A machine learning approach to leveraging electronic health records for enhanced omics analysis
Stanford Medicine · Stanford University · +1 more institution
Abstract
Omics studies produce a large number of measurements, enabling the development, validation and interpretation of systems-level biological models. Large cohorts are required to power these complex models; yet, the cohort size remains limited due to clinical and budgetary constraints. We introduce clinical and omics multimodal analysis enhanced with transfer learning (COMET), a machine learning framework that incorporates large, observational electronic health record databases and transfer learning to improve the analysis of small datasets from omics studies. By pretraining on electronic health record data and adaptively blending both early and late fusion strategies, COMET overcomes the limitations of existing…
Citation impact
- FWCI
- 26.27
- Percentile
- 100%
- References
- 40
Authors
20Topics & keywords
- Health records
- Electronic health record
- Computer science
- Data science
- Health care
- Political science