Results 191 to 200 of about 85,695 (269)

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

Electronic health record use factors linked to efficiency and productivity: an explainable machine learning analysis. [PDF]

JAMIA Open
Li H, Khanna VV, Apathy N, Holmgren AJ, Loza AJ, Melnick ER.   +5 more
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

Comparative machine learning to predict acute kidney injury in traumatic brain injury: a MIMIC-IV cohort with SHAP interpretation. [PDF]

Front Med (Lausanne)
Gu Z, Qian K, Wang X, Li M, Zhang B.
europepmc   +1 more source

Decoding temporal miRNA signatures of semen under in vitro exposure for forensic time since deposition estimation using machine learning‐driven modeling

Interdisciplinary Medicine, EarlyView.
This study develops a novel miRNA‐based framework for estimating the time since deposition of semen stains, combining small RNA sequencing with machine learning. Time‐dependent miRNA modules were identified using Mfuzz clustering and WGCNA, followed by a multi‐stage feature selection pipeline that reduced 261 candidate miRNAs to a minimal 7‐miRNA panel.
Meiming Cai, Qiong Lan, Tong Xie, Qinglin Liu, Ming Zhao, Xiaolian Wu, Xin Shi, Ruonan Shen, Yiman Wu, Chen Mao, Bin Cong, Bofeng Zhu   +11 more
wiley   +1 more source

Correction: Spatial distribution patterns and landslide susceptibility analysis from a global–local perspective along the Zhuzhou-Guangzhou section of the Beijing–Guangzhou railway

Frontiers in Earth Science
Hanxing Liu, Hanxing Liu, Chong Xu, Chong Xu, Chong Xu, Liye Feng, Liye Feng, Peng Wang, Jingjing Sun, Jingjing Sun, Xuewei Zhang, Xuewei Zhang, Juanling Wang, Qihao Sun, Kang Li   +14 more
doaj   +1 more source

Multimodal machine learning for predicting postoperative functional outcomes in surgically treated supratentorial deep intracerebral hemorrhage: a prospective multicenter study. [PDF]

Front Neurol
Cui M, Liu Y, He Q, Xiong W, Liu Y, Xu L, Deng Y, Tan X.   +7 more
europepmc   +1 more source

Magnetocardiography combined with machine learning for pulmonary hypertension detection and improved short‐term risk assessment

Interdisciplinary Medicine, EarlyView.
Magnetocardiography (MCG) enables non‐invasive mapping of cardiac magnetic fields. In this study, an MCG‐based machine learning model detects pulmonary hypertension with robust performance. Furthermore, MCG features may improve the accuracy of short‐term risk assessment.
Yuankun Qi, Jiaqi Liang, Kai Ma, Xiaoyu Hu, Yu Zhang, Xiaole Han, Dong Xu, Xu Zhang, Xiaopei Cui, Min Xiang, Hongyu Zhang, Jiancheng Fang   +11 more
wiley   +1 more source

Machine learning: predicting lymph node metastasis around the entrance point to the recurrent laryngeal nerve in cN0 papillary thyroid carcinoma. [PDF]

Front Endocrinol (Lausanne)
Peng J, Zhou J, Deng Y, Xiao Q, Su X, Deng C.   +5 more
europepmc   +1 more source