Conference proceeding
An Ensemble Method for Data Imputation
2019 IEEE International Conference on Healthcare Informatics (ICHI), pp.1-3
06/2019
DOI: 10.1109/ICHI.2019.8904629
Abstract
Healthcare analytics is transforming the healthcare industry, finding novel and useful patterns in patient data such as electronic health records (EHRs), to provide patients with improved care and service. Researchers train machine learning (ML) algorithms to discover new knowledge by mining patients' clinical data to provide better care such as accurate diagnoses and personalized therapy. However, the quality of clinical patient data may inhibit the discovery process. The type and frequency of collected data varies based on a patient' s clinical condition and administrative requirements. Patients can have different diagnostic tests and treatments at different times, even with the same symptoms. Therefore in EHRs, many aspects of a patient' s clinical condition could be unmeasured at different timestamps. Missing measurements may be clinically important, but cannot be used by ML algorithms. To utilize all clinical data and achieve optimal performance of ML algorithms, we address the missing data issue by imputing missing time series values. We will describe our imputation methods and apply them to 13 common clinical laboratory test results obtained from a set of 8267 inpatients to evaluate their performance using normalized root-mean-squared deviation (nRMSD) [1].
Details
- Title: Subtitle
- An Ensemble Method for Data Imputation
- Creators
- Yichen Ding - University of IowaW. Nick Street - University of IowaLing Tong - University of IowaShangguan Wang - University of Iowa
- Resource Type
- Conference proceeding
- Publication Details
- 2019 IEEE International Conference on Healthcare Informatics (ICHI), pp.1-3
- Publisher
- IEEE
- DOI
- 10.1109/ICHI.2019.8904629
- eISSN
- 2575-2634
- Language
- English
- Date published
- 06/2019
- Academic Unit
- Bus Admin College; Nursing; Computer Science; Business Analytics
- Record Identifier
- 9984380487302771
Metrics
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