The voice of decompensation: an explainable machine learning approach to assessing heart failure severity
We developed an explainable, high-performing voice-based HF monitoring model deployable via smartphones. The approach is non-invasive, low-burden and feasible for integration into nurse-coordinated post-discharge monitoring and remote triage.
Authors
- Ying Wang (ORCID: https://orcid.org/0000-0002-4568-4050)
- Zhenyue Gao (ORCID: https://orcid.org/0000-0003-2283-2820)
- Ying Yang (ORCID: https://orcid.org/0000-0002-9799-3293)
- Bochao Zhao
- Sen Zhang (ORCID: https://orcid.org/0009-0001-9744-1050)
- Keyao Liu
- Qiaoqin Wan
- Dire Ying
- Wendong Xiao
- Ping Han
Institutions
- Shihezi University (CN)
- King University (US)
- Peking University (CN)
- Ministry of Education (IR)
- Beijing Haidian Hospital (CN)
- Fu Wai Hospital (CN)
- University of Science and Technology Beijing (CN)
Publication Details
- Journal
- European Journal of Cardiovascular Nursing
- Published
- 2026-01-03
- DOI
- https://doi.org/10.1093/eurjcn/zvag003
- Citations
- 1
- Primary Topic
- Phonocardiography and Auscultation Techniques
- Type
- article
- Field-Weighted Citation Impact
- 36.44