Structured Abstract

Contents

• Key Messages
• Preface
• Peer Reviewers
• Background
• Methods
  • Stage 1. Develop Optimized Search Strategies
  • Stage 2. Test Machine Learning Classifier on Optimized Searches
  • Stage 3. Test Machine Learning Classifier on Monthly Update Searches
• Results
  • Stage 1. Develop Optimized Search Strategies
  • Stage 2. Test Machine Learning Classifier on Optimized Searches
  • Stage 3. Test Machine Learning Classifier on Monthly Update Searches
• Discussion
  • Conclusion
• References
• Abbreviations and Acronyms
• Appendixes
  • Appendix A. Project Flow Diagram
  • Appendix B. Original and Text Word-Only Search Strategies
  • Appendix C. Cases

Suggested citation:

Chou R, Dana T, Shetty KD. Testing a Machine Learning Tool for Facilitating Living Systematic Reviews of Chronic Pain Treatments. Methods Research Report. (Prepared by the Pacific Northwest Evidence-based Practice Center under Contract No. 290-2015-00009-I and the Southern California Evidence-based Practice Center-RAND Corporation under Contract No. 290-2015-00010-I.) AHRQ Publication No. 21-EHC004. Rockville, MD: Agency for Healthcare Research and Quality. November 2020. Posted final reports are located on the Effective Health Care Program search page. DOI: 10.23970/AHRQEPCMETHTESTINGMACHINELEARNING.

This report is based on research conducted by the Pacific Northwest Evidence-based Practice Center (EPC) and the Southern California EPC-RAND Corporation under contract to the Agency for Healthcare Research and Quality (AHRQ), Rockville, MD (Contract Nos. 290-2015-00009-I and 290-2015-00010-I). The findings and conclusions in this document are those of the authors, who are responsible for its contents; the findings and conclusions do not necessarily represent the views of AHRQ. Therefore, no statement in this report should be construed as an official position of AHRQ or of the U.S. Department of Health and Human Services.

None of the investigators have any affiliations or financial involvement that conflicts with the material presented in this report.

The information in this report is intended to help healthcare decision makers—patients and clinicians, health system leaders, and policymakers, among others—make well-informed decisions and thereby improve the quality of healthcare services. This report is not intended to be a substitute for the application of clinical judgment. Anyone who makes decisions concerning the provision of clinical care should consider this report in the same way as any medical reference and in conjunction with all other pertinent information, i.e., in the context of available resources and circumstances presented by individual patients.

This report is made available to the public under the terms of a licensing agreement between the author and the Agency for Healthcare Research and Quality. This report may be used and reprinted without permission except those copyrighted materials that are clearly noted in the report. Further reproduction of those copyrighted materials is prohibited without the express permission of copyright holders.

AHRQ or U.S. Department of Health and Human Services endorsement of any derivative products that may be developed from this report, such as clinical practice guidelines, other quality enhancement tools, or reimbursement or coverage policies may not be stated or implied.

AHRQ appreciates appropriate acknowledgment and citation of its work. Suggested language for acknowledgment: This work was based on a methods research report, Testing a Machine Learning Tool for Facilitating Living Systematic Reviews of Chronic Pain Treatments, by the Evidence-based Practice Center Program at the Agency for Healthcare Research and Quality (AHRQ).