Gestational diabetes mellitus (GDM) is a kind of diabetes that requires persistent medical care in patient self management education to prevent acute complications. One of the common and main problems in diagnosis of the diabetes is the weakness in its initial stages of the illness. This paper intends to propose an expert system in order to diagnose the risk of GDM by using FIS model. The knowl...

background: this study aimed at estimating the proportion of diabetes as a risk factor to the attributable burden of cardiovascular diseases in iran. methods: comparative risk assessment methodology was used to calculating potential impact fraction (pif). to calculate pif, data on the prevalence of newly diagnosed diabetes mellitus (ndm) and known diabetes mellitus (kdm) were obtained from 3rd ...

Minimum Bayes Risk (MBR) decoders improve upon MAP decoders by directly optimizing loss function of interest: Word Error Rate MBR decoding is expensive when the search spaces are large Segmental MBR (SMBR) decoding breaks the single utterance-level MBR decoder into a sequence of simpler search problems. – To do this, the N-best lists or lattices need to be segmented We present: A new lattice se...

abstract background and purpose: high-risk pregnancy is referred to a situation in which mother, fetus or neonate are in higher risk of morbidity or mortality. because of adverse outcomes of high-risk pregnancies, this study aims to determine these outcomes in the north of iran. materials and methods: we recruited 803 urban and rural pregnant women in this crosssectional study via consensus met...

Gestational diabetes mellitus (GDM) is a kind of diabetes that requires persistent medical care in patient self management education to prevent acute complications. One of the common and main problems in diagnosis of the diabetes is the weakness in its initial stages of the illness. This paper intends to propose an expert system in order to diagnose the risk of GDM by using FIS model. The knowl...

We show that the in-sample estimate of the variance of a global minimum risk portfolio constructed using an estimated covariance matrix of returns will on average be strictly smaller than its true variance. Scaling the in-sample estimate upward by a standard degrees-of-freedom related factor or using the Bayes covariance matrix estimator can be inadequate; the correction is likely to be twice a...

We present a Minimum Bayes Risk (MBR) decoder for statistical machine translation. The approach aims to minimize the expected loss of translation errors with regard to the BLEU score. We show that MBR decoding on N -best lists leads to an improvement of translation quality. We report the performance of the MBR decoder on four different tasks: the TCSTAR EPPS Spanish-English task 2006, the NIST ...

Abstract. We analyse contracts which pay out a guaranteed minimum rate of return and a fraction of a positive excess rate, which is specified on the basis of a benchmark portfolio. These contracts are closely related to unit–linked life–insurance/savings plan products and can be considered as alternatives to a direct investment in the underlying benchmark portfolio. The option embedded into the...

Minimum Bayes Risk (MBR) has been used as a decision rule for both singlesystem decoding and system combination in machine translation. For system combination, we argue that common MBR implementations are actually not correct, since probabilities in the hypothesis space cannot be reliably estimated. These implementations achieve the effect of consensus decoding (which may be beneficial in its o...

A new variant of AdaBoost is applied to a Minimum Bayes Risk discriminative training procedure that directly aims at reducing Word Error Rate for Automatic Speech Recognition. Both techniques try to improve the discriminative power of a classifier and we show that can be combined together to yield even better performance on a small vocabulary continuous speech recognition task. Our results also...