The voice of decompensation: an explainable machine learning approach to assessing heart failure severity.
Aims Heart failure (HF) is characterized by high morbidity and frequent hospital readmissions, highlighting the need for scalable out-of-hospital monitoring to support post-discharge nursing care. We aimed to (i) develop a HF-tailored, user-friendly voice task set suitable for remote monitoring; (ii...
| Publicado en: | European Journal of Cardiovascular Nursing Vol. 25; no. 3; pp. 607 - 617 |
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| Autores principales: | , , , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
Apr2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=195378296&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 195378296 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14745151 KJC jtl: European Journal of Cardiovascular Nursing issn: 14745151 maglogo: N pubinfo: dt: Apr2026 vid: 25 iid: 3 pid: 622 pub: Oxford University Press / USA artinfo: ui: 195378296 195378296 195378296 10.1093/eurjcn/zvag003 195378296 ppf: 607 ppct: 10 formats: tig: atl: The voice of decompensation: an explainable machine learning approach to assessing heart failure severity. aug: au: Yang, Yi Zhao, Bochao Ying, Dire Han, Ping Wang, Ying Liu, Keyao Gao, Zhenyue Zhang, Sen Xiao, Wendong Wan, Qiaoqin affil: School of Nursing, Peking University, No. 38, Xueyuan Road, Haidian District, Beijing 100083, China sug: subj: Heart Failure Diagnosis Severity of Illness Evaluation Machine Learning Utilization Telemetry Smartphone Utilization Speech Acoustics Task Performance and Analysis Human Funding Source China Tertiary Health Care Male Female Middle Age Aged Aged, 80 and Over Multicenter Studies Nonexperimental Studies Audiorecording Descriptive Statistics ROC Curve Sensitivity and Specificity Boosting Machine Learning Algorithms Pearson's Correlation Coefficient Spearman's Rank Correlation Coefficient Paired T-Tests Wilcoxon Rank Sum Test Data Analysis Software Cardiovascular Nursing Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Male Female ab: Aims Heart failure (HF) is characterized by high morbidity and frequent hospital readmissions, highlighting the need for scalable out-of-hospital monitoring to support post-discharge nursing care. We aimed to (i) develop a HF-tailored, user-friendly voice task set suitable for remote monitoring; (ii) identify HF-related acoustic markers and build explainable voice-based models; and (iii) evaluate the accuracy of smartphone recordings for clinical monitoring. Methods and results In this multicentre observational study, patients hospitalized with acute heart failure (AHF) were recruited at four centres. Voice recordings were obtained with professional devices and common smartphones (Apple, Huawei, Vivo). Eleven speech tasks were evaluated, and 11 acoustic feature categories were extracted [e.g. Mel frequency cepstrum coefficients (MFCCs), chroma, spectral, glottal features]. Two XGBoost models were trained to classify clinical status from admission to discharge and from mid-hospitalization to discharge; models were interpreted with Shapley Additive Explanations (SHAP). Four tasks were selected for the final model. The admission-to-discharge model achieved 0.76 accuracy, 0.86 sensitivity, 0.65 specificity, and an area under the ROC curve (AUC) of 0.77. The mid-hospitalization-to-discharge model showed 0.81 accuracy, 0.89 sensitivity, 0.71 specificity, and an AUC of 0.80. Shapley Additive Explanations analysis revealed vocal improvements corresponding to clinical recovery, characterized by transitions from unstable, noisy voice patterns to more stable, harmonic-rich, brighter, and stronger vocal expressions. Cross-device comparison demonstrated high consistency among recordings across different smartphone brands, supporting the feasibility of mobile-based voice data collection. Conclusion 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. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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