ArticleIEEE journal of biomedical and health informatics2018
A Model-Based Machine Learning Approach to Probing Autonomic Regulation From Nonstationary Vital-Sign Time Series.
Article in IEEE journal of biomedical and health informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 22 citations in OpenAlex.
- Switching State Space Modeling via Constrained Inference for Clinical Outcome Prediction.Proceedings of machine learning research · 2025Article
- A novel artificial intelligence based intensive care unit monitoring system: using physiological waveforms to identify sepsis.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2021Article
- Measuring Activities of Daily Living in Stroke Patients with Motion Machine Learning Algorithms: A Pilot Study.International journal of environmental research and public health · 2021Article
- Towards precision critical care management of blood pressure in hemorrhagic stroke patients using dynamic linear models.PloS one · 2019Article
- Estimating Patient's Health State Using Latent Structure Inferred from Clinical Time Series and Text.... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics · 2017Article
- Knowledge Distillation via Constrained Variational Inference.Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial IntelligenceArticle
- Early sepsis detection in critical care patients using multiscale blood pressure and heart rate dynamics.Journal of electrocardiologyArticle
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Physiological variables, such as heart rate (HR), blood pressure (BP) and respiration (RESP), are tightly regulated and coupled under healthy conditions, and a break-down in the coupling has been associated with aging and disease. We present an approach that incorporates physiological modeling within a switching linear dynamical systems (SLDS) framework to assess the various functional components of the autonomic regulation through transfer function analysis of nonstationary multivariate time series of vital signs. We validate our proposed SLDS-based transfer function analysis technique in automatically capturing 1) changes in baroreflex gain due to postural changes in a tilt-table study including ten subjects, and 2) the effect of aging on the autonomic control using HR/RESP recordings from 40 healthy adults. Next, using HR/BP time series of more than 450 adult ICU patients, we show that our technique can be used to reveal coupling changes associated with severe sepsis (AUC = 0.74, sensitivity = 0.74, specificity = 0.60). Our findings indicate that reduced HR/BP coupling is significantly associated with severe sepsis even after adjusting for clinical interventions (P 0.001). These results demonstrate the utility of our approach in phenotyping complex vital-sign dynamics, and in providing mechanistic hypotheses in terms of break-down of autoregulatory systems under healthy and disease conditions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.