title: Clinical decision support systems for intensive care units: using artificial neural networks.
abstract: The paper provides an overview of applications of artificial neural networks (ANNs) to various medical problems, with a particular focus on the intensive care unit environment (ICU). Several technical approaches were tested to see whether they improve the ANN performance in estimating medical outcomes and resource utilization in adult ICUs. These experiments include: (1) use of the weight-elimination cost function; (2) use of 'high' and 'low' nodes for input variables; (3) verifying the effect of the total number of input variables on the results; (4) testing the impact of the value of the constant predictor on the performance of the ANNs. The developments presented intend to help medical and nursing personnel to assess patient status, assist in making a diagnosis, and facilitate the selection of a course of therapy.
authors: Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada. monique_frize@carleton.ca
authors: M
authors: M
authors: Frize
authors: C M
authors: CM
authors: Ennett
authors: M
authors: M
authors: Stevenson
authors: H C
authors: HC
authors: Trigg
mesh_terms: D018806:APACHE; D000328:Adult; D002170:Canada; D020000:Decision Support Systems, Clinical / Q000458:organization & administration*; D006801:Humans; D007362:Intensive Care Units / Q000191:economics / Q000458:organization & administration*; D016014:Linear Models; D016571:Neural Networks, Computer; D017063:Outcome Assessment, Health Care; D011237:Predictive Value of Tests; D012372:ROC Curve