A Value of Data Science in the Medical Informatics: An Overview

Abstract

The use of data science and predictive modelling for real-time clinical decision making is increasingly recognized. The initial step in the path towards the adoption of real-time prediction and forecast is the creation and evaluation of predictive models for clinical practice. Training in medical informatics is not only necessary for medical students but also for all medical personnel at all technical levels of education. A critical move required for the learning and application of clinical medicine is to incorporate medical informatics into the broad scope of medical informatics. Current major fields of research can be categorized according to the organization, implementation, assessment, representation, and interpretation of medical information. We should expect many changes in medical informatics, because of many of the driving forces behind advancement in information management methods and their innovations, developments in medicine and health care, and the constantly evolving needs, requirements and aspirations of human societies. Data science and predictive analytics offer distinct methodologies for tapping vast data sets of medical knowledge from intelligence. These approaches have many possibilities, such as identifying patterns, forecasting outcomes, and optimizing algorithms better. But medical data collection and management often faces few problems, such as data size, data consistency, durability and data completeness. This research offers an extensive overview of medical data processing, predictive analytics and data science in order to contribute to the area of medical informatics and data science. It offers explanations of basic principles using data science in the evolving field of medical informatics. Also the research includes review of benefits, applications and future of data science in healthcare.KeywordsData scienceMedical informaticsPredictive modellingHealthcare