Unmasking the Chameleons: A Benchmark for Out-of-Distribution Detection in Medical Tabular Data

Despite their success, Machine Learning (ML) models do not generalize effectively to data not originating from the training distribution. To reliably employ ML models in real-world healthcare systems and avoid inaccurate predictions on out-of-distribution (OOD) data, it is crucial to detect OOD samples. Numerous OOD detection approaches have been suggested in other fields - especially in computer vision - but it remains unclear whether the challenge is resolved when dealing with medical tabular data. To answer this pressing need, we propose an extensive reproducible benchmark to compare different methods across a suite of tests including both near and far OODs. Our benchmark leverages the latest versions of eICU and MIMIC-IV, two public datasets encompassing tens of thousands of ICU patients in several hospitals. We consider a wide array of density-based methods and SOTA post-hoc detectors across diverse predictive architectures, including MLP, ResNet, and Transformer. Our findings show that i) the problem appears to be solved for far-OODs, but remains open for near-OODs; ii) post-hoc methods alone perform poorly, but improve substantially when coupled with distance-based mechanisms; iii) the transformer architecture is far less overconfident compared to MLP and ResNet

Digging deeper into decision-making of Chinese long-haul outbound tourists: a two-stage preference estimation approach

Investigating and understanding tourists’ preferences is of great importance for both decision-making theories and destination marketing practice. In the current study we investigate the process of consideration-set formation together with conjoint analysis to estimate destination preferences of Chinese long-haul outbound tourists. Through the integration of choice-set and characteristic theories, this study explores how to obtain more comprehensive insights into destination choice processes. The findings show that preferences can be analysed effectively in a two-stage model, which can reveal detailed additional insights regarding tourists’ preferences towards destination attributes, thus contributing to marketing insight on destination choice and selection

Gold and iodine diffusion in large area perovskite solar cells under illumination.

Operational stability is the main issue hindering the commercialisation of perovskite solar cells. Here, a long term light soaking test was performed on large area hybrid halide perovskite solar cells to investigate the morphological and chemical changes associated with the degradation of photovoltaic performance occurring within the devices. Using Scanning Transmission Electron Microscopy (STEM) in conjunction with EDX analysis on device cross sections, we observe the formation of gold clusters in the perovskite active layer as well as in the TiO2 mesoporous layer, and a severe degradation of the perovskite due to iodine migration into the hole transporter. All these phenomena are associated with a drastic drop of all the photovoltaic parameters. The use of advanced electron microscopy techniques and data processing provides new insights on the degradation pathways, directly correlating the nanoscale structure and chemistry to the macroscopic properties of hybrid perovskite devices.European Research Council (291522), European Research Council (259619