| Autor: |
Fengyang, Lin, Hao, Liu, Paul, Moon, Chunhua, Weng |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Studies in health technology and informatics. 290 |
| ISSN: |
1879-8365 |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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