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- Informatics in neurocritical care: new ideas for Big Data.Curr Opin Crit Care. 2016; 22: 87-93
- Machine learning in action: stroke diagnosis and outcome prediction.Front Neurol. 2021; 12: 734345
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- How many variables can humans process?.Psychol Sci. 2005; 16: 70-76
- Influence, information overload, and information technology in health care.Emerald Group Publishing Limited, Bingley, United Kingdom.2008
- Effect of fatigue, workload, and environment on patient safety in the pediatric intensive care unit.Pediatr Crit Care Med. 2007; 8: S11-S16
- Diagnostic errors in the intensive care unit: a systematic review of autopsy studies.BMJ Qual Saf. 2012; 21: 894-902
- Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review.F1000Res. 2019 Oct 8; 8 (PMID: 31824670; PMCID: PMC6894361): 1728https://doi.org/10.12688/f1000research.20498.2
- Forewarning of hypotensive events using a Bayesian artificial neural network in neurocritical care.J Clin Monit Comput. 2019; 33: 39-51
- A targeted real-time early warning score (TREWScore) for septic shock.Sci Translational Med. 2015; 7 (299ra122-299ra122)
- Early detection of increased intracranial pressure episodes in traumatic brain injury: external validation in an adult and in a pediatric cohort.Crit Care Med. 2017; 45: e316-e320
- A clinical prediction model to identify patients at high risk of hemodynamic instability in the pediatric intensive care unit.Crit Care. 2017; 21: 1-8
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- DeepSigns: a predictive model based on deep learning for the early detection of patient health deterioration.Expert Syst Appl. 2021; 165: 113905
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- Outcome prediction after moderate and severe traumatic brain injury: external validation of two established prognostic models in 1742 European patients.J Neurotrauma. 2021; 38: 1377-1388
- Prediction on critically ill patients: the role of “big data”.J Crit Care. 2020; 60: 64-68
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- Adding continuous vital sign information to static clinical data improves the prediction of length of stay after intubation: a data-driven machine learning approach.Respir Care. 2020; 65: 1367-1377
- Decoding accelerometry for classification and prediction of critically ill patients with severe brain injury.Scientific Rep. 2021; 11: 1-17
- Prognosis in severe brain injury.Crit Care Med. 2013; 41: 1104-1123
- The central role of prognosis in clinical decision making.JAMA. 2012; 307: 199-200
- High-throughput metabolite profiling: identification of plasma taurine as a potential biomarker of functional outcome after aneurysmal subarachnoid hemorrhage.J Neurosurg. 2019; 133: 1842-1849
- Outcome prediction in postanoxic coma with deep learning.Crit Care Med. 2019; 47: 1424-1432
- Quantitative eeg trends predict recovery in hypoxic-ischemic encephalopathy.Crit Care Med. 2019; 47: 1416
- Machine learning for precision medicine.Genome. 2021; 64: 416-425
- Detection of brain activation in unresponsive patients with acute brain injury.N Engl J Med. 2019; 380: 2497-2505
- Artificial intelligence to diagnose ischemic stroke and identify large vessel occlusions: a systematic review.J Neurointerventional Surg. 2020; 12: 156-164
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