Attention mechanisms for physiological signal deep learning: which attention should we take?

Attention mechanisms are widely used to dramatically improve deep learning model performance in various fields. However, their general ability to improve the performance of physiological signal deep learning model is immature. In this study, we experimentally analyze four attention mechanisms (e.g., squeeze-and-excitation, non-local, convolutional block attention module, and multi-head self-attention) and three convolutional neural network (CNN) architectures (e.g., VGG, ResNet, and Inception) for two representative physiological signal prediction tasks: the classification for predicting hypotension and the regression for predicting cardiac output (CO). We evaluated multiple combinations for performance and convergence of physiological signal deep learning model. Accordingly, the CNN models with the spatial attention mechanism showed the best performance in the classification problem, whereas the channel attention mechanism achieved the lowest error in the regression problem. Moreover, the performance and convergence of the CNN models with attention mechanisms were better than stand-alone self-attention models in both problems. Hence, we verified that convolutional operation and attention mechanisms are complementary and provide faster convergence time, despite the stand-alone self-attention models requiring fewer parameters.

Developing and validating multi-modal models for mortality prediction in COVID-19 patients: a multi-center retrospective study

The unprecedented global crisis brought about by the COVID-19 pandemic has sparked numerous efforts to create predictive models for the detection and prognostication of SARS-CoV-2 infections with the goal of helping health systems allocate resources. Machine learning models, in particular, hold promise for their ability to leverage patient clinical information and medical images for prediction. However, most of the published COVID-19 prediction models thus far have little clinical utility due to methodological flaws and lack of appropriate validation. In this paper, we describe our methodology to develop and validate multi-modal models for COVID-19 mortality prediction using multi-center patient data. The models for COVID-19 mortality prediction were developed using retrospective data from Madrid, Spain (N=2547) and were externally validated in patient cohorts from a community hospital in New Jersey, USA (N=242) and an academic center in Seoul, Republic of Korea (N=336). The models we developed performed differently across various clinical settings, underscoring the need for a guided strategy when employing machine learning for clinical decision-making. We demonstrated that using features from both the structured electronic health records and chest X-ray imaging data resulted in better 30-day-mortality prediction performance across all three datasets (areas under the receiver operating characteristic curves: 0.85 (95% confidence interval: 0.83-0.87), 0.76 (0.70-0.82), and 0.95 (0.92-0.98)). We discuss the rationale for the decisions made at every step in developing the models and have made our code available to the research community. We employed the best machine learning practices for clinical model development. Our goal is to create a toolkit that would assist investigators and organizations in building multi-modal models for prediction, classification and/or optimization.