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Updated: Mar 27, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Machine learning for medication error detection: a scoping review protocol.

Félicien Hêche¹, Anthony Yazdani², Sohrab Ferdowsi²

¹Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland felicien.heche@unige.ch.

|March 25, 2026

Summary

This summary is machine-generated.

Machine learning (ML) shows promise for detecting and predicting medication errors. This review maps ML applications in healthcare to improve medication safety and identify future research directions.

Keywords:

Artificial Intelligence Clinical Decision-Making Health & safety Health informatics Machine Learning Risk Assessment

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Last Updated: Mar 27, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Published on: January 11, 2020

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Published on: May 15, 2020

Area of Science:

Healthcare Informatics
Artificial Intelligence in Medicine
Patient Safety

Background:

Medication errors remain a significant public health concern despite existing interventions.
Machine learning (ML) offers a data-driven approach to enhance medication error detection and prediction.
Existing solutions require more effective and scalable methods to address persistent medication errors.

Purpose of the Study:

To systematically review and map the literature on ML-based approaches for medication error prediction and detection.
To identify the scope of ML applications across the medication use process.
To characterize methodological trends and pinpoint knowledge gaps in ML for medication safety.

Main Methods:

Adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guideline.
Comprehensive literature searches in PubMed, Embase, and Web of Science from January 2015 to April 2025.
Extraction and analysis of data on ML models, data sources, evaluation methods, and clinical contexts using descriptive statistics, visualizations, thematic analysis, and narrative synthesis.

Main Results:

The review will identify and categorize various ML models and techniques applied to medication error detection and prediction.
Analysis will reveal trends in data sources, evaluation metrics, and clinical settings where ML is utilized.
Key knowledge gaps and limitations in the current research landscape will be highlighted.

Conclusions:

ML presents a significant opportunity to advance medication safety through improved error detection and prediction.
This scoping review will provide a structured overview to guide future research and clinical implementation of ML in medication safety.
Findings will support the development of data-informed strategies for safer medication practices.