Submitted by yip4002 on August 20, 2025 - 4:06am
Title | EvidenceOutcomes: A Dataset of Clinical Trial Publications with Clinically Meaningful Outcomes. |
Publication Type | Journal Article |
Year of Publication | 2025 |
Authors | Zhou Y, Newbury AM, Zhang G, Idnay BRoss, Liu H, Weng C, Peng Y |
Journal | Stud Health Technol Inform |
Volume | 329 |
Pagination | 723-727 |
Date Published | 2025 Aug 07 |
ISSN | 1879-8365 |
Keywords | Clinical Trials as Topic, Data Mining, Evidence-Based Medicine, Humans, Machine Learning, Natural Language Processing, Outcome Assessment, Health Care |
Abstract | The fundamental process of evidence extraction in evidence-based medicine relies on identifying PICO elements, with Outcomes being the most complex and often overlooked. To address this, we introduce EvidenceOutcomes, a large annotated corpus of clinically meaningful outcomes. A robust annotation guideline was developed in collaboration with clinicians and NLP experts, and three annotators annotated the Results and Conclusions of 500 PubMed abstracts and 140 EBM-NLP abstracts, achieving an inter-rater agreement of 0.76. A fine-tuned PubMedBERT model achieved F1 scores of 0.69 (entity level) and 0.76 (token level). EvidenceOutcomes offers a benchmark for advancing machine learning algorithms in extracting clinically meaningful outcomes. |
DOI | 10.3233/SHTI250935 |
Alternate Journal | Stud Health Technol Inform |
PubMed ID | 40775953 |