Alexander Bonde

Assessing the utility of deep neural networks in predicting postoperative surgical complications: a retrospective study

2021 · 111 Zit. · The Lancet Digital Health

Assessing optimal methods for transferring machine learning models to low-volume and imbalanced clinical datasets: experiences from predicting outcomes of Danish trauma patients

2023 · 9 Zit. · Frontiers in Digital Health

An assessment of the value of deep neural networks in genetic risk prediction for surgically relevant outcomes

2024 · 2 Zit. · PLoS ONE

Assessing the value of deep neural networks for postoperative complication prediction in pancreaticoduodenectomy patients

2024 · 2 Zit. · PLoS ONE

Assessing the Utility of Deep Neural Networks in Detecting Superficial Surgical Site Infections From Free Text Electronic Health Record Data

2023 · 1 Zit. · Research Square

TOWARDS AN AI-DRIVEN REGISTRY FOR POSTOPERATIVE COMPLICA-TIONS: A PROOF-OF-CONCEPT STUDY EVALUATING THE OPPORTUNITIES AND CHALLENGES OF AI-MODELS

2025 · 1 Zit. · medRxiv

An assessment of the value of deep neural networks in genetic risk prediction for surgically relevant outcomes

2024 · 1 Zit. · medRxiv

Towards an AI-driven registry for postoperative complications: a proof-of-concept study evaluating the opportunities and challenges of AI models

2025 · 0 Zit. · BMJ Health & Care Informatics