Negar Saleh: Integrating Interpretability Into AI-Based Pulmonary Embolism Prediction
Negar Saleh, Head of Artificial Intelligence at AmootSoft, shared Toktam Dehghani‘s post on LinkedIn:
“I’m happy to share our published research on explainable machine learning for the early prediction and anatomical classification of pulmonary embolism in the emergency department.
This work explores the role of explainable AI in supporting clinical decision-making and improving the interpretability of machine learning models in healthcare.
I’m grateful to have contributed to this research and to collaborate with the team on work at the intersection of AI and medicine.”
Toktam Dehghani, Assistant Professor at Mashhad University of Medical Sciences, shared a post on LinkedIn about a recent article she and her colleagues co-authored, published in BMC Emergency Medicine, adding:
“I am excited to share my research is now published!
Explainable machine learning for early prediction and anatomical classification of pulmonary embolism in the emergency department.”
Title: Explainable machine learning for early prediction and anatomical classification of pulmonary embolism in the emergency department
Authors: Maryam Panahi, Mohanna Fallah Ghadi, Toktam Dehghani, Fatemeh Mahdizadeh, Raheleh Ghouchan Nezhad Noor Nia, Negar Saleh, Saeid Eslami
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