Extracting Factual Min/Max Age Information from Clinical Trial Studies
This work addresses the need for accurate age information extraction in clinical research, which is crucial for trial analysis and reporting, but it is incremental as it builds on existing QA methods for a specific domain task.
The paper tackled the problem of extracting minimum and maximum age values from clinical trial articles by using a neural network question-answering model trained on structured data from ClinicalTrials.gov, resulting in significant improvements over baseline systems when evaluated on 50 annotated research papers.
Population age information is an essential characteristic of clinical trials. In this paper, we focus on extracting minimum and maximum (min/max) age values for the study samples from clinical research articles. Specifically, we investigate the use of a neural network model for question answering to address this information extraction task. The min/max age QA model is trained on the massive structured clinical study records from ClinicalTrials.gov. For each article, based on multiple min and max age values extracted from the QA model, we predict both actual min/max age values for the study samples and filter out non-factual age expressions. Our system improves the results over (i) a passage retrieval based IE system and (ii) a CRF-based system by a large margin when evaluated on an annotated dataset consisting of 50 research papers on smoking cessation.