Automatic Summarization of Results from Clinical Trials
Abstract
A central concern in Evidence Based Medicine (EBM) is how to convey research results effectively to practitioners. One important idea is to summarize results by key summary statistics that describe the effectiveness (or lack thereof) of a given intervention, specifically the absolute risk reduction (ARR) and number needed to treat (NNT). Manual summarization is slow and expensive, thus, with the exponential growth of the biomedical research literature, automated solutions are needed. In this paper, we present a novel method for automatically creating EBM-oriented summaries from research abstracts of randomly-controlled trials (RCTs). The system extracts descriptions of the treatment groups and outcomes, as well as various associated quantities, and then calculates summary statistics. Results on a hand-annotated corpus of research abstracts show promising, and potentially useful, results.
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Data extraction systems have been created that can automatically retrieve unstructured information on the trial PICO elements and risk of bias.
“Data extraction systems have been created that can automatically retrieve unstructured information on the trial PICO elements,10–12 and risk of bias.13–16”
From Trialstreamer: A living, automatically updated database of clinical trial reports (2020) · cites this paper as [10]
Previous AI tools were incapable of extracting data deviating from a predetermined structure.
“or to extract data deviating from a predetermined structure (Summerscales et al., 2011; Wallace et al., 2016)”
From Can large language models replace humans in systematic reviews? Evaluating <scp>GPT</scp> ‐4's efficacy in screening and extracting data from peer‐reviewed and grey literature in multiple languages (2024) · cites this paper as Summerscales et al., 2011
Cites
An Assessment of Clinically Useful Measures of the Consequences of Treatment
Laupacis, A., Sackett, D. L., & Roberts, R. S. (1988) · New England Journal of Medicine · doi:10.1056/nejm198806303182605 · Cited by 1,681 · see citing works
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Systematic review automation technologies
Tsafnat, G., Glasziou, P., Choong, M. K., Dunn, A., Galgani, F., & Coiera, E. (2014) · Systematic Reviews · doi:10.1186/2046-4053-3-74 · Cited by 472 · see citing works
“We have identified only one study that attempted automatic extraction and synthesis of discrete distributions to NTT and RR formats [118].”
BackgroundMethodAutomating data extraction in systematic reviews: a systematic review
Jonnalagadda, S. R., Goyal, P., & Huffman, M. D. (2015) · Systematic Reviews · doi:10.1186/s13643-015-0066-7 · Cited by 287 · see citing works
Khraisha, Q., Put, S., Kappenberg, J., Warraitch, A., & Hadfield, K. (2023) · arXiv · doi:10.48550/arxiv.2310.17526 · Cited by 268 · see citing works
Khraisha, Q., Put, S., Kappenberg, J., Warraitch, A., & Hadfield, K. (2024) · Research Synthesis Methods · doi:10.1002/jrsm.1715 · Cited by 269 · see citing works
Trialstreamer: A living, automatically updated database of clinical trial reports
Marshall, I. J., Nye, B., Kuiper, J., Noel-Storr, A., Marshall, R., Maclean, R., Soboczenski, F., Nenkova, A., Thomas, J., & Wallace, B. C. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa163 · Cited by 88 · see citing works
Improving reference prioritisation with PICO recognition.
Brockmeier, A. J., Ju, M., Przybyła, P., & Ananiadou, S. (2019) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-019-0992-8 · Cited by 62 · see citing works
Automating Risk of Bias Assessment for Clinical Trials
Marshall, I. J., Kuiper, J., & Wallace, B. C. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2431314 · Cited by 59 · see citing works
Machine learning techniques for the automation of literature reviews and systematic reviews in EFSA
Jaspers, S., De Troyer, E., & Aerts, M. (2018) · EFSA Supporting Publications · doi:10.2903/sp.efsa.2018.en-1427 · Cited by 36 · see citing works
Stylianou, N., Razis, G., Goulis, D. G., & Vlahavas, I. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101949 · Cited by 22 · see citing works
A Neural Candidate-Selector Architecture for Automatic Structured Clinical Text Annotation.
Singh, G., Marshall, I. J., Thomas, J., Shawe-Taylor, J., & Wallace, B. C. (2017) · Proceedings of the 2017 ACM on Conference on Information and Knowledge Management · doi:10.1145/3132847.3132989 · Cited by 11 · see citing works
Sanchez-Graillet, O., Witte, C., Grimm, F., & Cimiano, P. (2022) · Journal of Biomedical Semantics · doi:10.1186/s13326-022-00271-7 · Cited by 9 · see citing works
Evaluating automated entity extraction with respect to drug and non-drug treatment strategies.
Guo, J., Blake, C., & Guan, Y. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103177 · Cited by 8 · see citing works
A Scoping Review of Adopted Information Extraction Methods for RCTs.
Aletaha, A., Nemati-Anaraki, L., Keshtkar, A., Sedghi, S., Keramatfar, A., & Korolyova, A. (2023) · Medical Journal of The Islamic Republic of Iran · doi:10.47176/mjiri.37.95 · Cited by 2 · see citing works
- Authors
- Summerscales, R. L., Argamon, S., Bai, S., Hupert, J., & Schwartz, A.
- Affiliations
- Illinois Institute of Technology (US)
- University of Illinois Chicago (US)
- Venue
- 2011 IEEE International Conference on Bioinformatics and Biomedicine
- Publisher
- IEEE
- Published
- 2011-11
- Pages
- 372-377
- OpenAlex
- W2053301680
- Cited by
- 49
- References
- 1
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 2 · 2023: 4 · 2022: 3 · 2021: 2 · 2020: 7 · 2019: 5 · 2018: 3 · 2017: 3 · 2016: 3 · 2015: 4 · 2014: 4 · 2013: 4
- FWCI
- 9.39
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (publishedVersion)
- Touro Scholar (Touro College) (submittedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- automatic summarization, clinical trial abstracts, evidence-based medicine, absolute risk reduction, number needed to treat, summary statistics, information extraction, treatment groups, outcomes, randomized controlled trials, biomedical literature
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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