Predicting vital sign deterioration with artificial intelligence or machine learning
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Multivariate regression-based models or AI-based early-warning systems have the potential to detect subtle trends in physiologic parameters over time to provide precision and reliability.
“Multivariate regression-based models or AI-based early-warning systems have the potential to detect subtle trends in physiologic parameters over time to provide precision and reliability (38–41).”
From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [40]
Preventative measures may be taken earlier and with more accuracy if AI metrics are implemented as opposed to traditional risk-evaluation scores.
“Preventative measures may be taken earlier and with more accuracy if AI metrics are implemented as opposed to the traditional risk-evaluation scores (40, 42).”
From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [40]
Vital sign monitoring and alarms are effective in alerting providers to discrete vital sign abnormalities in real time, but early or isolated vital sign abnormalities may fail to signal impending decompensation.
“They are effective in alerting providers to discrete vital sign abnormalities in real time; however, early or isolated vital sign abnormalities also may fail to signal to providers an impending decompensation (40).”
From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [40]
Vital sign monitoring and associated alarms were one of the earliest methods to detect patient decompensation.
“Vital sign monitoring and associated alarms were one of the earliest methods to detect patient decompensation (40).”
From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [40]
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Cites
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Self-citationAn Improved Multi-Output Gaussian Process RNN with Real-Time Validation for Early Sepsis DetectionPreprint
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Predicting tachycardia as a surrogate for instability in the intensive care unit
Yoon, J. H., Mu, L., Chen, L., Dubrawski, A. W., Hravnak, M., Pinsky, M. R., & Clermont, G. (2019) · Journal of Clinical Monitoring and Computing · doi:10.1007/s10877-019-00277-0 · Cited by 40 · see citing works
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Accessing Artificial Intelligence for Clinical Decision-Making
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Artificial Intelligence in the Intensive Care Unit
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Intraoperative hypotension and its prediction
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Self-citationPrediction of intraoperative hypotension from the linear extrapolation of mean arterial pressure
Jacquet‐Lagrèze, M., Larue, A., Guilherme, E., Schweizer, R., Portran, P., Ruste, M., Gazon, M., Aubrun, F., & Fellahi, J.-L. (2022) · European Journal of Anaesthesiology · doi:10.1097/eja.0000000000001693 · Cited by 44 · see citing works
Machine learning-based prediction of critical illness in children visiting the emergency department
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Artificial Intelligence in the Intensive Care Unit
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CON: The hypotension prediction index is not a validated predictor of hypotension
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Self-citationAu-Yeung, W.-T. M., Sevakula, R. K., Sahani, A. K., Kassab, M. B., Boyer, R. B., Isselbacher, E. M., & Armoundas, A. A. (2021) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztab058 · Cited by 24 · see citing works
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Artificial intelligence in vascular surgical decision making
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VitalDB: fostering collaboration in anaesthesia research
Vistisen, S. T., Pollard, T., Enevoldsen, J., & Scheeren, T. (2021) · British Journal of Anaesthesia · doi:10.1016/j.bja.2021.03.011 · Cited by 22 · see citing works
Self-citationA glimpse into the future of postoperative arterial blood pressure monitoring
Michard, F., Scheeren, T., & Saugel, B. (2020) · British Journal of Anaesthesia · doi:10.1016/j.bja.2020.04.065 · Cited by 18 · see citing works
Self-citationHypotension Prediction Index: from proof-of-concept to proof-of-feasibility
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Jensen, M. S. V., Eriksen, V. R., Rasmussen, S. M., Meyhoff, C. S., & Aasvang, E. K. (2024) · Acta Anaesthesiologica Scandinavica · doi:10.1111/aas.14541 · Cited by 14 · see citing works
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Awadallah, D., Thomas, G., Saklayen, S. L., Dalton, R., & Awad, H. (2020) · Journal of Cardiothoracic and Vascular Anesthesia · doi:10.1053/j.jvca.2020.11.048 · Cited by 8 · see citing works
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Mean arterial pressure is a major determinant of hypotension prediction index
Jacquet‐Lagrèze, M., Ruste, M., Schweizer, R., & Fellahi, J.-L. (2024) · European Journal of Anaesthesiology · doi:10.1097/eja.0000000000001991 · Cited by 6 · see citing works
Saugel, B., Critchley, L. A. H., Kaufmann, T., Flick, M., Kouz, K., Vistisen, S. T., & Scheeren, T. (2020) · Journal of Clinical Monitoring and Computing · doi:10.1007/s10877-020-00496-w · Cited by 4 · see citing works
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Mucksavage, J. J., He, K., Chang, J., Panlilio-Villanueva, M., Wang, T., Fraidenburg, D. R., & Benken, S. T. (2020) · Critical Care Explorations · doi:10.1097/cce.0000000000000204 · Cited by 3 · see citing works
Anesthesia Inmutable Registry of Real-time Vital Signs and Waveforms using BlockchainPreprint
Gutiérrez, A. F., Garbino, J. A. M., Burgos, V. L., Rajah, T., Risk, M. R., & Francisco, R. (2021) · medRxiv · doi:10.1101/2021.12.18.21267876 · Cited by 2 · see citing works
Pediatric Monitoring Technologies and Congenital Heart Disease: A Systematic Review
Duffy, S. S., Lee, S. C. L., & Sen, D. G. (2024) · World Journal for Pediatric and Congenital Heart Surgery · doi:10.1177/21501351241247500 · Cited by 2 · see citing works
Mehdiyev, S. (2025) · JOURNAL OF HIGH-FREQUENCY COMMUNICATION TECHNOLOGIES · doi:10.58399/cran2457 · Cited by 1 · see citing works
Artificial Intelligence and Predictive Analytics
de Keijzer, I. N., Vistisen, S. T., & Scheeren, T. (2021) · Springer eBooks · doi:10.1007/978-3-030-71752-0_29 · Cited by 1 · see citing works
Self-citationApplication of artificial intelligence techniques in the intensive care unit
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Internet of health things-enabled monitoring of vital signs in hospitals of the future
Sundas, A., Badotra, S., Singh, G., & Verma, A. (2024) · CRC Press eBooks · doi:10.1201/9781003471059-15
Khan, I., Choudhury, A. D., Alam, I., Salahuddin, M., Mathur, S. H., Siddiqui, F., & Badar, M. S. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-44915-4.00013-7
Kerremans, S., Dobney, W., Miranda, S. F. D. S., Manon, J., Cinelli, I., Parisi, A., Hoey, O. V., & Baatout, S. (2026) · Frontiers in Space Technologies · doi:10.3389/frspt.2026.1741121
- Authors
- Vistisen, S. T., Johnson, A. E. W., & Scheeren, T. W. L.
- Affiliations
- Aarhus University (DK)
- Massachusetts Institute of Technology (US)
- University Medical Center Groningen (NL)
- Corresponding author
- Scheeren, Thomas W. L.
- Venue
- Journal of Clinical Monitoring and Computing
- Published
- 2019-06-28
- Volume
- 33
- Issue
- 6
- Pages
- 949-951
- ISSN
- 1387-1307 (ISSN-L)
- 1573-2614
- OpenAlex
- W2954704200
- Cited by
- 35
- References
- 5
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 4 · 2023: 5 · 2022: 5 · 2021: 7 · 2020: 7 · 2019: 1
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Clinical Monitoring and Computing (publishedVersion, other-oa)
- PubMed (publishedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion, other-oa)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Surgery › Healthcare Technology and Patient Monitoring
- Keywords
- early warning scores, machine learning, artificial intelligence, clinical deterioration prediction, patient monitoring
- MeSH terms
- Machine Learning, Artificial Intelligence, Humans, Intensive Care Units, Tachycardia
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