Python for healthcare analytics made simple

Abstract

Machine learning (ML) is found to be an element of healthcare industries for the past two decades after it was initially implemented for monitoring the antibiotic doses for patients who suffer from various infections. Nowadays, the volume of electronic healthcare records (EHR) gets increased, and thus, it leads to the huge massive of genetic sequential data which in turn directs the healthcare's importance in ML. A brief discussion about the common data sources in healthcare would be explained in detail. From various inferences, it is found that Python is a go-to language for developers, and it is extensively used in several fields. This chapter gives a brief description of the characteristics of data and the significance of the data quality in healthcare. Data has to be extracted from various sources for better analysis. Hence, the chapter puts forth an overview of challenges that must be resolved, and various types of extraction tools would be discussed. To describe the perceptions that are obtained through the analysis of the large data sets, data visualization is immensely used. In this world of healthcare, data analytics is huge, and it could include an extensive variety of organizations and other uses cases such as emergency rooms (ER), intensive care units, hospitals, and medical equipment manufacturers. The function of data visualization in the data science process flow could be discussed in detail in addition to the several techniques used to denote the complex data. It also covers advanced visualization techniques that emphasize grid, wordcloud, heatmap, and geospatial. Data analytics is a buzzword that refers to the diversified types of analysis. Perception is required since more information is required by the users for an extensive analysis. Certain techniques for the management of the data and analysis need more efforts and continuous attention particularly for data aggregation, data capture, real-time data streaming, analytics, and other visualization solutions, so that the integration could be done further for the improved utilization of electronic medical record (EMR) with the healthcare. Finally, this chapter briefs about the challenges that could be identified with the vast amount of data composed as EMR.