Healthcare Information Platform in AI Era

Healthcare analytics has attracted increasing research interests as electronic health records (EHR) and medical image data have skyrocketed over the past decade [1]. EHR and lab reports contain rich text, visual, and time series information such as a patient’s medical and diagnosis history, radiology images, etc which is the major source for managing and predicting a patient’s health status. Meanwhile, Deep Learning [2] has greatly pushed forward the research frontier of computer vision, speech recognition, and natural language processing, since its big success in ImageNet 2012 competition [3]. There is an increasing interest in applying state-of-the-art deep learning techniques to the healthcare industry from the combined effort of industry and academia. IBM [4, 5], Amazon [6, 7], and Google [77, 9, 10, 11] all have pushed out their healthcare information services that can provide early symptoms warning, diagnostic support, and help make clinical decisions. Medical schools and healthcare institutes also have conducted extensive research on illness detection, physiological signals classification, mortality early warning detection, Intensive Care Unit(ICU) length of stay prediction, etc with deep learning models [12, 13, 14, 15, 16, 17]. In this dissertation, we will present two use cases of applying the recent progress of Deep Learning to the healthcare domain: (1) Faster Healthcare Time Series Classification with Convolutional Feature Engineering, and (2) Deep Healthcare Pre-Trained Language Models on Mobile Devices. Our work not only has generated several top tier conference papers, but will also lay the foundation for the next generation healthcare information platform development in the US.