ML-MEDIC: A Preliminary Study of an Interactive Visual Analysis Tool Facilitating Clinical Applications of Machine Learning for Precision Medicine

Stevens, Laura; Kao, David; Hall, Jennifer; Görg, Carsten; Abdo, Kaitlyn; Linstead, Erik

doi:10.3390/app10093309

Open AccessArticle

ML-MEDIC: A Preliminary Study of an Interactive Visual Analysis Tool Facilitating Clinical Applications of Machine Learning for Precision Medicine

by

Laura Stevens

^1,2,

David Kao

¹

,

Jennifer Hall

²,

Carsten Görg

¹,

Kaitlyn Abdo

³ and

Erik Linstead

^3,*

¹

Department of Cardiology, University of Colorado Medical School, Aurora, CO 80045, USA

²

Cardiovascular Medicine, Institute for Precision Cardiovascular Medicine at the American Heart Association, Dallas, TX 75231, USA

³

Electrical Engineering and Computer Science, Chapman University, Orange, CA 92866, USA

^*

Author to whom correspondence should be addressed.

Appl. Sci. 2020, 10(9), 3309; https://doi.org/10.3390/app10093309

Submission received: 19 March 2020 / Revised: 22 April 2020 / Accepted: 6 May 2020 / Published: 9 May 2020

(This article belongs to the Section Computing and Artificial Intelligence)

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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.

Keywords: data science; interactive visual analysis; data-driven medicine; machine learning; cloud computing

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MDPI and ACS Style

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

AMA Style

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. Applied Sciences. 2020; 10(9):3309. https://doi.org/10.3390/app10093309

Chicago/Turabian Style

Stevens, Laura, David Kao, Jennifer Hall, Carsten Görg, Kaitlyn Abdo, and Erik Linstead. 2020. "ML-MEDIC: A Preliminary Study of an Interactive Visual Analysis Tool Facilitating Clinical Applications of Machine Learning for Precision Medicine" Applied Sciences 10, no. 9: 3309. https://doi.org/10.3390/app10093309

APA Style

Stevens, L., Kao, D., Hall, J., Görg, C., Abdo, K., & Linstead, E. (2020). ML-MEDIC: A Preliminary Study of an Interactive Visual Analysis Tool Facilitating Clinical Applications of Machine Learning for Precision Medicine. Applied Sciences, 10(9), 3309. https://doi.org/10.3390/app10093309

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ML-MEDIC: A Preliminary Study of an Interactive Visual Analysis Tool Facilitating Clinical Applications of Machine Learning for Precision Medicine

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