited by 38
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Towards Trustworthy AI in Healthcare: Epistemic Uncertainty Estimation for Clinical Decision Support
Lindenmeyer, A., Blattmann, M., Franke, S., Neumuth, T., & Schneider, D. (2025) · Journal of Personalized Medicine · doi:10.3390/jpm15020058 · Cited by 17
“This limitation necessitates the development of automated methods capable of identify complex patterns, summarizing vast data [3–5], and indicating critical points that require human attention [6–8].”
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Metadata
- Authors
- MacEachern, S. J., & Forkert, N. D.
- Venue
- Genome
- Publisher
- Canadian Science Publishing
- Published
- 2021-04
- Volume
- 64
- Issue
- 4
- Pages
- 416-425
- ISSN
- 0831-2796 (ISSN-L)
- 1480-3321
- OpenAlex
- W3094108931
- Cited by
- 455
- References
- 47
- FWCI
- 10.78
- Type
- journal article
- Language
- en
- Open access
- green
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- MacEachern, S. J., & Forkert, N. D. (2021). Machine learning for precision medicine. Genome. https://doi.org/10.1139/gen-2020-0131
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