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West Ashley Library
9 a.m. - 7 p.m.
Phone: (843) 766-6635
Wando Mount Pleasant Library
9 a.m. – 8 p.m.
Phone: (843) 805-6888
Village Library
9 a.m. – 6 p.m.
Phone: (843) 884-9741
St. Paul's/Hollywood Library
9 a.m. – 8 p.m.
Phone: (843) 889-3300
Otranto Road Library
9 a.m. – 8 p.m.
Phone: (843) 572-4094
Mt. Pleasant Library
9 a.m. – 8 p.m.
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McClellanville Library
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Keith Summey North Charleston Library
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John's Island Library
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Hurd/St. Andrews Library
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Folly Beach Library
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Dorchester Road Library
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Phone: (843) 552-6466
John L. Dart Library
9 a.m. – 7 p.m.
Phone: (843) 722-7550
Bees Ferry West Ashley Library
9 a.m. – 8 p.m.
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Miss Jane's Building (Edisto Library Temporary Location)
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Predicting In-Hospital Death from Derived EHR Trajectory Features.
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- Author(s): BOPCHE, Rajeev; GUSTAD, Lise Tuset; AFSET, Jan Egil; DAMÅS, Jan Kristian; NYTRØ, Øystein
- Source:
Medinfo; 2023, Vol. 310, p269-273, 5p- Subject Terms:
DECISION trees; CONFIDENCE intervals; ACADEMIC medical centers; NOSOLOGY; CONFERENCES & conventions; MACHINE learning; RANDOM forest algorithms; HOSPITAL mortality; RISK assessment; DATABASE management; SEPSIS; COMPARATIVE studies; DESCRIPTIVE statistics; ELECTRONIC health records; PREDICTION models; PATIENT care; RECEIVER operating characteristic curves; EVALUATION - Source:
- Additional Information
- Subject Terms:
- Abstract: Medical histories of patients can predict a patient's immediate future. While most studies propose to predict survival from vital signs and hospital tests within one episode of care, we carried out selective feature engineering from longitudinal medical records in this study to develop a dataset with derived features. We thereafter trained multiple machine learning models for the binary prediction of whether an episode of care will culminate in death among patients suspected of bloodstream infections. The machine learning classifier performance is evaluated and compared and the feature importance impacting the model output is explored. The extreme gradient boosting model achieved the best performance for predicting death in the next hospital episode with an accuracy of 92%. Age at the time of the first visit, length of history, and information related to recent episodes were the most critical features. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of Medinfo is the property of IOS Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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