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1. Define data mining in the context of advancing
healthcare. Why is data mining considered crucial for
improving patient care? (Answer: Data mining is the
process of extracting patterns and actionable insights from
large sets of electronic health records or other healthcare
databases. It is essential for advancing healthcare as it
allows for identifying trends, predicting outcomes, and
personalizing treatment plans based on evidence-based
medicine.)
2. Explain the concept of predictive modeling in healthcare
data mining. How can predictive models help healthcare
professionals make informed decisions? (Answer:
Predictive modeling involves using historical data to build
a model that can predict future outcomes or events. In
healthcare, these models can help clinicians identify
patients at risk of specific conditions or adverse events,
enabling early interventions and targeted care.)
3. Discuss the role of data cleansing and preprocessing in
health data mining. Why is it critical to ensure the accuracy
and quality of the data before mining? (Answer: Data
cleansing and preprocessing involves removing irrelevant
or inaccurate data, standardizing formats, and handling
missing values. Ensuring data accuracy is paramount in
healthcare as incorrect or incomplete data can lead to
erroneous conclusions and potentially compromise patient
safety.)
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