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Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Critical Care giúp cho các bạn có thêm kiến thức về ngành y học đề tài: Computer says 2.5 litres - how best to incorporate intelligent software into clinical decision making in the intensive care unit? | Available online http ccforum.eom content 13 1 111 Commentary Computer says 2.5 litres - how best to incorporate intelligent software into clinical decision making in the intensive care unit Katie Lane and Owen Boyd Department of Critical Care Medicine Royal Sussex County Hospital Eastern Road Brighton BN2 5BE UK Corresponding author Owen Boyd owen.boyd@bsuh.nhs.uk Published 23 January 2009 This article is online at http ccforum.com content 13 1 111 2009 BioMed Central Ltd Critical Care 2009 13 111 doi 10.1186 cc7156 See related research by Celi et al. http ccforum.com content 12 6 R151 Abstract What will be the role of the intensivist when computer-assisted decision support reaches maturity Celi s group reports that Bayesian theory can predict a patient s fluid requirement on day 2 in 78 of cases based on data collected on day 1 and the known associations between those data based on observations in previous patients in their unit. There are both advantages and limitations to the Bayesian approach and this test study identifies areas for improvement in future models. Although such models have the potential to improve diagnostic and therapeutic accuracy they must be introduced judiciously and locally to maximize their effect on patient outcome. Efficacy is thus far undetermined and these novel approaches to patient management raise new challenges not least medicolegal ones. Introduction Does the computer-driven prediction of fluid requirement spell the beginning of the end for the intensivist s daily management of the critically ill patient Does it instead represent a useful adjunct to fluid balance assessment in the critically ill In the previous edition of Critical Care Celi and coworkers 1 describe an artificial intelligence tool that can predict the quantity of fluid a critically ill patient will require on their second day of intensive care. From a database of 3 014 patients receiving inotropic support Celi and colleagues constructed a Bayesian network see .
