Feature Importance and Predictive Modeling for Multi-source Healthcare Data with Missing Values-Reference-Cited by-同舟云学术

Feature Importance and Predictive Modeling for Multi-source Healthcare Data with Missing Values

Published:2016-04-11 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 6th International Conference on Digital Health Conference
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Author:

Srinivasan Karthik¹,Currim Faiz¹,Ram Sudha¹,Lindberg Casey¹,Sternberg Esther¹,Skeath Perry¹,Najafi Bijan²,Razjouyan Javad²,Lee Hyo-Ki²,Foe-Parker Colin³,Goebel Nicole³,Herzl Reuben³,Mehl Matthias R.¹,Gilligan Brian⁴,Heerwagen Judith⁴,Kampschroer Kevin⁴,Canada Kelli⁴

Affiliation:

1. University of Arizona, Tucson, AZ, USA

2. Baylor College of Medicine, Houston, TX, USA

3. Aclima, Inc., San Francisco, CA, USA

4. U. S. General Services Administration, Washington DC, DC, USA

Funder

U.S General Services Administration

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/2896338.2896347

Reference27 articles.

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4. Chai T. and Draxler R.R. 2014. Root mean square error ( RMSE ) or mean absolute error ( MAE )? -- Arguments against avoiding RMSE in the literature. Geoscientific Model Development. (2014) 1247--1250. Chai T. and Draxler R.R. 2014. Root mean square error ( RMSE ) or mean absolute error ( MAE )? -- Arguments against avoiding RMSE in the literature. Geoscientific Model Development. (2014) 1247--1250.

5. Smart wearable systems: Current status and future challenges

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