ELMV: an Ensemble-Learning Approach for Analyzing Electrical Health Records with Significant Missing Values Article Swipe
Lucas J. Liu
,
Hongwei Zhang
,
Jianzhong Di
,
Jin Chen
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2006.14942
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2006.14942
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.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2006.14942
- https://arxiv.org/pdf/2006.14942
- OA Status
- green
- Cited By
- 1
- References
- 33
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3037857794
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- OpenAlex ID
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https://openalex.org/W3037857794Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2006.14942Digital Object Identifier
- Title
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ELMV: an Ensemble-Learning Approach for Analyzing Electrical Health Records with Significant Missing ValuesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-06-25Full publication date if available
- Authors
-
Lucas J. Liu, Hongwei Zhang, Jianzhong Di, Jin ChenList of authors in order
- Landing page
-
https://arxiv.org/abs/2006.14942Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2006.14942Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2006.14942Direct OA link when available
- Concepts
-
Missing data, Ensemble learning, Health records, Computer science, Statistics, Artificial intelligence, Machine learning, Mathematics, Political science, Health care, LawTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 1Per-year citation counts (last 5 years)
- References (count)
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33Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.smaller | 45 |
| abstract_inverted_index.subsets | 104 |
| abstract_inverted_index.support | 122 |
| abstract_inverted_index.usually | 21 |
| abstract_inverted_index.values. | 13, 139 |
| abstract_inverted_index.accuracy | 54 |
| abstract_inverted_index.approach | 100 |
| abstract_inverted_index.contains | 7 |
| abstract_inverted_index.critical | 150 |
| abstract_inverted_index.ensemble | 126, 180 |
| abstract_inverted_index.increase | 71 |
| abstract_inverted_index.learning | 40, 127, 181 |
| abstract_inverted_index.multiple | 103 |
| abstract_inverted_index.original | 107 |
| abstract_inverted_index.reducing | 132 |
| abstract_inverted_index.training | 37 |
| abstract_inverted_index.construct | 102 |
| abstract_inverted_index.dedicated | 121 |
| abstract_inverted_index.different | 162 |
| abstract_inverted_index.effective | 99 |
| abstract_inverted_index.estimates | 76 |
| abstract_inverted_index.evaluated | 143 |
| abstract_inverted_index.increased | 78 |
| abstract_inverted_index.Electronic | 2 |
| abstract_inverted_index.algorithms | 60 |
| abstract_inverted_index.conclusion | 29 |
| abstract_inverted_index.framework, | 95 |
| abstract_inverted_index.healthcare | 147 |
| abstract_inverted_index.hypothesis | 84 |
| abstract_inverted_index.imputation | 59, 177 |
| abstract_inverted_index.inevitably | 70 |
| abstract_inverted_index.inference. | 57 |
| abstract_inverted_index.introduces | 97 |
| abstract_inverted_index.meaningful | 68 |
| abstract_inverted_index.mobilizing | 119 |
| abstract_inverted_index.proportion | 10 |
| abstract_inverted_index.real-world | 1, 146 |
| abstract_inverted_index.simulation | 159 |
| abstract_inverted_index.unreliable | 82 |
| abstract_inverted_index.drastically | 49 |
| abstract_inverted_index.information | 19 |
| abstract_inverted_index.outperforms | 173 |
| abstract_inverted_index.prediction. | 167 |
| abstract_inverted_index.reliability | 52 |
| abstract_inverted_index.significant | 23 |
| abstract_inverted_index.substantial | 15, 137 |
| abstract_inverted_index.unaddressed | 20 |
| abstract_inverted_index.validation. | 85 |
| abstract_inverted_index.variability | 73 |
| abstract_inverted_index.conventional | 174 |
| abstract_inverted_index.experiments, | 170 |
| abstract_inverted_index.missingness, | 79 |
| abstract_inverted_index.identification | 152 |
| abstract_inverted_index.nearly-complete | 46 |
| abstract_inverted_index.Ensemble-Learning | 90 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.4099999964237213 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |