Prepared for: Agency for Healthcare Research and Quality, U.S. Department of Health and Human Services1, Contract No. 290-2007-10062-I, Prepared by: Southern California Evidence-based Practice Center, RAND Corporation, Santa Monica, CA
Hempel S, Shetty KD, Shekelle PG, Rubenstein LV, Danz MS, Johnsen B, Dalal SR. Machine Learning Methods in Systematic Reviews: Identifying Quality Improvement Intervention Evaluations. Research White Paper (Prepared by the Southern California Evidence-based Practice Center under Contract No. 290-2007-10062-I). AHRQ Publication No. 12-EHC125-EF. Rockville, MD: Agency for Healthcare Research and Quality. September 2012. www.effectivehealthcare.ahrq.gov/reports/final.cfm.
This report is based on research conducted by the Southern California Evidence-based Practice Center under contract to the Agency for Healthcare Research and Quality (AHRQ), Rockville, MD (Contract No. 290-2007-10062-I). The findings and conclusions in this document are those of the author(s), 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.
The information in this report is intended to help health care decisionmakers—patients and clinicians, health system leaders, and policymakers, among others—make well-informed decisions and thereby improve the quality of health care 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 may be used, in whole or in part, as the basis for development of clinical practice guidelines and other quality enhancement tools, or as a basis for reimbursement and coverage policies. AHRQ or U.S. Department of Health and Human Services endorsement of such derivative products may not be stated or implied.
None of the investigators have any affiliation or financial involvement that conflicts with the material presented in this report.
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