  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. Second, 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 systematically reviewed studies focused on advancing and using
deep neural networks to leverage patients structured time series data for
healthcare prediction tasks. 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.
