Artificial Intelligence /
Machine Learning
Curated resources on AI and MA in neuromonitoring
Browse webinars, interviews, and publications exploring how AI and MA are being applied to brain monitoring, from signal analysis to clinical decision support.
Webinars
A selection of expert-led sessions from the Case Studies in Neurocritical Care AI webinar series, focusing on machine learning models, signal processing, and the integration of AI into multimodal monitoring workflows.
Browse all webinars to explore the full library across neuromonitoring topics.
Automated Artifact Detection for ABP Data From Heuristics to Deep Neural Networks
Tony Okeke explores innovative approaches to identifying artifacts in arterial blood pressure (ABP) data using a variety of machine learning techniques.
NCC Big Data Analysis: Is Unsupervised Clustering a Solution?
Jeanette Tas provides an overview of how unsupervised clustering, which uncovers new data patterns, has been applied to the field of neurocritical care.
A Data-Driven Disease Course of TBI: Mining Prognostic Trajectories from ICU Data
Shubhayu speaks on integrating heterogeneous medical record data to model how clinical course influences 6-month GOSE outcomes. Using a prospective TBI cohort, he trains recurrent neural networks to predict ordinal GOSE scores every 2 hours from a token-embedded time series, incorporating missing values. His approach converts large patient records into interpretable time series with minimal processing.
Data Exploration through Multimodal Animation
Dr. Ramon Diaz-Arrastia speaks on his experience as a leader in TBI research and how analytics and big data can advance his work into the future.
Structuring Unstructured Data
Professor Ed Kim from Drexel University’s College of Computing and Informatics brings us up to speed on the background to modern AI, machine learning, and deep learning methods.
The Groundwork for Multimodal Machine Learning
Professor Ed Kim from Drexel University’s College of Computing and Informatics brings us up to speed on the background to modern AI, machine learning, and deep learning methods.
Interviews & Podcasts
Conversations with clinicians, data scientists, and researchers discussing how AI and MA are shaping neurocritical care, including real-world challenges in clinical adoption and model validation.
Visit the full Interviews & Podcasts page to discover more perspectives from across the field.
Predictive Modeling in TBI: Insights from Florian van Leeuwen
Florian is a PhD Candidate in Statistics at Utrecht University, focusing on prediction models for complex datasets and applying these models to cases of brain injury. In this interview, he sits down with Dick to discuss what he believes should be the future of neuroscience with specific regards to brain injury and neurocritical care. The two speak on how prediction models can assist clinicians in making difficult decisions regarding individual TBI patients, and how we can make models output more reliable, precise predictions from complex multimodal data sources.
Big Data and the Cloud in TBI with Craig Maddux & Mark Talens
Craig Maddux, healthcare analytics and technical specialist at IBM, and Mark Talens, technical executive for data and AI in healthcare, discuss the roles of big data, the cloud, and AI in the care of patients with traumatic brain injury.Â
Publications
A curated collection of peer-reviewed research on AI and machine learning in neurocritical care, including algorithm development, validation studies, and clinical translation. Publications are organized by the newsletter editions in which they were featured, making it easy to explore them in context.
Explore all publications to stay up to date with the latest findings in the field.
March 2026
Freyer, O., Mathias, R., […], Gilbert, S. (2026). The regulation of artificial intelligence in intensive care units: from narrow tools to generalist systems. npj Digit. Med. https://doi.org/10.1038/s41746-026-02535-3.
Martin, J., Afshar, M., […], Churpek, M., (2026). Explainable multimodal deep learning models for variable-length sequences in critically ill patients. Journal of Biomedical Informatics. https://doi.org/10.1016/j.jbi.2026.105001.
February 2026
Azarfar, G., Naimimohasses, S., […], Bhat, M. (2025). Responsible adoption of multimodal artificial intelligence in health care: promises and challenges. The Lancet Digital Health. https://doi.org/10.1016/j.landig.2025.100917.
Doan, L.M.T., Shahhosseini, […], Occhipinti, A. (2026). Bridging modalities with AI: a review of AI advances in multimodal biomedical imaging. Commun Eng. https://doi.org/10.1038/s44172-026-00602-x.
Njei, B., Al-Ajlouni, Y. A., […], Al-Ajlouni, A. F. (2026). Artificial intelligence agents in healthcare research: A scoping review. PloS One. https://doi.org/10.1371/journal.pone.0342182.
November 2025
Wu, Jingyi, Bai, Shaojie, […], Kainerstorfer, Jana. Synthetic-data-driven LSTM framework for tracing cardiac pulsation in optical signals. Biomedical Optics Express. https://doi.org/10.1364/BOE.574286.
May 2025
Chen, X., Olakorede, I., Bögli, S., […], Smielewski, P. (2025) Generalised Label-free Artefact Cleaning for Real-time Medical Pulsatile Time Series, arXiv. https://doi.org/10.48550/arXiv.2504.21209.
December 2024
Liu, T., Hetherington, T.C., […], Cleveland, J.A. Does AI-Powered Clinical Documentation Enhance Clinician Efficiency? A Longitudinal Study. NEJM AI 1(12). https://doi.org/10.1056/AIoa2400659.
Sharma, R., Salman, S., Gu, Q., Freeman, W.D. (2024). Advancing Neurocritical Care with Artificial Intelligence and Machine Learning: The Promise, Practicalities, and Pitfalls Ahead. Neurol Clin 43(1):153-165. https://doi.org/10.1016/j.ncl.2024.08.003.
November 2024
Jiang, M., Cao, F., Zhang, Q. et al. (2024). Model-Predicted Brain Temperature Computational Imaging by Multimodal Noninvasive Functional Neuromonitoring of Cerebral Oxygen Metabolism and Hemodynamics: MRI-Derived and Clinical Validation. Journal of Cerebral Blood Flow and Metabolism: Official Journal of the International Society of Cerebral Blood Flow and Metabolism. https://doi.org/10.1177/0271678×241270485.
September 2024
Bhattacharyay, S. (2024). From Big Data to Personal Narratives: A Supervised Learning Framework of Decoding the Course of Traumatic Brain Injury in Intensive Care. Apollo – University of Cambridge Repository. https://doi.org/10.17863/CAM.109007.
July 2024
Smielewski, P., Beqiri, E., Mataczynski, C., Placek, M., Kazimierska, A., Hutchinson, P.J., Czosnyka, M., Kasprowicz, M. (2024). Advanced Neuromonitoring Powered by ICM+ and its Place in the Brand New AI World, Reflections at the 20th Anniversary Boundary. Brain and Spine. https://doi.org/10.1016/j.bas.2024.102835.
Srichawla, B.S. (2024). Future of Neurocritical Care: Integrating Neurophysics, Multimodal Monitoring, and Machine Learning. World Journal of Critical Care Medicine. https://doi.org/10.5492/wjccm.v13.i2.91397
Vakitbilir, N., Bergmann, T., Froese, L., Gomez, A., Sainbhi, A.S., Stein, K.Y., Islam, A., Zeiler, F.A. (2024). Multivariate Modeling and Prediction of Cerebral Physiology in Acute Traumatic Neural Injury: A Scoping Review. Computers in Biology and Medicine. https://doi.org/10.1016/j.compbiomed.2024.108766
March 2024
Ravindranath, M., Candan, K.S., Sapino, M.L., Appavu, B. (2024). MMA: metadata supported multi-variate attention for onset detection and prediction. Data Mining and Knowledge Discovery. DOI: 10.1007/s10618-024-01008-z
October 2023
Beqiri E, Badjatia N, […], Curing Coma Campaign and its Contributing Members. (2023). Common Data Elements for Disorders of Consciousness: Recommendations from the Working Group on Physiology and Big Data. Neurocrit Care. 2023 Sep 13. Epub ahead of print. PMID: 37704934. https://doi.org/10.1007/s12028-023-01846-7.
July 2023
Zou, B., Mi, X., Stone, E., & Zou, F. (2023). A deep neural network framework to derive interpretable decision rules for accurate traumatic brain injury identification of infants. BMC Medical Informatics and Decision Making. https://doi.org/10.1186/s12911-023-02155-x.
May 2023
Zou, B., Mi, X., Stone, E., & Zou, F. (2023). A deep neural network framework to derive interpretable decision rules for accurate traumatic brain injury identification of infants. BMC Medical Informatics and Decision Making. https://doi.org/10.1186/s12911-023-02155-x
October 2022
Ack, S. E., Loiseau, S. Y., Sharma, G., Goldstein, J. N., Lissak, I. A., Duffy, S. M., … & Rosenthal, E. S. (2022). Neurocritical Care Performance Measures Derived from Electronic Health Record Data are Feasible and Reveal Site-Specific Variation: A CHoRUS Pilot Project. Big Data in Neurocritical Care, Neurocritical Care, 37(2), 276-290. https://pubmed.ncbi.nlm.nih.gov/35689135/.
Elmer, J., He, Z., May, T., Osborn, E., Moberg, R., Kemp, S., … & Hirsch, K. G. (2022). Precision Care in Cardiac Arrest: ICECAP (PRECICECAP) Study Protocol and Informatics Approach. Big Data in Neurocritical Care, Neurocritical Care, 1-11. https://pubmed.ncbi.nlm.nih.gov/35229231/.
Moberg, R., Moyer, E. J., Olson, D., Rosenthal, E., & Foreman, B. (2022). Harmonization of Physiological Data in Neurocritical Care: Challenges and a Path Forward. Big Data in Neurocritical Care, Neurocritical Care, 1-4. https://pubmed.ncbi.nlm.nih.gov/35641807/.