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Big Data Cohort Extraction for Personalized Statin Treatment and Machine Learning

  • Terrence J. AdamEmail author
  • Chih-Lin Chi
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1939)

Abstract

The creation of big clinical data cohorts for machine learning and data analysis require a number of steps from the beginning to successful completion. Similar to data set preprocessing in other fields, there is an initial need to complete data quality evaluation; however, with large heterogeneous clinical data sets, it is important to standardize the data in order to facilitate dimensionality reduction. This is particularly important for clinical data sets including medications as a core data component due to the complexity of coded medication data. Data integration at the individual subject level is essential with medication-related machine learning applications since it can be difficult to accurately identify drug exposures, therapeutic effects, and adverse drug events without having high-quality data integration of insurance, medication, and medical data. Successful data integration and standardization efforts can substantially improve the ability to identify and replicate personalized treatment pathways to optimize drug therapy.

Key words

Medication safety Clinical data integration Clinical comorbidity evaluation Personalized medication therapy 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Pharmaceutical Care and Health Systems, Health Informatics, Social and Administrative PharmacyUniversity of Minnesota College of PharmacyMinneapolisUSA
  2. 2.University of Minnesota School of NursingMinneapolisUSA

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