Christopher R. King

Use of Machine Learning to Develop and Evaluate Models Using Preoperative and Intraoperative Data to Identify Risks of Postoperative Complications

2021 · 341 Zit. · JAMA Network Open

Effect of machine learning models on clinician prediction of postoperative complications: the Perioperative ORACLE randomised clinical trial

2024 · 18 Zit. · British Journal of Anaesthesia

User-Centered Design of a Machine Learning Dashboard for Prediction of Postoperative Complications

2023 · 14 Zit. · Anesthesia & Analgesia

Update to ‘Deep-learning model for predicting 30-day postoperative mortality’ (Br J Anaesth 2019; 123: 688–95)

2020 · 9 Zit. · British Journal of Anaesthesia

Perioperative Predictions with Interpretable Latent Representation

2022 · 8 Zit. · Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

(Un)fairness in Post-operative Complication Prediction Models

2020 · 4 Zit. · arXiv (Cornell University)

Effect of Machine Learning on Anaesthesiology Clinician Prediction of Postoperative Complications: The Perioperative ORACLE Randomised Clinical Trial

2024 · 1 Zit. · medRxiv

Machine learning models in anaesthesiology: bridging the gap from model training to implementation

2026 · 0 Zit. · British Journal of Anaesthesia