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Interpretable machine learning for predicting optimal surgical timing in polytrauma patients with TBI and fractures to reduce postoperative infection risk

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This retrospective study leverages machine learning to determine the optimal timing for fracture reconstruction surgery in polytrauma patients, focusing on those with concomitant traumatic brain injury. The analysis included 218 patients admitted to Qilu Hospital of Shandong University from July 2011 to April 2024.

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Philosophy Eats AI

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In 2011, coder-turned-venture-investor Marc Andreessen famously declared, Software is eating the world in the analog pages of The Wall Street

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Notes on a New Philosophy of Empirical Science (2011)

Hacker News

The book also considers the field of machine learning. Here the traditional approach requires that the models proposed to solve learning problems be extremely simple, in order to avoid overfitting. The argument is especially strong in the context of computer vision, which is plagued by chronic problems of evaluation.

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Adaptive Gradient Algorithm

Dataconomy

The Adaptive Gradient Algorithm (AdaGrad) represents a significant stride in optimization techniques, particularly in the realms of machine learning and deep learning. By dynamically adjusting the learning rates for different parameters during model training, AdaGrad helps tackle challenges of convergence and efficiency.

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Revolutionary AI Tool Shows Promise in Pancreatic Cancer Screening, Facing Regulatory Hurdles in China

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Although pancreatic cancer has a low survival rate, losses like Steve Jobs in 2011 and Wu Zunyou highlight its impact. A group of researchers made a groundbreaking advancement in early-stage screening for pancreatic cancer using an artificial intelligence tool.

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With AI Changing Everything, Here's How Teachers Can Shape the New Culture of Learning

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When Chris Knight started teaching in 2011, overhead projectors were being replaced by smartboards and teachers could finally project slides directly

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LLM Agents Underscore One Truth: Data Is The Real Differentiator.

Towards AI

Since 2011, Peter Norvig’s words underscore the power of a data-centric approach in machine learning. Upgrade to access all of Medium. Edited Photo by Taylor Vick on Unsplash In ML engineering, data quality isn’t just critical — it’s foundational. Because of how ML practitioners were initially trained.

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