Last Posted: May 02, 2019
- The Transcriptomic Toolbox: Resources for Interpreting Large Gene Expression Data within a Precision Medicine Context for Metabolic Disease Atherosclerosis.
Marín de Evsikova Caralina et al. Journal of personalized medicine 2019 Apr 9(2) - Characterization of clot composition in acute cerebral infarct using machine learning techniques.
Chung Jong-Won et al. Annals of clinical and translational neurology 2019 Apr 6(4) 739-747 - Promising Use of Big Data to Increase the Efficiency and Comprehensiveness of Stroke Outcomes Research.
Ung David et al. Stroke 2019 May 50(5) 1302-1309 - Building Linked Big Data for Stroke in Korea: Linkage of Stroke Registry and National Health Insurance Claims Data.
Kim Tae Jung et al. Journal of Korean medical science 2018 Dec 33(53) e343 - FDA backs clinician-free AI imaging diagnostic tools.
Ratner Mark et al. Nature biotechnology 2018 36(8) 673-674 - Stroke genetics: discovery, biology, and clinical applications.
Dichgans Martin et al. The Lancet. Neurology 2019 Apr - Use of machine-learning algorithms to determine features of systolic blood pressure variability that predict poor outcomes in hypertensive patients.
Lacson Ronilda C et al. Clinical kidney journal 2019 Apr 12(2) 206-212 - Pharmacogenomic considerations for antiplatelet agents: the era of precision medicine in stroke prevention and neurointerventional practice.
Bonney Phillip A et al. Cold Spring Harbor molecular case studies 2019 Apr 5(2) - Accurate and rapid screening model for potential diabetes mellitus.
Pei Dongmei et al. BMC medical informatics and decision making 2019 Mar 19(1) 41 - Machine Learning-Based Model for Prediction of Outcomes in Acute Stroke.
Heo JoonNyung et al. Stroke 2019 Mar STROKEAHA118024293
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