Improving health care organizational management through neural network learning.
In order to provide more ethical and objective measures of the likelihood of Intensive Care Unit (ICU) recovery, hospitals have turned increasingly to decision support system software packages, such as APACHE. However, these packages derive estimates from parametric techniques, such as Binary Logit Regression (BLR) in the APACHE case, and require the developer to specify in advance the functional relationships among variables in the model. Recent rapid advancements in computer software and hardware technology have encouraged researchers to use more computationally intensive, non-parametric techniques such as Neural Networks (NNs), which are purported to be better than parametric models in terms of prediction capabilities. The present study applies both methodologies to a sample of ICU patients and shows that the NN technique predicts mortality rates more correctly than BLR, and offers a promising non-parametric alternative to the parametric methodologies in hospital settings.
D018806:APACHE; D003363:Cost Control; D003660:Decision Support Systems, Management; D003953:Diagnosis-Related Groups; D017598:Efficiency, Organizational; D006739:Hospital Administration; D006801:Humans; D007362:Intensive Care Units / Q000458:organization & administration / Q000706:statistics & numerical data*; D008404:Massachusetts; D015233:Models, Statistical; D016571:Neural Networks, Computer; D011336:Probability; D018570:Risk Assessment; D012720:Severity of Illness Index; D012984:Software; D016019:Survival Analysis