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Activity Number:
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100
- Optimizing Medical Decision Making with Real World Evidence
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Type:
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Invited
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Date/Time:
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Monday, July 30, 2018 : 8:30 AM to 10:20 AM
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Sponsor:
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ENAR
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Abstract #326507
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Title:
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Learning Individualized Treatment Rules from Electronic Health Records Data
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Author(s):
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Yuanjia Wang*
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Companies:
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Columbia University
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Keywords:
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Personalized Medicine;
Machine Learning;
Subgroup analysis;
Real World Evidence
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Abstract:
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Current guidelines for treatment decision making largely rely on data from randomized controlled trials (RCT) studying average treatment effects. They may be inadequate to make real-world individualized treatment decisions in real-world settings due to stringent inclusion/exclusion criteria of RCT and a lack of evidence for long-term outcomes. Large-scale electronic health records (EHR) data provide unprecedented opportunities to fulfill the goals of personalized medicine and learn individualized treatment rule (ITR) depending on patient-specific characteristics. In this talk, we propose a new machine learning approach to estimate ITR which accommodates challenges in EHR. The advantages include improved robustness and flexibility to accommodate complex patterns among features collected in EHR databases. The methods are applied to learn the optimal ITR for second-line treatments of type 2 diabetes using patient records from a large EHR database.
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Authors who are presenting talks have a * after their name.
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