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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Analyzing adverse drug reaction using statistical and machine learning methods: A systematic review.

Hae Reong Kim1, MinDong Sung1, Ji Ae Park1

  • 1Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, South Korea.

Medicine
|June 27, 2022
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Summary
This summary is machine-generated.

This review identifies key statistical and machine learning methods for detecting adverse drug reactions (ADRs). It guides database selection and analysis techniques for drug safety research.

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Area of Science:

  • Pharmacovigilance and Drug Safety
  • Data Science in Healthcare
  • Computational Statistics

Background:

  • Adverse drug reactions (ADRs) are unintended, negative drug responses requiring robust detection methods.
  • Identifying associations between drugs and ADRs is critical for patient safety and effective therapeutics.
  • Various statistical and machine learning approaches have been developed to detect ADR signals.

Purpose of the Study:

  • To systematically review and examine analytical tools used for detecting ADRs.
  • To assess original research articles employing statistical and machine learning methods for ADR detection.
  • To provide guidelines on database utilization and analysis method selection for ADR research.

Main Methods:

  • A systematic literature review was performed for articles published between 2015 and 2020.
  • Keywords included statistical, machine learning, and deep learning methods for ADR signal detection.
  • The review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.

Main Results:

  • 72 articles were reviewed: 51 focused on statistical methods, and 21 on machine learning.
  • Regression analysis was exclusively used for Electronic Medical Record (EMR) data.
  • Disproportionality methods were preferred for FDA Adverse Event Reporting System (FAERS) data; DrugBank was common for machine learning.
  • Supervised methods were the second most frequent approach.

Conclusions:

  • The review offers guidance on frequently used databases and applicable analysis methods for ADR detection.
  • Over 90% of statistical analyses utilized disproportionality or regression methods with Spontaneous Reporting System (SRS) or EMR data.
  • Machine learning research showed a tendency to analyze diverse data combinations, with k-nearest neighbor being prominent.