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Primer on machine learning: utilization of large data set analyses to individualize pain management
Parisa Rashidi1, David A Edwards2, Patrick J Tighe3
1J. Crayton Pruitt Family Department of Biomedical Engineering (BME), University of Florida (UF).
Current Opinion in Anaesthesiology
|August 14, 2019
Summary
Machine learning (ML) is becoming essential in pain research and clinical practice. Understanding ML principles and applications is crucial for its ethical and effective use in evidence-based medicine.
Area of Science:
- Pain research
- Machine learning applications
- Evidence-based medicine
Background:
- Machine learning (ML) algorithms are increasingly prevalent in pain research and clinical settings.
- Clinicians and researchers need to understand ML principles and applications.
Purpose of the Study:
- To summarize key machine learning principles.
- To review ML applications in pain research using structured and unstructured data.
Main Methods:
- Review of machine learning principles.
- Analysis of ML applications in pain research, including electronic health records, neuroimaging, and facial expression recognition.
Main Results:
- Machine learning and deep learning are key tools for analyzing electronic health record data in pain research.
- ML is increasingly used for neuroimaging and facial expression recognition in pain studies.
Conclusions:
- Machine learning will be a cornerstone of evidence-based medicine.
- Successful and ethical implementation of ML in research and clinics requires new skills and perspectives.
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