Investigating deep-learning NLP for automating the extraction of oncology efficacy endpoints from scientific literature

• Published in: Intelligence-based medicine Vol. 10; p. 100152
• Main Authors: Gendrin-Brokmann, Aline, Harrison, Eden, Noveras, Julianne, Souliotis, Leonidas, Vince, Harris, Smit, Ines, Costa, Francisco, Milward, David, Dimitrievska, Sashka, Metcalfe, Paul, Louvet, Emilie
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 2024
• Subjects: Bidirectional encoder representations from transformers (BERT); Informatics; Information extraction (IE); Machine learning (ML); Natural language processing (NLP); Machine learning (ML); Natural language processing (NLP); Bidirectional encoder representations from transformers (BERT); Information extraction (IE); Information Extraction (IE); Machine Learning (ML); Bidirectional Encoder Representations from Transformers (BERT); Natural Language Processing (NLP)

Benchmarking drug efficacy is a critical step in clinical trial design and planning. The challenge is that much of the data on efficacy endpoints is stored in scientific papers in free text form, so extraction of such data is currently a largely manual task. Our objective is to automate this task as...