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ASTRAL-Net: A Vectorized Python Library for Scalable Clinical Prognostication in High-Volume Ischemic Stroke Registries (v1.0.0)

Authors: Fatima, Noor;

ASTRAL-Net: A Vectorized Python Library for Scalable Clinical Prognostication in High-Volume Ischemic Stroke Registries (v1.0.0)

Abstract

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

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