AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
Artificial Intelligence (AI) has been a major factor to transform healthcare delivery and decision-making in healthcare. Computer vision, machine learning, and deep learning are some of the AI techniques widely used in workflows of healthcare institutions to promote risk assessment, diagnosis, and care planning. This s...
It is concluded that explainable artificial intelligence improves trust, reliability, and accountability in healthcare systems and is a prerequisite for successful integration of intelligent technologies into clinical practice.
It is argued that LASSO, not the highest-discriminating model, is the model best suited to direct clinical deployment, and lessons for the machine learning and healthcare community regarding data infrastructure, model selection, and value of calibration and interpretability in high-stakes decision support are presented...
A unified framework that integrates predictive modeling, SHapley Additive exPlanations (SHAP), and constrained intervention simulation for interpretable multi-complication risk prediction in Type-2 Diabetes Mellitus (T2DM).
BACKGROUND This multicenter study developed and validated an interpretable machine learning model integrating granular nursing and emergency department data collected within the first 24 hours to predict in-hospital mortality in acute ischemic stroke (AIS). METHODS We analyzed a retrospective cohort of 5,014 adult AI...
The protocol emphasizes several key components, including the preparation of standardized healthcare datasets, the development of stable machine learning models, the evaluation of predictive performance, the generation of clinically relevant explanations, and the statistical assessment of reproducibility across repeate...
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