Abstract
Elevated serum chloride levels (hyperchloremia) and the administration of intravenous (IV) fluids with high chloride content have both been associated with increased morbidity and mortality in certain subgroups of critically ill patients, such as those with sepsis. Here, we demonstrate this association in a general intensive care unit (ICU) population using data from the Medical Information Mart for Intensive Care III (MIMIC-III) database and propose the use of supervised learning to predict hyperchloremia in critically ill patients. Clinical variables from records of the first 24h of adult ICU stays were represented as features for four predictive supervised learning classifiers. The best performing model was able to predict second-day hyperchloremia with an AUC of 0.80 and a ratio of 5 false alerts for every true alert, which is a clinically-actionable rate. Our results suggest that clinicians can be effectively alerted to patients at risk of developing hyperchloremia, providing an opportunity to mitigate this risk and potentially improve outcomes.
Original language | English (US) |
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Title of host publication | Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 |
Editors | Illhoi Yoo, Jinbo Bi, Xiaohua Tony Hu |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1703-1707 |
Number of pages | 5 |
ISBN (Electronic) | 9781728118673 |
DOIs | |
State | Published - Nov 2019 |
Event | 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 - San Diego, United States Duration: Nov 18 2019 → Nov 21 2019 |
Publication series
Name | Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 |
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Conference
Conference | 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 |
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Country/Territory | United States |
City | San Diego |
Period | 11/18/19 → 11/21/19 |
Funding
This research is partly supported by the NIH/NLM (R21LM012618, Luo). ACKNOWLEDGMENTS This research is partly supported by the NIH/NLM (R21LM012618, Luo).
Keywords
- biomedical informatics
- decision support systems
- electronic healthcare
- machine learning
- predictive models
ASJC Scopus subject areas
- Biochemistry
- Biotechnology
- Molecular Medicine
- Modeling and Simulation
- Health Informatics
- Pharmacology (medical)
- Public Health, Environmental and Occupational Health