Predictive analytics can also be based on unsupervised learning which does not have a guiding hypothesis and uses an algorithm to seek patterns and structure in data and cluster them into groups or insights. A set of rules and processes are developed into a formula that undertakes calculations and is known as an algorithm. Predictive analytics is based on logic that is drawn from theories developed by humans to fit a hypothesis (supervised learning). These predictions offer a unique opportunity to see into the future and identify future trends in patient care both at an individual level and at a cohort scale. It is a discipline that utilises various techniques including modelling, data mining, and statistics, as well as artificial intelligence (AI) (such as machine learning) to evaluate historical and real-time data and make predictions about the future.
Predictive analytics can be described as a branch of advanced analytics that is utilised in the making of predictions about unknown future events or activities that lead to decisions. More specifically, this paper will look at the various ethical issues and moral hazards that need to be navigated following the adoption and use of predictive analytics in the health care sector with an emphasis on accountable algorithms. Government health agencies, doctors, and primary health givers need to be aware of the risks emerging and agree on levels of assurance as society continues to move into a new era of decision-making supplemented, and at times replaced, by evidence from digital technologies. However, the extension of this into new technologies such as the use of predictive analytics, the algorithms behind them, and the point where a machine process should be replaced by a human mental process is not clearly regulated or controlled by industry standards. Health care has a long track record of evidence-based clinical practice and ethical standards in research.
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