Utility of thromboelastography to assess the effect of anticoagulation reversal in intracranial hemorrhage.

Background: Intracranial hemorrhage (ICH) is a serious complication associated with oral anticoagulant use and is associated with significant morbidity and mortality. Although anticoagulation reversal agents are utilized as standard of care, practitioners are limited in their ability to assess degree of anticoagulation reversal for direct oral anticoagulants (DOACs). There is a clinical need identify biomarkers to assess anticoagulation status in patients with DOAC-associated ICH to ensure hemostatic efficacy of anticoagulation reversal agents in the acute setting.

Precise Image-level Localization of Intracranial Hemorrhage on Head CT Scans with Deep Learning Models Trained on Study-level Labels.

Purpose To develop a highly generalizable weakly supervised model to automatically detect and localize image-level intracranial hemorrhage (ICH) by using study-level labels. Materials and Methods In this retrospective study, the proposed model was pretrained on the image-level Radiological Society of North America dataset and fine-tuned on a local dataset by using attention-based bidirectional long short-term memory networks. This local training dataset included 10 699 noncontrast head CT scans in 7469 patients, with ICH study-level labels extracted from radiology reports.

Phenotypes of Patients with Intracerebral Hemorrhage, Complications, and Outcomes.

Background: The objective of this study was to define clinically meaningful phenotypes of intracerebral hemorrhage (ICH) using machine learning.

Methods: We used patient data from two US medical centers and the Antihypertensive Treatment of Acute Cerebral Hemorrhage-II clinical trial. We used k-prototypes to partition patient admission data.

Northwestern University resource and education development initiatives to advance collaborative artificial intelligence across the learning health system.

Introduction: The rapid development of artificial intelligence (AI) in healthcare has exposed the unmet need for growing a multidisciplinary workforce that can collaborate effectively in the learning health systems. Maximizing the synergy among multiple teams is critical for Collaborative AI in Healthcare.

Methods: We have developed a series of data, tools, and educational resources for cultivating the next generation of multidisciplinary workforce for Collaborative AI in Healthcare.