A State of Art Approaches on Deep Learning Models in Healthcare: An Application Perspective

Abstract

Acquisition of knowledge and actionable insights from complex, high-dimensional and nonhomogeneous healthcare data still remains a major difficulty in the evolving health care applications. Different data types have been emerged in the advanced healthcare research area such as maintaining patient's records, imaging, sensors data and content that are not simple, nonhomogeneous, badly annotated and normally not structured well. Conventional data mining and machine learning methods has been executing feature engineering to attain efficient and highly robust features from the data, and then constructs a model to predict or cluster data. Several difficulties exist in the situation of complex information and insufficient domain information. The recent advancements in the Deep Learning (DL) models offer novel and efficient end to end frameworks for health care data. In this study, we attempt to survey the recently presented DL models in the advanced medicinal filed in various aspects.