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Targeted learning with daily EHR data.

Oleg Sofrygin1,2, Zheng Zhu1, Julie A Schmittdiel1

  • 1Division of Research, Kaiser Permanente, Northern California, Oakland, California.

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|April 27, 2019
PubMed
Summary
This summary is machine-generated.

Analyzing electronic health records (EHR) with smaller time intervals improves causal inference for dynamic treatment rules. This study demonstrates a scalable targeted learning approach for granular EHR data analysis in diabetes research.

Keywords:
EHRTargeted Minimum Loss-Based Estimationbig datacausal inferencedynamic treatment regimesmachine learning

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Area of Science:

  • Biostatistics
  • Health Informatics
  • Causal Inference

Background:

  • Electronic health records (EHR) offer cost-effective cohort studies in real-world settings.
  • Analyzing granular EHR data presents computational challenges, often leading to coarser time interval analysis.
  • The impact of interval coarsening on EHR data analysis remains unevaluated.

Purpose of the Study:

  • To develop a scalable targeted learning approach for analyzing electronic health records (EHR) data at smaller time intervals.
  • To evaluate the practical effects of different interval coarsening strategies on causal inference from EHR data.
  • To address computational challenges in analyzing large-scale EHR data for time-dependent treatment effect estimation.

Main Methods:

  • Leveraged large-scale EHR data from a diabetes study.
  • Developed a scalable targeted learning approach, including a novel "long-format TMLE" implementation.
  • Applied semiparametric, doubly robust estimation (longitudinal Targeted Minimum Loss-Based Estimation - TMLE) to daily EHR data mapped into 90-, 30-, 15-, and 5-day intervals.
  • Utilized machine learning software (xgboost, h2o) for efficient nuisance parameter estimation.

Main Results:

  • Demonstrated a feasible and scalable targeted learning approach for granular EHR data analysis.
  • Showcased the practical impact of varying interval coarseness on causal effect estimations of dynamic treatment rules.
  • The proposed "long-format TMLE" effectively overcomes computational hurdles in large-scale EHR data analysis.

Conclusions:

  • Granular analysis of EHR data using scalable targeted learning methods is feasible and impactful.
  • The choice of time interval significantly influences causal inference from EHR data.
  • This research provides a robust methodology for analyzing complex, time-varying treatment effects in large EHR datasets.