Who Controlled the Evidence? Question Answering for Disclosure Information Extraction

Hardy Hardy, Derek Ruths, Nicholas B. King
Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:340-349, 2023.

Abstract

Conflict of interest (COI) disclosure statements provide rich information to support transparency and reduce bias in research. We introduce a novel task to identify relationships between sponsoring entities and the research studies they sponsor from the disclosure statement. This task is challenging due to the complexity of recognizing all potential relationship patterns and the hierarchical nature of identifying entities first and then extracting their relationships to the study. To overcome these challenges, in this paper, we also constructed a new annotated dataset and proposed a Question Answering-based method to recognize entities and extract relationships. Our method has demonstrated robustness in handling diverse relationship patterns, and it remains effective even when trained on a low-resource dataset.

Cite this Paper

BibTeX
@InProceedings{pmlr-v209-hardy23a, title = {Who Controlled the Evidence? Question Answering for Disclosure Information Extraction}, author = {Hardy, Hardy and Ruths, Derek and King, Nicholas B.}, booktitle = {Proceedings of the Conference on Health, Inference, and Learning}, pages = {340--349}, year = {2023}, editor = {Mortazavi, Bobak J. and Sarker, Tasmie and Beam, Andrew and Ho, Joyce C.}, volume = {209}, series = {Proceedings of Machine Learning Research}, month = {22 Jun--24 Jun}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v209/hardy23a/hardy23a.pdf}, url = {https://proceedings.mlr.press/v209/hardy23a.html}, abstract = {Conflict of interest (COI) disclosure statements provide rich information to support transparency and reduce bias in research. We introduce a novel task to identify relationships between sponsoring entities and the research studies they sponsor from the disclosure statement. This task is challenging due to the complexity of recognizing all potential relationship patterns and the hierarchical nature of identifying entities first and then extracting their relationships to the study. To overcome these challenges, in this paper, we also constructed a new annotated dataset and proposed a Question Answering-based method to recognize entities and extract relationships. Our method has demonstrated robustness in handling diverse relationship patterns, and it remains effective even when trained on a low-resource dataset.} }
Endnote
%0 Conference Paper %T Who Controlled the Evidence? Question Answering for Disclosure Information Extraction %A Hardy Hardy %A Derek Ruths %A Nicholas B. King %B Proceedings of the Conference on Health, Inference, and Learning %C Proceedings of Machine Learning Research %D 2023 %E Bobak J. Mortazavi %E Tasmie Sarker %E Andrew Beam %E Joyce C. Ho %F pmlr-v209-hardy23a %I PMLR %P 340--349 %U https://proceedings.mlr.press/v209/hardy23a.html %V 209 %X Conflict of interest (COI) disclosure statements provide rich information to support transparency and reduce bias in research. We introduce a novel task to identify relationships between sponsoring entities and the research studies they sponsor from the disclosure statement. This task is challenging due to the complexity of recognizing all potential relationship patterns and the hierarchical nature of identifying entities first and then extracting their relationships to the study. To overcome these challenges, in this paper, we also constructed a new annotated dataset and proposed a Question Answering-based method to recognize entities and extract relationships. Our method has demonstrated robustness in handling diverse relationship patterns, and it remains effective even when trained on a low-resource dataset.
APA
Hardy, H., Ruths, D. & King, N.B.. (2023). Who Controlled the Evidence? Question Answering for Disclosure Information Extraction. Proceedings of the Conference on Health, Inference, and Learning, in Proceedings of Machine Learning Research 209:340-349 Available from https://proceedings.mlr.press/v209/hardy23a.html.

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