Prediction of New Onset Diabetes after Liver Transplant

Yasodhara, Angeline; Bhat, Mamatha; Goldenberg, Anna

doi:10.48550/arxiv.1812.00506

Cited by 1 publication

(1 citation statement)

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“…Consequently, WTTE-RNN enjoyed immediate popularity in the applications for discrete event predictions, mostly in the medical domain, for example, in [8] for hypoglycemic event prediction based on data collected by wearable devices and in [20] for breast cancer-related data. Several other applications in the medical prognosis field exist such as in [4,18,39]. WTTE-RNN also finds applications in telecom industry, such as in [33] which compared a multilayered perceptron (MLP) with WTTE-RNN algorithm for customer churn prediction in the telecom industry.…”

Section: Weibull Time-to-event Recurrent Neural Networkmentioning

confidence: 99%

Weibull recurrent neural networks for failure prognosis using histogram data

Dhada

Parlikad

Steinert

et al. 2022

Neural Comput & Applic

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Weibull time-to-event recurrent neural networks (WTTE-RNN) is a simple and versatile prognosis algorithm that works by optimising a Weibull survival function using a recurrent neural network. It offers the combined benefits of the sequential nature of the recurrent neural network, and the ability of the Weibull loss function to incorporate censored data. The goal of this paper is to present the first industrial use case of WTTE-RNN for prognosis. Prognosis of turbocharger conditions in a fleet of heavy-duty trucks is presented here, where the condition data used in the case study were recorded as a time series of sparsely sampled histograms. The experiments include comparison of the prediction models trained using data from the entire fleet of trucks vs data from clustered sub-fleets, where it is concluded that clustering is only beneficial as long as the training dataset is large enough for the model to not overfit. Moreover, the censored data from assets that did not fail are also shown to be incorporated while optimising the Weibull loss function and improve prediction performance. Overall, this paper concludes that WTTE-RNN-based failure predictions enable predictive maintenance policies, which are enhanced by identifying the sub-fleets of similar trucks.

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Section: Weibull Time-to-event Recurrent Neural Networkmentioning

confidence: 99%