For example, the United States is exploring the potential to use of one of its largest healthcare systems (the Military Healthcare System) to provide healthcare to eligible veterans in order to potentially benefit > 9 million eligible personnel. Currently, this trend is shifting from civilian medicine to military medicine. Across the medical industry, various types of medical data are generated at a high speed, and trends indicate that applying big data in the medical field helps improve the quality of medical care and optimizes medical processes and management strategies. Generally, big data refers to a dataset that exceeds the scope of a simple database and data-processing architecture used in the early days of computing and is characterized by high-volume and -dimensional data that is rapidly updated represents a phenomenon or feature that has emerged in the digital age. In the field of computer science, big data refers to a dataset that cannot be perceived, acquired, managed, processed, or served within a tolerable time by using traditional IT and software and hardware tools. “Big data” as an abstract concept currently affects all walks of life, and although its importance has been recognized, its definition varies slightly from field to field. With the rapid development of computer software/hardware and internet technology, the amount of data has increased at an amazing speed. The goal of this work was to aid clinical researchers in gaining a clear and intuitive understanding of the application of data-mining technology on clinical big-data in order to promote the production of research results that are beneficial to doctors and patients. Additionally, we described data-mining methods along with their practical applications. This article introduced the main medical public database and described the steps, tasks, and models of data mining in simple language. Therefore, data mining has unique advantages in clinical big-data research, especially in large-scale medical public databases. Data-mining technology has been a frontier field in medical research, as it demonstrates excellent performance in evaluating patient risks and assisting clinical decision-making in building disease-prediction models. Many high quality studies have emerged from public databases, such as Surveillance, Epidemiology, and End Results (SEER), National Health and Nutrition Examination Survey (NHANES), The Cancer Genome Atlas (TCGA), and Medical Information Mart for Intensive Care (MIMIC) however, these data are often characterized by a high degree of dimensional heterogeneity, timeliness, scarcity, irregularity, and other characteristics, resulting in the value of these data not being fully utilized.
0 Comments
Leave a Reply. |