ASTRAL-Net: A Vectorized Python Library for Scalable Clinical Prognostication in High-Volume Ischemic Stroke Registries (v1.0.0)
Description
Official v1.0.0 production release of ASTRAL-Net (astralnet), an open-source scientific computing package engineered to accelerate big data neuro-prognostication workflows across high-volume stroke registries.
The package provides a production-grade, highly optimized python implementation of the peer-reviewed ASTRAL (Acute Stroke Registry and Analysis of Lausanne) Protocol criteria published in Neurology (2012). Unlike standard bedside calculators that iterate through patient cohorts sequentially, astralnet implements high-performance vectorized linear matrix algebra utilizing the NumPy and Pandas processing cores to batch-process thousands of multi-variable patient records simultaneously in milliseconds.
Global Package Registry Link: https://pypi.org/project/astralnet/
Source Code Matrix: https://github.com/noorfatimacheema249-design/astralnet-core
Files
astralnet-v1.0.0.zip
Files
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Additional details
Software
- Repository URL
- https://github.com/noorfatimacheema249-design/astralnet-core
References
- Ntaios G, Faouzi M, Ferrari J, Lang W, Vemmos K, Michel P. An integer-based score to predict functional outcome in acute ischemic stroke: the ASTRAL score. Neurology. 2012 Jun 12;78(24):1916-22. doi: 10.1212/WNL.0b013e318259e221. Epub 2012 May 30. PMID: 22649218.