Results 211 to 220 of about 495,754 (290)

IAR‐Net: Tabular Deep Learning Model for Interventionalist's Action Recognition

Advanced Intelligent Systems, EarlyView.
This study presents IAR‐Net, a deep‐learning framework for catheterization action recognition. To ensure optimality, this study quantifies interoperator similarities and differences using statistical tests, evaluates the distribution fidelity of synthetic data produced by six generative models, and benchmarks multiple deep‐learning models.
Toluwanimi Akinyemi, Olatunji Omisore, Wenjing Du, Wenke Duan, Chen Bailang, Liu Kun, Lei Wang, Minxin Wei   +7 more
wiley   +1 more source

Development and validation of a nomogram model for assessing the severity of acute pancreatitis from the perspective of PICS. [PDF]

Front Nutr
Liu D, Liu H, Yao J, Chen G, Wang W, Cao B, Hu J, Wu W.   +7 more
europepmc   +1 more source

Autonomous Recognition of Retained Secretions in Central‐Airway Based on Deep Learning for Adult Patients Receiving Invasive Mechanical Ventilation

Advanced Intelligent Systems, EarlyView.
This work presents a deep learning model to autonomously recognize and classify the secretion retention into three levels for patients receiving invasive mechanical ventilation, achieving 89.08% accuracy. This model can be implemented to ventilators by edge computing, whose feasibility is approved.
Shuai Wang, Zhuoran Xiao, Meng Wang, Jianchao Li, Chunjing Tao, Jiangzhen Guo, Yubo Fan   +6 more
wiley   +1 more source

Serum IL-17F as a biomarker of infection-independent cirrhosis progression. [PDF]

Front Immunol
Konstantis G, Passenberg M, Guntlisbergen C, Schütte A, Jung B, Nuruzade N, Seltsam F, Hoyer DP, Best J, Willuweit K, Schmidt HH, Guckenbiehl S, Rashidi-Alavijeh J.   +12 more
europepmc   +1 more source

Predicting Postresection Colorectal Liver Metastases Recurrence Using Advanced Graph Neural Networks with Explainability and Causal Inference

Advanced Intelligent Systems, EarlyView.
This study introduces a framework that combines graph neural networks with causal inference to forecast recurrence and uncover the clinical and pathological factors driving it. It further provides interpretability, validates risk factors via counterfactual and interventional analyses, and offers evidence‐based insights for treatment planning ...
Jubair Ahmed, Md. Abdur Rahman, Mohaimenul Azam Khan Raiaan, Sami Azam   +3 more
wiley   +1 more source

Time-constant absolute effect measures for time-to-event outcomes. [PDF]

BMC Med Res Methodol
Kuss O, Hoyer A.
europepmc   +1 more source

RPSLearner: A Novel Approach Based on Random Projection and Deep Stacking Learning for Categorizing Non‐Small Cell Lung Cancer

Advanced Intelligent Systems, EarlyView.
Identifying non‐small cell lung cancer (NSCLC) subtypes is essential for precision cancer treatment. Conventional methods are laborious, or time‐consuming. To address these concerns, RPSLearner is proposed, which combines random projection and stacking ensemble learning for accurate NSCLC subtyping. RPSLearner outperforms state‐of‐the‐art approaches in
Xinchao Wu, Jieqiong Wang, Shibiao Wan
wiley   +1 more source

Association between serum vitamin D deficiency and visceral fat indices in adolescents: The Ewha Birth and growth cohort study. [PDF]

PLoS One
Lee H, Park H, Jun S, Jang H, Hong YS, Jung-Choi K, Lee HA, Park H.   +7 more
europepmc   +1 more source

Edge Information‐Augmented Auxiliary Diagnosis Method for Cervical Cancer in Medical Decision‐Making Systems

Advanced Intelligent Systems, EarlyView.
To address the problems of insufficient utilization of multiscale features and inefficient feature sharing between tasks in the model, this study proposes an edge‐enhanced intelligent cervical cancer screening method that achieves feature reuse and improves efficiency by jointly optimizing nucleolus segmentation and lesion classification.
Li Wen, Xiaoqing Zhang, Tingyi Dai, Fangfang Gou, Jia Wu   +4 more
wiley   +1 more source

Gamma exponentiated generalized family of distributions with properties and applications. [PDF]

Sci Rep
Alshawarbeh E, Makubate B, Dutta S, Musekwa RR.   +3 more
europepmc   +1 more source