Zero shot health trajectory prediction using transformer.

Integrating modern machine learning and clinical decision-making has great promise for mitigating healthcare's increasing cost and complexity. We introduce the Enhanced Transformer for Health Outcome Simulation (ETHOS), a novel application of the transformer deep-learning architecture for analyzing high-dimensional, heterogeneous, and episodic health data. ETHOS is trained using Patient Health Timelines (PHTs)-detailed, tokenized records of health events-to predict future health trajectories, leveraging a zero-shot learning approach.

Pedestrian behavior during evacuation from road tunnel in smoke condition-Empirical results.

Five evacuation experiments were performed in a road tunnel in order to test how pedestrians react when exposed to reduced visibility, how the decision making process is carried out, and finally what is the impact of various circumstances like: different level of smokiness, competitive behavior or learning effect on an evacuation process. In four experiments pedestrians were exposed to artificial, non-toxic smoke. During evacuation of a group of people gathered in low and moderate level of smokiness (when Cs < 0.

Velocity correlations and spatial dependencies between neighbors in a unidirectional flow of pedestrians.

The aim of the paper is an analysis of self-organization patterns observed in the unidirectional flow of pedestrians. On the basis of experimental data from Zhang et al. [J.