Increasing the Density of Laboratory Measures for Machine Learning Applications

Increasing the Density of Laboratory Measures for Machine Learning Applications

Background. The imputation of missingness is a key step in Electronic Health Records (EHR) mining, as it can significantly affect the conclusions derived from the downstream analysis in translational medicine. The missingness of laboratory values in EHR is not at random, yet imputation techniques te...

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Bibliographic Details
Main Authors: Vida Abedi, Jiang Li, Manu K. Shivakumar, Venkatesh Avula, Durgesh P. Chaudhary, Matthew J. Shellenberger, Harshit S. Khara, Yanfei Zhang, Ming Ta Michael Lee, Donna M. Wolk, Mohammed Yeasin, Raquel Hontecillas, Josep Bassaganya-Riera, Ramin Zand
Format: Article
Language:English
Published: MDPI AG 2020-12-01
Series:Journal of Clinical Medicine
Subjects:
imputation
electronic health records
machine learning
EHR
laboratory measures
medical informatics
Online Access:https://www.mdpi.com/2077-0383/10/1/103

Internet

https://www.mdpi.com/2077-0383/10/1/103

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