Abstract: Palliative care is referred to a set of programs for patients that suffer life-limiting illnesses. These programs aim to maximize the quality of life (QoL) for the last stage of life. They are currently based on clinical evaluation of the risk of 1-year mortality. The main aim of this work is to develop and validate machine-learning-based models to predict the exitus of a patient within the next year using data gathered at hospital admission. Five machine-learning techniques were applied using a retrospective dataset. The evaluation was performed with five metrics computed by a resampling strategy: Accuracy, the area under the ROC curve, Specificity, Sensitivity, and the Balanced Error Rate. All models reported an AUC ROC from 0.857 to 0.91. Specifically, Gradient Boosting Classifier was the best model, producing an AUC ROC of 0.91, a sensitivity of 0.858, a specificity of 0.808, and a BER of 0.1687. Information from standard procedures at hospital admission combined with machine learning techniques produced models with competitive discriminative power. Our models reach the best results reported in the state of the art. These results demonstrate that they can be used as an accurate data-driven palliative care criteria inclusion.

Abstract: Motivated by the recent XENON1T results, we explore various new physics models that can be discovered through searches for electron recoils in O(keV)-threshold direct-detection experiments. First, we consider the absorption of axion-like particles, dark photons, and scalars, either as dark matter relics or being produced directly in the Sun. In the latter case, we find that keV mass bosons produced in the Sun provide an adequate fit to the data but are excluded by stellar cooling constraints. We address this tension by introducing a novel Chameleon-like axion model, which can explain the excess while evading the stellar bounds. We find that absorption of bosonic dark matter provides a viable explanation for the excess only if the dark matter is a dark photon or an axion. In the latter case, photophobic axion couplings are necessary to avoid X-ray constraints. Second, we analyze models of dark matter-electron scattering to determine which models might explain the excess. Standard scattering of dark matter with electrons is generically in conflict with data from lower-threshold experiments. Momentum-dependent interactions with a heavy mediator can fit the data with dark matter mass heavier than a GeV but are generically in tension with collider constraints. Next, we consider dark matter consisting of two (or more) states that have a small mass splitting. The exothermic (down)scattering of the heavier state to the lighter state can fit the data for keV mass splittings. Finally, we consider a subcomponent of dark matter that is accelerated by scattering off cosmic rays, finding that dark matter interacting though an O(100 keV)-mass mediator can fit the data. The cross sections required in this scenario are, however, typically challenged by complementary probes of the light mediator. Throughout our study, we implement an unbinned Monte Carlo analysis and use an improved energy reconstruction of the XENON1T events.