Big Data Cohort Extraction to Facilitate Machine Learning to Improve Statin Treatment
Chih-Lin Chi1,2, Jin Wang1,2, Thomas R Clancy1,2
11 University of Minnesota, Minneapolis, MN, USA.
Western Journal of Nursing Research
|September 14, 2018
Summary
Domain experts are crucial for selecting patient data cohorts in Big Data studies. This ensures machine learning models effectively support clinical practice, like preventing statin adverse events.
Area of Science:
- Health Informatics
- Machine Learning Applications
Background:
- Big Data in healthcare offers potential for clinical practice improvement.
- Machine learning (ML) is a key analytic tool for data-driven decision support across industries.
Purpose of the Study:
- To highlight the critical role of domain experts in Big Data analysis.
- To demonstrate a method for extracting a relevant patient cohort for ML model development.
Main Methods:
- Utilized Big Data from the OptumLabs™ Data Warehouse (130 million de-identified lives).
- Employed domain expertise to define criteria for patient data cohort selection.
- Extracted a specific patient cohort of 37,506 individuals for ML analysis.
Main Results:
- Feature-selection tools alone are insufficient for creating effective patient data cohorts.
- Domain expert input is essential for maximizing the success of ML projects in healthcare.
Conclusions:
- Domain experts are vital for identifying valuable data elements and defining patient cohorts in Big Data studies.
- This process is key to developing successful machine learning strategies for clinical practice improvement.
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