Cover of Machine Learning Methods in Systematic Reviews: Identifying Quality Improvement Intervention Evaluations

Machine Learning Methods in Systematic Reviews: Identifying Quality Improvement Intervention Evaluations

Research White Papers

Investigators: , PhD, , MD, , MD, PhD, , MD, , MD, , BA, and , PhD.

Southern California Evidence-based Practice Center, RAND Corporation
Rockville (MD): Agency for Healthcare Research and Quality (US); .
Report No.: 12-EHC125-EF
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Structured Abstract

Background:

Electronic searches typically yield far more citations than are relevant, and reviewers spend a substantial amount of time screening titles and abstracts to identify potential studies eligible for inclusion in a review. This is of particular relevance in complex research fields such as quality improvement. We tested a semiautomated literature screening process applied to the title and abstract screening stage of systematic reviews. A machine learning approach may allow literature reviewers to screen only a fraction of a search output and to use a predictive model to learn and then emulate the reviewers’ decisions. Once learned, the model can apply the selection process to an essentially unlimited number of citations.

Method:

Two independent literature reviewers screened 1,591 quasi-randomly selected citations in a training dataset used to predict decisions on the remaining citations in a MEDLINE search output of 9,395 citations. We explored different prediction algorithms and tested results against reference samples screened by experts in quality improvement. Qualitative (relevance cutoff determined in ROC curve) and quantitative predictions (probability rank order of citations) were determined.

Results:

The agreement between independent literature reviewers ranged from κ= 0.55 to 0.57. Across two reference samples, the predictive performance of the machine learning approach demonstrated 90.1 percent sensitivity, 43.9 percent specificity, and 32.1 percent PPV. This translates to a reduction of 36.1 percent in citation screening if applied. The predictive performance was affected by reviewer disagreements: a subgroup analysis restricted to citations both reviewers agreed on showed a sensitivity of 98.8 percent (specificity 43.9 percent).

Conclusion:

Machine learning approaches may assist in the title and abstract inclusion screening process in systematic reviews of complex, steadily expanding research fields such as quality improvement. Increased reviewer agreement appeared to be associated with improved predictive performance.

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

Suggested citation:

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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Bookshelf ID: NBK109711PMID: 23101052