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An Acoustical and Lexical Machine-Learning Pipeline to Identify Connectional Silences.
- Source :
-
Journal of Palliative Medicine . Dec2023, Vol. 26 Issue 12, p1627-1633. 7p. - Publication Year :
- 2023
-
Abstract
- Context: Developing scalable methods for conversation analytics is essential for health care communication science and quality improvement. Purpose: To assess the feasibility of automating the identification of a conversational feature, Connectional Silence, which is associated with important patient outcomes. Methods: Using audio recordings from the Palliative Care Communication Research Initiative cohort study, we develop and test an automated measurement pipeline comprising three machine-learning (ML) tools—a random forest algorithm and a custom convolutional neural network that operate in parallel on audio recordings, and subsequently a natural language processing algorithm that uses brief excerpts of automated speech-to-text transcripts. Results: Our ML pipeline identified Connectional Silence with an overall sensitivity of 84% and specificity of 92%. For Emotional and Invitational subtypes, we observed sensitivities of 68% and 67%, and specificities of 95% and 97%, respectively. Conclusion: These findings support the capacity for coordinated and complementary ML methods to fully automate the identification of Connectional Silence in natural hospital-based clinical conversations. [ABSTRACT FROM AUTHOR]
- Subjects :
- *HOSPITALS
*PHONOLOGICAL awareness
*CONVERSATION
*NATURAL language processing
*MACHINE learning
*AUTOMATION
*HEALTH
*INFORMATION resources
*COMMUNICATION
*SOUND recordings
*RESEARCH funding
*QUALITY assurance
*DESCRIPTIVE statistics
*PATIENT-professional relations
*ARTIFICIAL neural networks
*SOUND
*PALLIATIVE treatment
*LONGITUDINAL method
*ALGORITHMS
Subjects
Details
- Language :
- English
- ISSN :
- 10966218
- Volume :
- 26
- Issue :
- 12
- Database :
- Academic Search Index
- Journal :
- Journal of Palliative Medicine
- Publication Type :
- Academic Journal
- Accession number :
- 174081249
- Full Text :
- https://doi.org/10.1089/jpm.2023.0087