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Robust Outcome Prediction for Intensive-Care Patients
Open accessAbstract
Missing data are a major plague of medical databases in general, and of Intensive Care Unit databases in particular. The time pressure of work in an Intensive Care Unit pushes the physicians to omit randomly or selectively record data. These different omission strategies give rise to different patterns of missing data and the recommended approach of completing the database using median imputation and fitting a logistic regression model can lead to significant biases. This paper applies a new classification method, called robust Bayes classifier, which does not rely on any particular assumption about the pattern of missing data and compares it to the median imputation approach using a database of 324 Intensive Care Unit patients.
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“Studies addressing missing data were not limited to a particular missingness mechanism (e.g., missing completely at random, missing at random, missing not at random)31,32.”
From A scoping review of robustness concepts for machine learning in healthcare (2025) · cites this paper as [32]
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A scoping review of robustness concepts for machine learning in healthcare
Balendran, A., Beji, C., Bouvier, F., Khalifa, O., Evgeniou, T., Ravaud, P., & Porcher, R. (2025) · npj Digital Medicine · doi:10.1038/s41746-024-01420-1 · Cited by 43
Metadata
- Authors
- Sebastiani, P., Dybowski, R., & Ramoni, M.
- Affiliations
- King's College London (GB)
- Harvard University (US)
- Corresponding author
- Ramoni, M.
- ORCID
- Dybowski, R.
- Venue
- Methods of Information in Medicine
- Publisher
- Georg Thieme Verlag KG
- Published
- 2001
- Volume
- 40
- Issue
- 01
- Pages
- 39-45
- ISSN
- 0026-1270 (ISSN-L)
- 2511-705X
- OpenAlex
- W2002395738
- Cited by
- 12
- References
- 0
- Citations by year
- 2025: 1 · 2024: 1 · 2020: 1 · 2013: 2
- FWCI
- 4.79
- Type
- journal article
- Language
- en
- Open access
- green
- repository.uel.ac.uk
- Licence
- cc-by-nd
- Locations
- Methods of Information in Medicine (publishedVersion)
- PubMed (publishedVersion)
- UEL Research Repository (University of East London) (acceptedVersion)
- UEL Research Repository (University of East London) (submittedVersion, cc-by-nd)
- Funders
- Division of Chemistry
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- missing data, intensive care unit patients, outcome prediction, median imputation
- MeSH terms
- Bayes Theorem, Decision Support Techniques, Emergency Medicine, Health Status Indicators, Humans, Intensive Care Units, Sensitivity and Specificity, Models, Statistical
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