Document Type
Article
Publication Date
5-9-2020
Abstract
Accessible interactive tools that integrate machine learning methods with clinical research and reduce the programming experience required are needed to move science forward. Here, we present Machine Learning for Medical Exploration and Data-Inspired Care (ML-MEDIC), a point-and-click, interactive tool with a visual interface for facilitating machine learning and statistical analyses in clinical research. We deployed ML-MEDIC in the American Heart Association (AHA) Precision Medicine Platform to provide secure internet access and facilitate collaboration. ML-MEDIC’s efficacy for facilitating the adoption of machine learning was evaluated through two case studies in collaboration with clinical domain experts. A domain expert review was also conducted to obtain an impression of the usability and potential limitations.
Recommended Citation
Stevens, L.; Kao, D.; Hall, J.; Görg, C.; Abdo, K.; Linstead, E. ML-MEDIC: A Preliminary Study of an Interactive Visual Analysis Tool Facilitating Clinical Applications of Machine Learning for Precision Medicine. Appl. Sci. 2020, 10, 3309. https://doi.org/10.3390/app10093309
Copyright
The authors
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Included in
Biomedical Commons, Cardiovascular Diseases Commons, Health Information Technology Commons, Other Biomedical Engineering and Bioengineering Commons, Other Computer Engineering Commons, Other Computer Sciences Commons, Other Electrical and Computer Engineering Commons, Software Engineering Commons
Comments
This article was originally published in Applied Sciences, volume 10, issue 9, in 2020. https://doi.org/10.3390/app10093309