A comparison of statistical and connectionist models for the prediction of chronicity in a surgical intensive care unit.
OBJECTIVE
To compare statistical and connectionist models for the prediction of chronicity which is influenced by patient disease and external factors.


METHODS
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.


METHODS
Surgical intensive care unit (ICU) equipped with a clinical information system in a +/- 1000-bed university hospital.


METHODS
Four hundred ninety-one patients with ICU length of stay 3 days who survived at least an additional 4 days.


METHODS
None.


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.
Buchman
T G
TG
Johns Hopkins Medical Institutions, Baltimore, MD.
Kubos
K L
KL
Seidler
A J
AJ
Siegforth
M J
MJ
D005069:Evaluation Studies as Topic; D005544:Forecasting; D006295:Health Resources / Q000706:statistics & numerical data; D006801:Humans; D007362:Intensive Care Units / Q000706:statistics & numerical data*; D007902:Length of Stay / Q000706:statistics & numerical data*; D016015:Logistic Models; D016571:Neural Networks, Computer; D017063:Outcome Assessment, Health Care; D011182:Postoperative Care / Q000706:statistics & numerical data*; D011379:Prognosis; D011446:Prospective Studies; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D013997:Time Factors