Integrating data science into clinical and medical research- Special Issue

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This is the age of big data sets and conclusions drawn from big data analytics. As a result, data science now plays a pivotal role in advancing clinical and medical research, revolutionizing the way we understand diseases, treatments, and patient outcomes. Advances in computational biology and bioinformatics have been essential in moving forward the applied life sciences of clinical medicine. For example, big data analytics have enabled clinicians to gain heretofore unknown insights from vast amounts of health-related data, particularly on previously understudied populations. Data transformation can reveal hidden aspects of medical systems and can elevate our health intelligence quota by increasing our abilities to predict disorder outcomes and forecast disease processes. Leveraging detailed data on a large number of patients provides researchers with insights on the nuances of human biodiversity which can aid in effective clinical decision-making. The era of big data has provided us with practical models derived from scientific databases that are both extensive and replicable. These, in turn drive advancements in medical research and development, bring closer together applied scientists (e.g., clinicians) and basic scientists.

In this special issue of Advances in Clinical and Medical Research, we solicit papers that explore diverse data mining techniques that generate data that can impact clinical decision-making. We encourage papers that address the impact of big data from large-scale medical public and semi-public databases on disease-prediction models. We have observed that the use of AI technologies can generate increasingly sophisticated quantities of medical data, detect diseases, and propose interventions that result in improved patient care, and we encourage papers along these lines as well. Finally, we hope that this special issue will attract papers on new biostatistical methods that can begin to account for variability in patients’ responses to treatment and get us closer to the goal of precision medicine for all. 

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