The use of olanzapine as part of standard antiemetic regimen is cost-effective for the prevention of CINV in patients receiving HEC in multiple SEA countries.

This analysis showed that HLA-B*58:01 genetic testing before allopurinol initiation is unlikely to be a cost-effective intervention in Malaysia.

Background Understanding patient preferences in cancer management is essential for shared decision-making. Patient or societal willingness-to-pay (WTP) for desired outcomes in cancer management represents their preferences and values of these outcomes. Objective The aim of this systematic review is to critically evaluate how current literature has addressed WTP in relation to cancer treatment and achievement of outcomes. Methods Seven databases were searched from inception until 2 March 2021 to include studies with primary data of WTP values for cancer treatments or achievement of outcomes that were elicited using stated preference methods. Results Fifty-four studies were included in this review. All studies were published after year 2000 and more than 90% of the studies were conducted in high-income countries. Sample size of the studies ranged from 35 to 2040, with patient being the most studied population. There was a near even distribution between studies using contingent valuation and discrete choice experiment. Based on the included studies, the highest WTP values were for a quality-adjusted life year (QALY)

For various purposes and, in particular, in the context of data compression, a graph can be examined at three levels. Its structure can be described as the unlabeled version of the graph; then the labeling of its structure can be added; and finally, given then structure and labeling, the contents of the labels can be described. Determining the amount of information present at each level and quantifying the degree of dependence between them, requires the study of symmetry, graph automorphism, entropy, and graph compressibility. In this paper, we focus on a class of small-world graphs. These are geometric random graphs where vertices are first connected to their nearest neighbors on a circle and then pairs of non-neighbors are connected according to a distance-dependent probability distribution. We establish the degree distribution of this model, and use it to prove the model's asymmetry in an appropriate range of parameters. Then we derive the relevant entropy and structural entropy of these random graphs, in connection with graph compression.

Conservation of energy is at the core of many physical phenomena and dynamical systems. There have been a significant number of works in the past few years aimed at predicting the trajectory of motion of dynamical systems using neural networks while adhering to the law of conservation of energy. Most of these works are inspired by classical mechanics such as Hamiltonian and Lagrangian mechanics as well as Neural Ordinary Differential Equations. While these works have been shown to work well in specific domains respectively, there is a lack of a unifying method that is more generally applicable without requiring significant changes to the neural network architectures. In this work, we aim to address this issue by providing a simple method that could be applied to not just energyconserving systems, but also dissipative systems, by including a different inductive bias in different cases in the form of a regularisation term in the loss function. The proposed method does not require changing the neural network architecture and could form the basis to validate a novel idea, therefore showing promises to accelerate research in this direction.Preprint. Under review.

Deep-learning-based models are increasingly used to emulate scientific simulations to accelerate scientific research. However, accurate, supervised deep learning models require huge amount of labelled data, and that often becomes the bottleneck in employing neural networks. In this work, we leveraged an active learning approach called core-set selection to actively select data, per a pre-defined budget, to be labelled for training. To further improve the model performance and reduce the training costs, we also warm started the training using a shrink-and-perturb trick. We tested on two case studies in different fields, namely galaxy halo occupation distribution modelling in astrophysics and x-ray emission spectroscopy in plasma physics, and the results are promising: we achieved competitive overall performance compared to using a random sampling baseline, and more importantly, successfully reduced the larger absolute losses, i.e. the long tail in the loss distribution, at virtually no overhead costs.

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