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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Feldman, K. Chawla, N.V. |
| Copyright Year | 2014 |
| Description | Author affiliation: Dept. of Comput. Sci. & Eng, Univ. of Notre Dame, Notre Dame, IN, USA (Feldman, K.; Chawla, N.V.) |
| Abstract | In today's healthcare environment, nurses play an integral role in determining patient outcomes. This role becomes especially clear in intensive care units such as the Neonatal Intensive Care Unit (NICU). In the NICU, critically ill infants rely almost completely on the care of these nurses for survival. Given the importance of their role, and the volatile conditions of the infants, it is imperative that nurses be able to focus on the infants in their charge. In order to provide this level of care there must be an appropriate infant to nurse ratio each day. However traditional staffing models often utilize a number of factors, including historical census counts, which when incorrect leave a NICU at risk of operating barely reaching, or even below the recommended staffing level. This work will present the novel ADMIT (Admission Duration Model for Infant Treatment) model, which yields personalized length of stay estimates for an infant, utilizing data available from time of admission to the NICU. |
| Starting Page | 583 |
| Ending Page | 587 |
| File Size | 183217 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781479956692 |
| DOI | 10.1109/BIBM.2014.6999225 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-11-02 |
| Publisher Place | UK |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Pediatrics Correlation Hospitals Predictive models Boosting Data models Diseases |
| Content Type | Text |
| Resource Type | Article |
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