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John L. Dart Library
9 a.m. – 7 p.m.
Phone: (843) 722-7550
West Ashley Library
9 a.m. – 7 p.m.
Phone: (843) 766-6635
Folly Beach Library
Closed
Phone: (843) 588-2001
Edgar Allan Poe/Sullivan's Island Library
Closed for renovations
Phone: (843) 883-3914
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.
Phone: (843) 849-6161
McClellanville Library
9 a.m. - 6 p.m.
Phone: (843) 887-3699
Keith Summey North Charleston Library
9 a.m. – 8 p.m.
Phone: (843) 744-2489
John's Island Library
9 a.m. – 8 p.m.
Phone: (843) 559-1945
Hurd/St. Andrews Library
9 a.m. – 8 p.m.
Phone: (843) 766-2546
Miss Jane's Building (Edisto Library Temporary Location)
9 a.m. – 6 p.m.
Phone: (843) 869-2355
Dorchester Road Library
9 a.m. – 8 p.m.
Phone: (843) 552-6466
Baxter-Patrick James Island
9 a.m. – 8 p.m.
Phone: (843) 795-6679
Main Library
9 a.m. – 8 p.m.
Phone: (843) 805-6930
Bees Ferry West Ashley Library
9 a.m. – 8 p.m.
Phone: (843) 805-6892
Mobile Library
9 a.m. - 5 p.m.
Phone: (843) 805-6909
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Development and validation of predictive models for COVID-19 outcomes in a safety-net hospital population.
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- Author(s): Hao, Boran; Hu, Yang; Sotudian, Shahabeddin; Zad, Zahra; Adams, William G; Assoumou, Sabrina A; Hsu, Heather; Mishuris, Rebecca G; Paschalidis, Ioannis C
- Source:
Journal of the American Medical Informatics Association; Jul2022, Vol. 29 Issue 7, p1253-1262, 10p, 4 Charts - Source:
- Additional Information
- Abstract: Objective To develop predictive models of coronavirus disease 2019 (COVID-19) outcomes, elucidate the influence of socioeconomic factors, and assess algorithmic racial fairness using a racially diverse patient population with high social needs. Materials and Methods Data included 7,102 patients with positive (RT-PCR) severe acute respiratory syndrome coronavirus 2 test at a safety-net system in Massachusetts. Linear and nonlinear classification methods were applied. A score based on a recurrent neural network and a transformer architecture was developed to capture the dynamic evolution of vital signs. Combined with patient characteristics, clinical variables, and hospital occupancy measures, this dynamic vital score was used to train predictive models. Results Hospitalizations can be predicted with an area under the receiver-operating characteristic curve (AUC) of 92% using symptoms, hospital occupancy, and patient characteristics, including social determinants of health. Parsimonious models to predict intensive care, mechanical ventilation, and mortality that used the most recent labs and vitals exhibited AUCs of 92.7%, 91.2%, and 94%, respectively. Early predictive models, using labs and vital signs closer to admission had AUCs of 81.1%, 84.9%, and 92%, respectively. Discussion The most accurate models exhibit racial bias, being more likely to falsely predict that Black patients will be hospitalized. Models that are only based on the dynamic vital score exhibited accuracies close to the best parsimonious models, although the latter also used laboratories. Conclusions This large study demonstrates that COVID-19 severity may accurately be predicted using a score that accounts for the dynamic evolution of vital signs. Further, race, social determinants of health, and hospital occupancy play an important role. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of Journal of the American Medical Informatics Association is the property of Oxford University Press / USA 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.)
- Abstract:
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