Results 191 to 200 of about 66,452 (290)

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

A Machine Learning Model Based on Blood Indices for the Differential Diagnosis of Colorectal Cancer and Colorectal Polyps

iLABMED, EarlyView.
This diagram systematically sorts out the complete data processing pipeline, clearly presenting the key links and logical connections from raw data collection, preprocessing (including cleaning, deduplication, format standardization, etc.), and core computational analysis to the result output.
Wenhui Yan, Lin Zhu, Hongming Wei, Jie Feng, Yanhong Gao   +4 more
wiley   +1 more source

Construction and Validation of a Preoperative Surgical Difficulty Prediction and Risk Stratification System for Posterior Spinal Deformity Correction Surgery Based on Machine Learning ‐ Multicenter Cohort Study

iMetaMed, EarlyView.
A web calculator, trained on multicenter data with seven Boruta‐selected preoperative features, predicts prolonged operative time for posterior spinal deformity correction to enable individualized planning and optimized operating‐room resources. ABSTRACT Operative duration reflects surgical complexity and is valuable for perioperative planning.
Chan Xu, Jialiang Zheng, Chengliang Yin, Runmin Li, Xubin Quan, Binghao Chen, Zhaohui Hu, Tongqing Luo, Hao Jiang, Xuewen Hou, Qizhong Xu, Weiheng He, Liang Chen, Anfa Chen, Xin Zhou, Wei Huang, Sen Gao, Jiali Yu, Zhi‐Ri Tang, Yusi Zhang, Kai Wang, Shi‐Nan Wu, Wencai Liu, Xiangdong Xi, Haizhen Ding, Qiang Liu, Haopeng Luan, Wenle Li   +27 more
wiley   +1 more source

Recent advances in data‐driven and artificial intelligence‐integrated perovskite solar cells: From design to self‐driving laboratories

InfoMat, EarlyView.
To address the limitations of conventional trial‐and‐error approaches, perovskite solar cell research is shifting toward a new paradigm that utilizes datasets and AI. This review examines the fundamental elements of data‐driven and AI‐integrated research: data platforms, AI methodologies, and self‐driving laboratories, demonstrating how their ...
Jaehee Lee, Juhwan Noh, Hyunjun Lee, Yea‐Lee Lee, Jino Im, Jangwon Seo   +5 more
wiley   +1 more source

Enhancing Omics Analyses Through Coalitional Games and Shapley Values. [PDF]

Methods Protoc
Vargas E, de la Torre I, Esteban FJ.
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

Interpretability of an FDA-authorized AI/ML sepsis diagnostic tool improved by SHAP values. [PDF]

JAMIA Open
Watson GL, Staples G, Carver R, Bhargava A, López-Espina C, Schmalz L, Ali F, Antkowiak PS, Azad S, Berghea R, Chawla L, Crisp M, Dagan A, Davila F, Davila H, DeMarco C, Doodlesack A, Espinosa A, Evans NS, Ezekiel C, Friederich A, Gosai F, Halalau A, Iyer K, Kravitz MS, Kurtzman N, Lee JH, Maddens N, Malkani R, Mayer S, Oke V, Palagiri AV, Patel R, Raghavakurup L, Raouf S, Reseland E, Sadaka F, Sarma D, Smith S, Shvilkina T, Sims MD, Singh S, Stenson BA, Syed A, Tafa M, Thomas K, Zhao SD, Zhu R, Bashir R, Reddy B, Shapiro NI.   +50 more
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

Explainable artificial intelligence for molecular design in pharmaceutical research. [PDF]

Chem Sci
Lamens A, Bajorath J.
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