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PET/CT imaging is the gold standard for the diagnosis and staging of lung cancer. However, especially in healthcare systems with limited resources, costly PET/CT images are often not readily available. Conventional machine learning models either process CT or PET/CT images but not both. Models designed for PET/CT images are hence restricted by the number of PET images, such that they are unable to additionally leverage CT-only data. In this work, we apply the concept of visual soft attention to efficiently learn a model for lung cancer segmentation from only a small fraction of PET/CT scans and a larger pool of CT-only scans. We show that our model is capable of jointly processing PET/CT as well as CT-only images, which performs on par with the respective baselines whether or not PET images are available at test time. We then demonstrate that the model learns efficiently from only a few PET/CT scans in a setting where mostly CT-only data is available, unlike conventional models. Electronic supplementary material The online version of this chapter (10.1007/978-3-030-71278-5_32) contains supplementary material, which is available to authorized users.

Multimodal generative models learn a joint distribution over multiple modalities and thus have the potential to learn richer representations than unimodal models. However, current approaches are either inefficient in dealing with more than two modalities or fail to capture both modality-specific and shared variations. We introduce a new multimodal generative model that integrates both modality-specific and shared factors and aggregates shared information across any subset of modalities efficiently. Our method partitions the latent space into disjoint subspaces for modality-specific and shared factors and learns to disentangle these in a purely self-supervised manner. Empirically, we show improvements in representation learning and generative performance compared to previous methods and showcase the disentanglement capabilities. Electronic supplementary material The online version of this chapter (10.1007/978-3-030-71278-5_33) contains supplementary material, which is available to authorized users.

Machine learning models may enhance the early detection of clinically relevant hyperbilirubinemia based on patient information available in every hospital. We conducted a longitudinal study on preterm and term born neonates with serial measurements of total serum bilirubin in the first two weeks of life. An ensemble, that combines a logistic regression with a random forest classifier, was trained to discriminate between the two classes phototherapy treatment vs. no treatment. Of 362 neonates included in this study, 98 had a phototherapy treatment, which our model was able to predict up to 48 h in advance with an area under the ROC-curve of 95.20%. From a set of 44 variables, including potential laboratory and clinical confounders, a subset of just four (bilirubin, weight, gestational age, hours since birth) suffices for a strong predictive performance. The resulting early phototherapy prediction tool (EPPT) is provided as an open web application. Early detection of clinically relevant hyperbilirubinemia can be enhanced by the application of machine learning. Existing guidelines can be further improved to optimize timing of bilirubin measurements to avoid toxic hyperbilirubinemia in high-risk patients while minimizing unneeded measurements in neonates who are at low risk.

This research uses football online betting odds of a broad variety of matches and bookmakers to identify known biases in odds pricing, namely the favorite-longshot bias and the away-favorite bias. Furthermore, it tries to answer the question whether a naive strategy of betting against these biases can be profitable. Our findings are consistent with previous research, confirming the existence of the favorite-longshot bias, but turn out to be in contrast with regard to the away-favorite bias, for which there is little evidence in the data. Also, we find that a strategy of betting against the favorite-longshot bias can be marginally profitable, whereas bets in favor of it result in large losses on average.