@inproceedings{michalopoulos-etal-2020-wheres,
title = "Where{'}s the Question? A Multi-channel Deep Convolutional Neural Network for Question Identification in Textual Data",
author = "Michalopoulos, George and
Chen, Helen and
Wong, Alexander",
editor = "Rumshisky, Anna and
Roberts, Kirk and
Bethard, Steven and
Naumann, Tristan",
booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.clinicalnlp-1.24",
doi = "10.18653/v1/2020.clinicalnlp-1.24",
pages = "215--226",
abstract = "In most clinical practice settings, there is no rigorous reviewing of the clinical documentation, resulting in inaccurate information captured in the patient medical records. The gold standard in clinical data capturing is achieved via {``}expert-review{''}, where clinicians can have a dialogue with a domain expert (reviewers) and ask them questions about data entry rules. Automatically identifying {``}real questions{''} in these dialogues could uncover ambiguities or common problems in data capturing in a given clinical setting. In this study, we proposed a novel multi-channel deep convolutional neural network architecture, namely Quest-CNN, for the purpose of separating real questions that expect an answer (information or help) about an issue from sentences that are not questions, as well as from questions referring to an issue mentioned in a nearby sentence (e.g., can you clarify this?), which we will refer as {``}c-questions{''}. We conducted a comprehensive performance comparison analysis of the proposed multi-channel deep convolutional neural network against other deep neural networks. Furthermore, we evaluated the performance of traditional rule-based and learning-based methods for detecting question sentences. The proposed Quest-CNN achieved the best F1 score both on a dataset of data entry-review dialogue in a dialysis care setting, and on a general domain dataset.",
}
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<abstract>In most clinical practice settings, there is no rigorous reviewing of the clinical documentation, resulting in inaccurate information captured in the patient medical records. The gold standard in clinical data capturing is achieved via “expert-review”, where clinicians can have a dialogue with a domain expert (reviewers) and ask them questions about data entry rules. Automatically identifying “real questions” in these dialogues could uncover ambiguities or common problems in data capturing in a given clinical setting. In this study, we proposed a novel multi-channel deep convolutional neural network architecture, namely Quest-CNN, for the purpose of separating real questions that expect an answer (information or help) about an issue from sentences that are not questions, as well as from questions referring to an issue mentioned in a nearby sentence (e.g., can you clarify this?), which we will refer as “c-questions”. We conducted a comprehensive performance comparison analysis of the proposed multi-channel deep convolutional neural network against other deep neural networks. Furthermore, we evaluated the performance of traditional rule-based and learning-based methods for detecting question sentences. The proposed Quest-CNN achieved the best F1 score both on a dataset of data entry-review dialogue in a dialysis care setting, and on a general domain dataset.</abstract>
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%0 Conference Proceedings
%T Where’s the Question? A Multi-channel Deep Convolutional Neural Network for Question Identification in Textual Data
%A Michalopoulos, George
%A Chen, Helen
%A Wong, Alexander
%Y Rumshisky, Anna
%Y Roberts, Kirk
%Y Bethard, Steven
%Y Naumann, Tristan
%S Proceedings of the 3rd Clinical Natural Language Processing Workshop
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F michalopoulos-etal-2020-wheres
%X In most clinical practice settings, there is no rigorous reviewing of the clinical documentation, resulting in inaccurate information captured in the patient medical records. The gold standard in clinical data capturing is achieved via “expert-review”, where clinicians can have a dialogue with a domain expert (reviewers) and ask them questions about data entry rules. Automatically identifying “real questions” in these dialogues could uncover ambiguities or common problems in data capturing in a given clinical setting. In this study, we proposed a novel multi-channel deep convolutional neural network architecture, namely Quest-CNN, for the purpose of separating real questions that expect an answer (information or help) about an issue from sentences that are not questions, as well as from questions referring to an issue mentioned in a nearby sentence (e.g., can you clarify this?), which we will refer as “c-questions”. We conducted a comprehensive performance comparison analysis of the proposed multi-channel deep convolutional neural network against other deep neural networks. Furthermore, we evaluated the performance of traditional rule-based and learning-based methods for detecting question sentences. The proposed Quest-CNN achieved the best F1 score both on a dataset of data entry-review dialogue in a dialysis care setting, and on a general domain dataset.
%R 10.18653/v1/2020.clinicalnlp-1.24
%U https://aclanthology.org/2020.clinicalnlp-1.24
%U https://doi.org/10.18653/v1/2020.clinicalnlp-1.24
%P 215-226
Markdown (Informal)
[Where’s the Question? A Multi-channel Deep Convolutional Neural Network for Question Identification in Textual Data](https://aclanthology.org/2020.clinicalnlp-1.24) (Michalopoulos et al., ClinicalNLP 2020)
ACL