An overview of deep learning in healthcare

In recent years, a rising expectation has been built around the analysis of huge quantities of data often accessible in companies, which has been examined by academics and effectively utilized by business. Big Data and Deep Learning are two of the most often used phrases in scientific circles nowadays. In this two-part study, we want to shed some light on the present status of these two distinct but connected areas of Data Science in order to better understand the current state and future development of healthcare. We begin by providing a basic review of the technical components of Big Data technologies, as well as an overview of the elements of Deep Learning methods as described in scientific literature. Then we focus on the application areas that may be claimed to have produced meaningful real-world success stories, with examples ranging from major technology firms to financial institutions, among others. The academic effort that has gone into bringing these technologies to the healthcare sector is then summarized and analyzed in two ways: first, the landscape of application examples is globally scrutinized according to the varying nature of medical data, including data forms in electronic health records, medical time signals, and medical images; and second, a specific application example is scrutinized according to the varying nature of medical data. The freely accessible MIMIC dataset includes a collection of toy application examples designed to assist novices get started with some principled, simple, and structured content and code. Current and future obstacles to the employment of both sets of methods in our future healthcare are discussed critically.