One challenge is that the proposed clinical requirements and data mining are not synchronized. Additionally, the exotic predictions of data mining are difficult to apply directly in local medical institutions.
Feb 20, 2024
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Our results show that the limitations of data mining in healthcare include reliability of medical data, data sharing between healthcare organizations, ...
Mar 31, 2023 · Data mining in the healthcare industry refers to identifying patterns and trends in analyzed data to help the healthcare decision-making process.
Aug 11, 2021 · This article introduced the main medical public database and described the steps, tasks, and models of data mining in simple language.
Medical big data analyses are complicated by many technical issues, such as missing values, curse of dimensionality, and bias control.
Abstract: The principle of any retrospective on patient data-based investigation is searching the patients by problem or sign, but not by name.
Jun 22, 2024 · Incomplete or invalid data: Poor data quality can spoil data-mining results. There are many variations of this issue. Data fields in patient ...
Abstract. The principle of any retrospective on patient data based investigation is searching the patients by problem or sign, but no name.
In clinical datasets, data scarcity/sparsity often conspires with data imbalance. Medical studies often focus on rare or less frequent cases (fewer ill than ...
Dec 10, 2021 · Data mining in healthcare is a very real and effective practice. It helps optimize costs, improve patient outcomes, and prevent fraud.