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Neonatal mortality prediction using real-time medical measurements

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These file attachments have been under embargo and were made available to the public after the embargo was lifted on 21 September 2011.

Author: Gilchrist, J. (Carleton University) · Frize, M. (Carleton University) · Ennett, C.M. · Bariciak, E. (CHEO)
Type:Conference paper
Date:2011-09-21
Embargo lifted:2011-09-21
Publisher: IEEE
Institution: Philips Research
Source:MeMeA 2011: IEEE International Symposium on Medical Measurements and Applications, Bari, Italy, 30-31 May 2011; authors version
Identifier: MS 32.263
Keywords: clinical data repository · clinical decision support · outcome prediction · real time data
Rights: (c) IEEE

Abstract

Current neonatal illness scoring systems are not designed to predictoutcomes 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 bestperformance 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 duringthe 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 clinicalpartners. 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 pO2 blood oxygen level.

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