Frontiers

Interpretable machine learning models to predict survival in esophageal cancer: a study based on the SEER database and external validation in China

The final, formatted version of the article will be published soon. Objective:We developed interpretable machine learning(ML) models to predict the overall survival(OS) of esophageal cancer patients.
IEEE

CiSeg: Unsupervised Cross-Modality Adaptation for 3D Medical Image Segmentation via Causal Intervention

Abstract: Unsupervised domain adaptation (UDA) addresses the domain shift problem by transferring knowledge from labeled source domain data (e.g. CT) to unlabeled target domain data (e.g. MRI). While ...
IEEE

Parameter Optimization for Model-Free Predictive Current Control of PMSMs Using ANN and K-Fold Cross-Validation

Abstract: Model-free predictive current control (MFPCC) of permanent magnet synchronous motors (PMSMs) based on an extended state observer (ESO) has been extensively studied in recent years due to its ...
New York Post

FanDuel promo code: Bet $5, get $300 in bonus bets if your bet wins for Knicks vs. 76ers

New York Knicks guard Josh Hart stares at a ball hanging on the basket during the third quarter. Charles Wenzelberg / New York Post New York Post may be compensated and/or receive an affiliate ...
Frontiers

Personalized ICU mortality assessment by interpretable machine learning algorithms in patients with sepsis combined lung cancer: a population-based study and an external ...

1 Department of Critical Care Medicine, Xuzhou Central Hospital, Xuzhou, Jiangsu, China 2 Department of Endocrinology and Metabolism, Huai’an Hospital Affiliated to Xuzhou Medical University and ...