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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Extracting Factual Min/Max Age Information from Clinical Trial Studies
%A Hou, Yufang
%A Ganguly, Debasis
%A Deleris, Léa
%A Bonin, Francesca
%Y Rumshisky, Anna
%Y Roberts, Kirk
%Y Bethard, Steven
%Y Naumann, Tristan
%S Proceedings of the 2nd Clinical Natural Language Processing Workshop
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F hou-etal-2019-extracting
%X 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.
%R 10.18653/v1/W19-1914
%U https://aclanthology.org/W19-1914/
%U https://doi.org/10.18653/v1/W19-1914
%P 107-116

Markdown (Informal)

[Extracting Factual Min/Max Age Information from Clinical Trial Studies](https://aclanthology.org/W19-1914/) (Hou et al., ClinicalNLP 2019)

• Extracting Factual Min/Max Age Information from Clinical Trial Studies (Hou et al., ClinicalNLP 2019)

ACL

• Yufang Hou, Debasis Ganguly, Léa Deleris, and Francesca Bonin. 2019. Extracting Factual Min/Max Age Information from Clinical Trial Studies. In Proceedings of the 2nd Clinical Natural Language Processing Workshop, pages 107–116, Minneapolis, Minnesota, USA. Association for Computational Linguistics.