![]() ![]() Unfortunately, many of these applications resulted in inaccurate or irrelevant research results, as proper research protocols were not fully followed. In the healthcare industry, these techniques are often applied to a variety of healthcare claims and electronic health records data to garner valuable insights into diagnostic and treatment pathways in order to help optimize patient healthcare access and treatment process. In the recent years, machine learning techniques have been utilized to solve problems at hand across multitudes of industries and topics. Understanding of the best suited methodologies can help the healthcare industry to develop and apply advanced analytical tools to speed up the diagnostic and treatment processes as well as improve the quality of life for their patients. Furthermore, the chapter will also provide examples of applications in the healthcare industry and outline how to decide which approach is best when analyzing healthcare data. Due to the importance and relevance of the subject in the current analytics environment, this chapter will present an overview of each approach as well as outline their similarities and differences to provide the needed understanding when selecting the proper technique for problems at hand. Experienced analysts and data scientists are often well versed in both types of approaches and their applications, hence use best suited tools for their analytical challenges. ![]() Determining the best approach should consider the problem to be solved, empirical evidence and resulting hypothesis, data sources and their completeness, number of variables/data elements, assumptions, and expected outcomes such as the need for predictions or causality and reasoning. Overall machine learning and statistical modeling are complementary techniques that are guided on similar mathematical principles, but leverage different tools to arrive at insights. The decision on which approach to choose is often based on the problem at hand, expected outcome(s), real world application of the results and insights, as well as the availability and granularity of data for the analysis. However, machine learning and statistical modeling are actually more closely related to each other rather than being on different sides of an analysis battleground. Data scientists and statisticians are often at odds when determining the best approaches and choosing between machine learning and statistical modeling to solve their analytical challenges and problem statements across industries.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |