OBJECTIVE: To compare statistical and connectionist models for the prediction of chronicity which is influenced by patient disease and external factors. DESIGN: Retrospective development of predictive criteria and subsequent prospective testing of the same predictive criteria, using multiple logistic regression and three architecturally distinct neural networks; revision of predictive criteria. SETTING: Surgical intensive care unit (ICU) equipped with a clinical information system in a +/- 1000-bed university hospital. PATIENTS: Four hundred ninety-one patients with ICU length of stay 3 days who survived at least an additional 4 days. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Chronicity was defined as a length of stay > 7 days. Neural networks predicted chronicity more reliably than the statistical model regardless of the former's architecture. However, the neural networks' ability to predict this chronicity degraded over time. CONCLUSIONS: Connectionist models may contribute to the prediction of clinical trajectory, including outcome and resource utilization, in surgical ICUs.
OBJECTIVE: To compare statistical and connectionist models for the prediction of chronicity which is influenced by patient disease and external factors. DESIGN: Retrospective development of predictive criteria and subsequent prospective testing of the same predictive criteria, using multiple logistic regression and three architecturally distinct neural networks; revision of predictive criteria. SETTING: Surgical intensive care unit (ICU) equipped with a clinical information system in a +/- 1000-bed university hospital. PATIENTS: Four hundred ninety-one patients with ICU length of stay 3 days who survived at least an additional 4 days. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Chronicity was defined as a length of stay > 7 days. Neural networks predicted chronicity more reliably than the statistical model regardless of the former's architecture. However, the neural networks' ability to predict this chronicity degraded over time. CONCLUSIONS: Connectionist models may contribute to the prediction of clinical trajectory, including outcome and resource utilization, in surgical ICUs.
Authors: Rocco J LaFaro; Suryanarayana Pothula; Keshar Paul Kubal; Mario Emil Inchiosa; Venu M Pothula; Stanley C Yuan; David A Maerz; Lucresia Montes; Stephen M Oleszkiewicz; Albert Yusupov; Richard Perline; Mario Anthony Inchiosa Journal: PLoS One Date: 2015-12-28 Impact factor: 3.240