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Variations in clinical practice are common. However, some variations may cause undesired consequences. Careful consideration of their causes and effects is necessary to assure the quality of healthcare delivery. A modelling approach that could capture these aspects would help to achieve this goal. In this paper, a Newborn Life Support procedure is modelled. This activity is considered prone to error with reduced outcomes for the patient. Hence, it is necessary to understand the nature of the activity and its variations. A Coloured Petri Net (CPN) approach and a simulation technique are used for this purpose. The CPN colours are used to represent the characteristics of babies and to control the flow of tokens representing the resuscitation procedure. Probabilistic modelling aspects include the duration of individual tasks, the choice of treatment and the condition of the baby. The model outputs consist of the percentage of babies with an unsatisfactory outcome, the percentage of babies who need full resuscitation, and the duration of the procedure until a satisfactory condition is achieved. The modelling approach is demonstrated using a number of scenarios on some common NLS variations, relating to the maximum number of ventilation and the probability of errors in the inflation procedure.

Automatically recognising apparent emotions from face and voice is hard, in part because of various sources of uncertainty, including in the input data and the labels used in a machine learning framework. This paper introduces an uncertainty-aware multimodal fusion approach that quantifies modality-wise aleatoric or data uncertainty towards emotion prediction. We propose a novel fusion framework, in which latent distributions over unimodal temporal context are learned by constraining their variance. These variance constraints, Calibration and Ordinal Ranking, are designed such that the variance estimated for a modality can represent how informative the temporal context of that modality is w.r.t. emotion recognition. When well-calibrated, modality-wise uncertainty scores indicate how much their corresponding predictions are likely to differ from the ground truth labels. Well-ranked uncertainty scores allow the ordinal ranking of different frames across different modalities. To jointly impose both these constraints, we propose a softmax distributional matching loss. Our evaluation on AVEC 2019 CES, CMU-MOSEI, and IEMOCAP datasets shows that the proposed multimodal fusion method not only improves the generalisation performance of emotion recognition models and their predictive uncertainty estimates, but also makes the models robust to novel noise patterns encountered at test time.