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17 mar 2021 · Data mining, a subfield of artificial intelligence that makes use of vast amounts of data in order to allow significant information to be extracted through previously unknown patterns, has been progressively applied in healthcare to assist clinical diagnoses and disease predictions [2].
25 maj 2020 · Data mining is defined as a set of rules, processes, algorithms that are designed to generate actionable insights, extract patterns, and identify relationships from large datasets (Morabito, 2016). Data mining incorporates automated data extraction, processing, and modeling by means of a range of methods and techniques.
20 lut 2024 · Clinical data mining of predictive models offers significant advantages for re-evaluating and leveraging large amounts of complex clinical real-world data and experimental comparison data for tasks such as risk stratification, diagnosis, classification, and survival prediction.
23 maj 2018 · Twenty-five of the articles we reviewed focus on the theoretical aspects of the application of data mining in healthcare including designing the database framework, data collection, and management to algorithmic development.
11 sie 2021 · This article introduced the main medical public database and described the steps, tasks, and models of data mining in simple language. Additionally, we described data-mining methods along with their practical applications.
3 maj 2011 · 1 Mention. Explore all metrics. Abstract. As a new concept that emerged in the middle of 1990’s, data mining can help researchers gain both novel and deep insights and can facilitate unprecedented understanding of large biomedical datasets.
This systematic literature review aims to examine the techniques of data mining has been applied in healthcare management systems. The research method used in this study is structured according to PRISMA guidelines.
