Summary Chronic alcohol consumption may lead to primary and secondary malnutrition. In particular, protein energy malnutrition not only aggravates alcoholic liver disease but also correlates with impaired liver function and increased mortality. Therefore, in these patients, adequate nutritional support should be implemented in order to improve their prognosis. Clinical trials addressing this issue have shown that nutritional therapy either enterally or parenterally improves various aspects of malnutrition, and there is increasing evidence that it may also improve survival. Therefore, malnourished alcoholics should be administered a diet rich in carbohydrate‐ and protein‐derived calories preferentially via the oral or enteral route. Micronutrient deficiencies typically encountered in alcoholics, such as for thiamine and folate, require specific supplementation. Patients with hepatic encephalopathy may be treated with branched‐chain amino acids in order to achieve a positive nitrogen balance. Fatty liver represents the early stage of alcoholic liver disease, which is usually reversible with abstinence. Metadoxine appears to improve fatty liver but confirmatory studies are necessary. S‐adenosyl‐l‐methionine may be helpful for patients with severe alcoholic liver damage, since various mechanisms of alcohol‐related hepatotoxicity are counteracted with this essential methyl group donor, while a recent large trial showed that the use of polyenylphosphatidylcholine is of limited efficacy.

Deciding who should receive a liver transplant (LT) depends on both urgency and utility. Most survival scores are validated through discriminative tests, which compare predicted outcomes between patients. Assessing post-transplant survival utility is not discriminate, but should be “calibrated” to be effective. There are currently no such calibrated models. We developed and validated a novel calibrated model to predict individual survival after LT for Primary Sclerosing Cholangitis (PSC). We applied a software tool, PSSP, to adult patients in the Scientific Registry of Transplant Recipients (n = 2769) who received a LT for PSC between 2002 and 2013; this produced a model for predicting individual survival distributions for novel patients. We also developed an appropriate evaluation measure, D-calibration, to validate this model. The learned PSSP model showed an excellent D-calibration (p = 1.0), and passed the single-time calibration test (Hosmer-Lemeshow p-value of over 0.05) at 0.25, 1, 5 and 10 years. In contrast, the model based on traditional Cox regression showed worse calibration on long-term survival and failed at 10 years (Hosmer-Lemeshow p value = 0.027). The calculator and visualizer are available at: http://pssp.srv.ualberta.ca/calculator/liver_transplant_2002. In conclusion we present a new tool that accurately estimates individual post liver transplantation survival.

Uncertainty in poker stems from two key sources, the shuffled deck and an adversary whose strategy is unknown. One approach to playing poker is to find a pessimistic game-theoretic solution (i.e., a Nash equilibrium), but human players have idiosyncratic weaknesses that can be exploited if some model or counterstrategy can be learned by observing their play. However, games against humans last for at most a few hundred hands, so learning must be very fast to be useful. We explore two approaches to opponent modelling in the context of Kuhn poker, a small game for which game-theoretic solutions are known. Parameter estimation and expert algorithms are both studied. Experiments demonstrate that, even in this small game, convergence to maximally exploitive solutions in a small number of hands is impractical, but that good (e.g., better than Nash) performance can be achieved in as few as 50 hands. Finally, we show that amongst a set of strategies with equal game-theoretic value, in particular the set of Nash equilibrium strategies, some are preferable because they speed learning of the opponent's strategy by exploring it more effectively.

Automated brain tumor segmenters typically run a "skull-stripping" pre-process to extract the brain from the 3D image, before segmenting the area of interest within the extracted volume. We demonstrate that an effective existing segmenter can be improved by replacing its skull-stripper component with one that instead uses a registration-based approach. In particular, we compare our automated brain segmentation system with the original system as well as three other approaches that differ only by using a different skull-stripper-BET, HWA, and ROBEX: (1) Over scans of 120 patients with brain tumors, our system's segmentation accuracy (Dice score with respect to expert segmentation) is 8.6% (resp. 2.7%) better than the original system on gross tumor volumes (resp. edema); (2) Over 103 scans of controls, the new system found 92.9% (resp. 57.8%) fewer false positives on T1C (resp. FLAIR) volumes. (The other three methods were significantly worse on both tasks). Finally, the new registration-based approach is over 15% faster than the original, requiring on average only 178 CPU seconds per volume.

This paper introduces an automatic brain tumor segmentation method (ABTS) for segmenting multiple components of brain tumor using four magnetic resonance image modalities. ABTS's four stages involve automatic histogram multi-thresholding and morphological operations including geodesic dilation. Our empirical results, on 16 real tumors, show that ABTS works very effectively, achieving a Dice accuracy compared to expert segmentation of 81% in segmenting edema and 85% in segmenting gross tumor volume (GTV).