Fast Prototyping a Dialogue Comprehension System for Nurse-Patient Conversations on Symptom Monitoring
This enables automated symptom monitoring from limited data to improve clinical workflows, though it is incremental as it adapts existing methods to a new domain.
The paper tackled the scarcity of healthcare dialogue data by fast prototyping a comprehension system using minimal nurse-patient conversations, achieving over 80% F1 score on a real-world test set.
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.