Traditional machine learning methods face two main challenges in dealing with healthcare predictive analytics tasks. First, the high-dimensional nature of healthcare data needs labor-intensive and time-consuming processes to select an appropriate set of features for each new task. Secondly, these methods depend on feature engineering to capture the sequential nature of patient data, which may not adequately leverage the temporal patterns of the medical events and their dependencies. Recent deep learning methods have shown promising performance for various healthcare prediction tasks by addressing the high-dimensional and temporal challenges of medical data. These methods can learn useful representations of key factors (e.g., medical concepts or patients) and their interactions from high-dimensional raw (or minimally-processed) healthcare data. In this paper we systemically reviewed studies focused on using deep learning as the prediction model to leverage patient time series data for a healthcare prediction task from methodological perspective. To identify relevant studies, MEDLINE, IEEE, Scopus and ACM digital library were searched for studies published up to February 7th 2021. We found that researchers have contributed to deep time series prediction literature in ten research streams: deep learning models, missing value handling, irregularity handling, patient representation, static data inclusion, attention mechanisms, interpretation, incorporating medical ontologies, learning strategies, and scalability. This study summarizes research insights from these literature streams, identifies several critical research gaps, and suggests future research opportunities for deep learning in patient time series data.
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