44 resultados para 159-959A


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We present a systematic, practical approach to developing risk prediction systems, suitable for use with large databases of medical information. An important part of this approach is a novel feature selection algorithm which uses the area under the receiver operating characteristic (ROC) curve to measure the expected discriminative power of different sets of predictor variables. We describe this algorithm and use it to select variables to predict risk of a specific adverse pregnancy outcome: failure to progress in labour. Neural network, logistic regression and hierarchical Bayesian risk prediction models are constructed, all of which achieve close to the limit of performance attainable on this prediction task. We show that better prediction performance requires more discriminative clinical information rather than improved modelling techniques. It is also shown that better diagnostic criteria in clinical records would greatly assist the development of systems to predict risk in pregnancy.

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The complex, fragmented and diverse aspects of a sustainable development perspective are translated into an eight-point framework that defines a problem boundary larger than that traditionally adopted by civil engineers. This leads to practical questions intended to inform engineers who ask 'am I being sustainable?' during project implementation. The value of the questions is tested against a case history of a wastewater treatment project. This demonstrates the relevance of the questions to successive project delivery phases of defining the problem, choosing a solution and implementing that solution through design, construction and operation. The case history highlights that answers to several of the additional questions raised by considering this wider problem space are currently buried within government and clients' policies, regulations and standard practice; these answers may not be accessible to the professional engineer.