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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Gilchrist, J. Frize, M. Ennett, C.M. Bariciak, E. |
| Copyright Year | 2011 |
| Description | Author affiliation: Dept. Systems and Computer Engineering, Carleton University, Ottawa, Canada (Gilchrist, J.; Ennett, C.M.) || Faculty of Medicine, University of Ottawa, Division of Neonatology, Children's Hospital of Eastern Ontario, Ottawa, Canada (Bariciak, E.) || Dept. Controls, Communication and Healthcare Informatics, Philips Research North America, Briarcliff Manor, NY, USA (Frize, M.) |
| Abstract | Current neonatal illness scoring systems are not designed to predict outcomes for individual patients, but rather can provide an overview of a population of patients for objective comparison when reporting outcomes. Having more patient-specific predictions may help physicians make better treatment decisions in a Neonatal Intensive Care Unit (NICU) environment. We developed neonatal mortality prediction models using C5.0 decision tree software that met criteria for clinically useful results (>50–60% sensitivity, >90% specificity) for individual patients using data from real-time medical measurement devices. The models were evaluated to identify: (1) the model with the best performance based on minimizing false positives, and (2) the attributes used most often in the best clinically useful models. Performance results showed that the mortality model using summary data during the first 48 hours after NICU admission provided, on average, the highest sensitivity and specificity with the least number of false positives (sensitivity=63%, specificity=94%, positive predictive value=38%), exceeding the performance criteria requested by our clinical partners. The attributes used most often in the best models for predicting mortality with our data were: mean blood pressure, serum pH, immature/total neutrophil ratio, serum sodium, serum glucose, respiratory rate, heart rate, and pO blood oxygen level. |
| Starting Page | 65 |
| Ending Page | 70 |
| File Size | 648042 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424493364 |
| e-ISBN | 9781424493388 |
| DOI | 10.1109/MeMeA.2011.5966653 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-05-30 |
| Publisher Place | Italy |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Data models Predictive models Pediatrics Real time systems Blood Databases Sensitivity real-time data clinical data respository decision-support systems outcome prediction |
| Content Type | Text |
| Resource Type | Article |
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