UC Irvine

There are numerous applications for deep learning in a healthcare setting including: providing more accurate diagnoses, recommending treatment plans, predicting patient outcomes, tracking patient engagement and adherence, and reducing the burden of administrative tasks. This plethora of applications has resulted in the widespread publication of deep learning algorithms applied to healthcare data. Despite numerous publications showing deep learning to be very successful in retrospective healthcare studies, very few of these algorithms are then actually incorporated into clinical practice. While there are many factors influencing the lack of algorithm deployment, one of the major reasons is a lack of trust in deep learning. This lack of trust stems in part from a lack of model interpretability and an inability to independently verify published results due to a lack of data availability. In this work, we explore generalized additive models with neural networks (GAM-NNs) as a method of improving model interpretability and we propose MOVER: Medical Informatics Operating Room Vi- tals and Events Repository a publicly available repository of medical data designed to improve visibility into deep learning algorithms in healthcare.

Similarly, deep learning can be used to analyze circadian omic (e.g. transcriptomic, metabolomic, proteomic) time series data. Several studies have shown that a disruption to circadian rhythms have been linked to health problems such as cancer, diabetes, obesity, and premature aging. In order to gain clinician trust in the conclusions drawn from circadian omic analyses we propose CircadiOmics: the largest annotated repository of circadian omic time series data analyzed using deep learning. Clinicians and researchers can use CircadiOmics to not only validate the findings of their circadian omic experiments, but also to analyze multiple circadian omic experiments in aggregate.