Fast Prototyping a Dialogue Comprehension System for Nurse-Patient Conversations on Symptom Monitoring
Data for human-human spoken dialogues for research and development are currently very limited in quantity, variety, and sources; such data are even scarcer in healthcare. In this work, we investigate fast prototyping of a dialogue comprehension system by leveraging on minimal nurse-to-patient conver...
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Format | Journal Article |
Language | English |
Published |
08.03.2019
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Abstract | Data for human-human spoken dialogues for research and development are
currently very limited in quantity, variety, and sources; such data are even
scarcer in healthcare. In this work, we investigate fast prototyping of a
dialogue comprehension system by leveraging on minimal nurse-to-patient
conversations. We propose a framework inspired by nurse-initiated clinical
symptom monitoring conversations to construct a simulated human-human dialogue
dataset, embodying linguistic characteristics of spoken interactions like
thinking aloud, self-contradiction, and topic drift. We then adopt an
established bidirectional attention pointer network on this simulated dataset,
achieving more than 80% F1 score on a held-out test set from real-world
nurse-to-patient conversations. The ability to automatically comprehend
conversations in the healthcare domain by exploiting only limited data has
implications for improving clinical workflows through red flag symptom
detection and triaging capabilities. We demonstrate the feasibility for
efficient and effective extraction, retrieval and comprehension of symptom
checking information discussed in multi-turn human-human spoken conversations. |
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AbstractList | Data for human-human spoken dialogues for research and development are
currently very limited in quantity, variety, and sources; such data are even
scarcer in healthcare. In this work, we investigate fast prototyping of a
dialogue comprehension system by leveraging on minimal nurse-to-patient
conversations. We propose a framework inspired by nurse-initiated clinical
symptom monitoring conversations to construct a simulated human-human dialogue
dataset, embodying linguistic characteristics of spoken interactions like
thinking aloud, self-contradiction, and topic drift. We then adopt an
established bidirectional attention pointer network on this simulated dataset,
achieving more than 80% F1 score on a held-out test set from real-world
nurse-to-patient conversations. The ability to automatically comprehend
conversations in the healthcare domain by exploiting only limited data has
implications for improving clinical workflows through red flag symptom
detection and triaging capabilities. We demonstrate the feasibility for
efficient and effective extraction, retrieval and comprehension of symptom
checking information discussed in multi-turn human-human spoken conversations. |
Author | Ng, Angela Ramasamy, Savitha Macdonald, Michael R Chow, Wai Leng Lim, Hazel Tong, Shao Chuen Chen, Nancy F Suhaimi, Nur Farah Ain Binte Liu, Zhengyuan Krishnaswamy, Pavitra Ong, Sharon Lee, Sheldon |
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BackLink | https://doi.org/10.48550/arXiv.1903.03530$$DView paper in arXiv |
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Snippet | Data for human-human spoken dialogues for research and development are
currently very limited in quantity, variety, and sources; such data are even
scarcer in... |
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SubjectTerms | Computer Science - Computation and Language |
Title | Fast Prototyping a Dialogue Comprehension System for Nurse-Patient Conversations on Symptom Monitoring |
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