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Decaying Relevance of Clinical Data when Predicting Future Decisions
Presenter: Muthu Alagappan, MD Jonathan Chen, MD, PhD Mary K Goldstein, MD, MS Steven M Asch MD, MPH Russ B Altman MD, PhD March 30th, 2017 Joint Summits on Translational Science General Medicine Disciplines, Department of Medicine, Stanford University
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Disclosures I do not have any relevant financial relationships or conflicts of interest
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Objectives To develop a clinical order recommender system
To determine how varying historical training data affects prediction To estimate the “decay rate” (i.e. relevance) of clinical data
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Randomized Clinical Trials (RCTs)
Knowledge Scientific inquiry Randomized Clinical Trials (RCTs) Clinical Orders Patient Care Expert Opinion Anecdotal Experience
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Clinical Decision Support - Order Sets
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Clinical Decision Support - A bottom-up approach
Collaborative filter Doctor Collective Medical Record
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Data Description
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Patient Timeline Model
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Example: Chest Pain (ICD9: 786.5)
Top orders following an admission diagnosis of “Chest Pain” Co-occurrences within 24 hours, ranked by Odds Ratio (OR)
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Internal Validation Use initial data as query
Predict subsequent orders Compare against next actual 24 hour orders Identify real order set use
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Prediction Accuracy
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Add more data? “…invariably, simple models and a lot of data trump more elaborate models based on less data.” “…use available large-scale data rather than hoping for annotated data that isn’t available.”
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Is More Data Better? Prediction is difficult
Clinical knowledge evolves quickly Recent data > More data
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Decaying Relevance of Clinical Data
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Objectives To develop a clinical order recommender system
To determine how varying historical training data affects prediction To estimate the “decay rate” (i.e. relevance) of clinical date
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Acknowledgements Mentor Jonathan Chen, MD, PhD
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