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Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

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Related Experiment Videos

A pilot study using machine learning and domain knowledge to facilitate comparative effectiveness review updating.

Siddhartha R Dalal1, Paul G Shekelle1,2, Susanne Hempel1

  • 1Southern California Evidence-based Practice Center, RAND Corporation, Santa Monica, CA (SRD, PGS, SH, SJN, AM, KDS)

Medical Decision Making : an International Journal of the Society for Medical Decision Making
|September 11, 2012
PubMed
Summary

Automated screening models significantly reduce the workload for updating systematic reviews. Statistical classifiers predicted article relevance, cutting screening time by over 50% with minimal loss of key studies.

Related Experiment Videos

Area of Science:

  • Health Informatics
  • Medical Review Automation
  • Evidence Synthesis

Background:

  • Comparative effectiveness reviews and systematic reviews necessitate frequent and resource-intensive updates.
  • Efficiently updating these reviews is crucial for maintaining current medical knowledge.

Purpose of the Study:

  • To develop and evaluate statistical models for predicting article relevance in systematic review updates.
  • To reduce the manual screening workload associated with literature reviews.

Main Methods:

  • Collected 16,707 PubMed citation classifications from two comparative effectiveness reviews (low bone density and atypical antipsychotic drugs).
  • Developed and evaluated statistical classifiers, including GLMnet and gradient boosting machine (GBM), using MEDLINE indexing terms.
  • Assessed model performance based on sensitivity, positive predictive value (PPV), and screening workload reduction.

Main Results:

  • GLMnet-based models slightly outperformed GBM-based models.
  • High sensitivities (0.99-1.0) were achieved, reducing projected screening by 55.4% (AAP) and 63.2% (LBD).
  • The GLMnet model achieved sensitivities of 0.921 (AAP) and 0.905 (LBD) for efficacy/effectiveness articles.

Conclusions:

  • A pilot system using statistical classifiers reduced screening workload by over 50% for simulated review updates.
  • This approach minimizes the loss of relevant articles while significantly decreasing manual effort.
  • The system shows promise for streamlining the updating process of evidence-based medical literature.