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Extracting Factual Min/Max Age Information from Clinical Trial Studies
lib:cdd88224088a6ca8 (v1.0.0)
Authors: Yufang Hou,Debasis Ganguly,Lea A. Deleris,Francesca Bonin
Where published: WS 2019 6
ArXiv: 1904.03262
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1904.03262v1
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.
Where published: WS 2019 6
ArXiv: 1904.03262
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1904.03262v1
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.
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