ELMV
| Autor: | Jianzhong Di, Lucas J. Liu, Jin Chen, Hongwei Zhang |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Source code Computer science media_common.quotation_subject Reliability (computer networking) Machine Learning (stat.ML) Machine learning computer.software_genre 01 natural sciences Machine Learning (cs.LG) 010104 statistics & probability 03 medical and health sciences 0302 clinical medicine Statistics - Machine Learning Feature (machine learning) 030212 general & internal medicine Imputation (statistics) 0101 mathematics media_common business.industry Construct (python library) Missing data Ensemble learning Identification (information) Artificial intelligence business computer |
| Zdroj: | BCB |
| DOI: | 10.1145/3388440.3412431 |
| Popis: | Many real-world Electronic Health Record (EHR) data contains a large proportion of missing values. Leaving substantial portion of missing information unaddressed usually causes significant bias, which leads to invalid conclusion to be drawn. On the other hand, training a machine learning model with a much smaller nearly-complete subset can drastically impact the reliability and accuracy of model inference. Data imputation algorithms that attempt to replace missing data with meaningful values inevitably increase the variability of effect estimates with increased missingness, making it unreliable for hypothesis validation. We propose a novel Ensemble-Learning for Missing Value (ELMV) framework, which introduces an effective approach to construct multiple subsets of the original EHR data with a much lower missing rate, as well as mobilizing a dedicated support set for the ensemble learning in the purpose of reducing the bias caused by substantial missing values. ELMV has been evaluated on a real-world healthcare data for critical feature identification as well as a batch of simulation data with different missing rates for outcome prediction. On both experiments, ELMV clearly outperforms conventional missing value imputation methods and ensemble learning models. Comment: 15 pages, 8 Figures, Typos corrected, Accepted to ACM-BCB 2020 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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