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A comparison of statistical and connectionist models for the prediction of chronicity in a surgical intensive care
T G Buchman1, K L Kubos, A J Seidler
1Johns Hopkins Medical Institutions, Baltimore, MD.
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.