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A Sample Size Extractor for RCT Reports.
Lin, Fengyang; Liu, Hao; Moon, Paul; Weng, Chunhua.
  • Lin F; Department of Biomedical Informatics, Columbia University, New York, NY, United States.
  • Liu H; Department of Biomedical Informatics, Columbia University, New York, NY, United States.
  • Moon P; College of Physicians and Surgeons: Institute of Human Nutrition, Columbia University, New York, NY, United States.
  • Weng C; Department of Biomedical Informatics, Columbia University, New York, NY, United States.
Stud Health Technol Inform ; 290: 617-621, 2022 Jun 06.
Article in English | MEDLINE | ID: covidwho-1933568
ABSTRACT
Sample size is an important indicator of the power of randomized controlled trials (RCTs). In this paper, we designed a total sample size extractor using a combination of syntactic and machine learning methods, and evaluated it on 300 Covid-19 abstracts (Covid-Set) and 100 generic RCT abstracts (General-Set). To improve the performance, we applied transfer learning from a large public corpus of annotated abstracts. We achieved an average F1 score of 0.73 on the Covid-Set testing set, and 0.60 on the General-Set using exact matches. The F1 scores for loose matches on both datasets were over 0.74. Compared with the state-of-the-art tool, our extractor reports total sample sizes directly and improved F1 scores by at least 4% without transfer learning. We demonstrated that transfer learning improved the sample size extraction accuracy and minimized human labor on annotations.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Limits: Humans Language: English Journal: Stud Health Technol Inform Journal subject: Medical Informatics / Health Services Research Year: 2022 Document Type: Article Affiliation country: SHTI220151

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Limits: Humans Language: English Journal: Stud Health Technol Inform Journal subject: Medical Informatics / Health Services Research Year: 2022 Document Type: Article Affiliation country: SHTI220151