Results 221 to 230 of about 25,341,143 (317)

Identifying Venous Insufficiency in Head and Neck Reconstruction Flaps Using Machine Learning and Deep Learning Methods

Head &Neck, EarlyView.
ABSTRACT Background Venous insufficiency is a major cause of flap failure in head and neck reconstruction. AI provides a reliable, convenient solution for early detection. Methods Clinical data and postoperative flap photos of head and neck cancer patients (2018–2024) at our center were retrospectively collected, categorized into normal and venous ...
Yurong He, Jugao Fang, Lizhen Hou, Zufei Li, Shizhi He, Yanming Zhao, Qijia Li, Rongjia Li, Xiaoyu Shi, Jiayi Yang, Qi Zhong   +10 more
wiley   +1 more source

A Comparative Machine Learning Study Identifies Light Gradient Boosting Machine (LightGBM) as the Optimal Model for Unveiling the Environmental Drivers of Yellowfin Tuna (<i>Thunnus albacares</i>) Distribution Using SHapley Additive exPlanations (SHAP) Analysis. [PDF]

Biology (Basel)
Yang L, Zhou W, Zhang C, Tang F.
europepmc   +1 more source

Machine learning‐based prediction of large‐for‐gestational‐age neonates in diabetic and non‐diabetic pregnancies

International Journal of Gynecology &Obstetrics, EarlyView.
Abstract Objective This study determines whether a machine‐learning model integrating sonographic biometry with maternal clinical parameters improves prediction of large‐for‐gestational‐age (LGA) compared with Hadlock's EFW formula. Methods We conducted a retrospective cohort study including all singleton live births at ≥32 gestational weeks at a ...
Ohad Houri, Asaf Romano, Asnat Walfisch, Eran Hadar, Yinon Gilboa, Leor Perl, Nadav Loebl, Ron Unger   +7 more
wiley   +1 more source

Improving Machine Learning Classification Predictions through SHAP and Features Analysis Interpretation

Journal of Chemical Information and Modeling
Leonardo Bernal, Giulio Rastelli, Luca Pinzi   +2 more
openaire   +1 more source

Progress of metabolomics‐centric multi‐omics research in medicine

iMetaOmics, EarlyView.
The graphical abstract illustrates a holistic roadmap for metabolomics‐centric multi‐omics integration in medical research. The upper panel depicts the technological transition from traditional bulk analysis to high‐resolution single‐cell and spatial methodologies, specifically addressing inherent challenges such as molecular complexity and dynamic ...
Ziyi Wang, Shiwen Deng, Xiaoxu Wang, Xiao Chang, Na Guo, Peng Chen, Hongjun Yang   +6 more
wiley   +1 more source

Elucidating predictors of preoperative acute heart failure in older people with hip fractures through machine learning and SHAP analysis: a retrospective cohort study. [PDF]

BMC Geriatr
Yu Q, Fu M, Hou Z, Wang Z.
europepmc   +1 more source

Comprehensive spatial profiling reveals transitional microbiome dynamics and microbial heterogeneity in pediatric adenoid hypertrophy

iMetaOmics, EarlyView.
This study characterizes the upper respiratory microbiome in 276 children (101 Adenoid hypertrophy (AH) 119 Adenotonsillar hypertrophy (ATH), 11 Tonsil hypertrophy (TH), and 45 healthy controls by analyzing 1149 samples across five distinct niches: nasopharyngeal swabs (NS), adenoid swab (AS), and tonsil swabs (TS), plus adenoid tissues (AT) and tonsil
Kaining Chen, Xin Wan, Wenli Guo, Hongyan Yu, Yuantao Tong, Huiwen Ke, Junjie Fang, Xilian Luo, Di Che, Chao Hou, Xu Mei, Bingxue Hu, Yuehong Liu, Xiaoxiao Yu, Daoman Xiang, Wensi Chen, Yingjia Zeng, Yufen Xu, Jun Shen, Qiuxia Chen, Lei Pi, Lanyan Fu, Huazhong Zhou, Bingshao Liang, Meng Zhang, Yifan Dong, Yanqiu Chen, Wenhao Zhou, Xiaoqiong Gu   +28 more
wiley   +1 more source

Tailoring Hematite Photoanodes for Improved PEC Performance: The Role of Alcohol Species Revealed by SHAP Analysis. [PDF]

ACS Omega
Idei T, Nagai Y, Pan Z, Katayama K.
europepmc   +1 more source

Interpretable machine learning enables early and accurate detection of drug‐induced liver injury: A multicenter study with real‐world clinical translation

Interdisciplinary Medicine, EarlyView.
This study develops an interpretable gradient‐boosting model that accurately identifies drug‐induced liver injury (DILI) using routine laboratory data. The model explains key clinical features through SHapley Additive exPlanations analysis and detects DILI earlier than expert evaluation, offering a transparent and practical tool for precision ...
Jingyi Ling, Shian Zhang, Jun Lin, Zhengju Xu, Runcheng Xu, Qian Lv, Kaixiang Zhang, Sheng Liu, Jie Guo, Cheng Hua, Yin Jia, Xiaoyu Xu, Kun Qian, Shanrong Liu   +13 more
wiley   +1 more source

Correction: Enhanced desalination with polyamide thin-film membranes using ensemble ML chemometric methods and SHAP analysis. [PDF]

RSC Adv
Usman J, Abba SI, Abdu FJ, Yogarathinam LT, Usman AG, Lawal D, Salhi B, Aljundi IH.   +7 more
europepmc   +1 more source