Leveraging machine learning in nursing: innovations, challenges, and ethical insights by Sophie So Wan Yip, Sheng Ning, Niki Yan Ki Wong, Jeffrey Chan, Kei Shing Ng, Bernadette Oi Ting Kwok, Robert L. Anders, Simon Ching Lam

“…In nursing education, ML has improved simulation-based training by facilitating adaptive learning experiences that support continual skill development. …”
Get full text

Machine Learning-Assisted NIR Spectroscopy for Dynamic Monitoring of Leaf Potassium in Korla Fragrant Pear by Mingyang Yu, Weifan Fan, Junkai Zeng, Yang Li, Lanfei Wang, Hao Wang, Feng Han, Jianping Bao

“…Competitive adaptive reweighted sampling (CARS) is then utilized to screen five potassium-sensitive bands, specifically in the regions of 4003.5–4034.35 nm, 4458.62–4562.75 nm, and 5145.15–5249.29 nm, among others, which are associated with O-H stretching vibration and changes in water status. …”
Get full text

Option Pricing Based on Modular Neural Network by Moslem Peymany Foroushani, mohamad ali dehghan dehnavi, Milad Kouhkan

Get full text

Leveraging the integration of bioinformatics and machine learning to uncover common biomarkers and molecular pathways underlying diabetes and nephrolithiasis by Xudong Shen, Xudong Shen, Xudong Shen, Guoxiang Li, Guoxiang Li, Guoxiang Li, Junfeng Yao, Junfeng Yao, Junfeng Yao, Junping Yang, Xiaobo Ding, Xiaobo Ding, Xiaobo Ding, Zongyao Hao, Zongyao Hao, Zongyao Hao, Yan Chen, Yang Chen, Yang Chen, Yang Chen

“…After eliminating batch effects, we performed differential expression analysis and applied weighted gene co-expression network analysis (WGCNA) to investigate associations with 18 forms of cell death. Differentially expressed genes (DEGs) were subsequently analyzed using 10 commonly used machine learning algorithms, generating 101 unique combinations to identify the final DEGs. …”
Get full text

Multiple automated machine-learning prediction models for postoperative reintubation in patients with acute aortic dissection: a multicenter cohort study by Shuyu Wen, Chao Zhang, Junwei Zhang, Ying Zhou, Yin Xu, Minghui Xie, Jinchi Zhang, Zhu Zeng, Long Wu, Weihua Qiao, Xingjian Hu, Xingjian Hu, Nianguo Dong, Nianguo Dong

“…This study aims to employ machine learning algorithms to establish a practical platform for the prediction of reintubation.MethodsA total of 861 patients diagnosed with AAD and undergoing surgical procedures, 688 patients as training and testing cohort from a single center, and 173 patients as validation cohort from four centers were enrolled. …”
Get full text

Lessons from the PROTECT-CH COVID-19 platform trial in care homes by Philip M Bath, Jonathan Ball, Matthew Boyd, Heather Gage, Matthew Glover, Maureen Godfrey, Bruce Guthrie, Jonathan Hewitt, Robert Howard, Thomas Jaki, Edmund Juszczak, Daniel Lasserson, Paul Leighton, Val Leyland, Wei Shen Lim, Pip Logan, Garry Meakin, Alan Montgomery, Reuben Ogollah, Peter Passmore, Philip Quinlan, Caroline Rick, Simon Royal, Susan D Shenkin, Clare Upton, Adam L Gordon

“…Results We initiated the trial including protocol, approvals, insurance, website, database, data algorithms, intervention selection and training materials. …”
Get full text

‘Machine Learning’ multiclassification for stage diagnosis of Alzheimer’s disease utilizing augmented blood gene expression and feature fusion by Manash Sarma, Subarna Chatterjee

“…Abstract Objective The present study explores the classification of Alzheimer’s disease (AD) stages, encompassing cognitive normalcy, Mild Cognitive Impairment (MCI), and AD/Dementia, through the application of Machine Learning (ML) multiclassification algorithms. This investigation utilizes blood gene expression datasets obtained from participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the National Center for Biotechnology Information (NCBI). …”
Get full text

Measurement of Intratumor Heterogeneity and Its Changing Pattern to Predict Response and Recurrence Risk After Neoadjuvant Chemotherapy in Breast Cancer by Mingxi Zhu, Qiong Wu, Xiaochuan Geng, Huaying Xie, Yan Wang, Ziping Wu, Yanping Lin, Liheng Zhou, Shuguang Xu, Yumei Ye, Wenjin Yin, Jia Hua, Jingsong Lu, Yaohui Wang

“…MR images were processed by algorithms to quantify the heterogeneity of tumors. …”
Get full text

Development and Validation of a Cost-Effective Machine Learning Model for Screening Potential Rheumatoid Arthritis in Primary Healthcare Clinics by Wu W, Hu X, Yan L, Li Z, Li B, Chen X, Lin Z, Zeng H, Li C, Mo Y, Wu Y, Wang Q

“…Subsequently, we retrained and validated our proposed model based on two primary healthcare validation cohorts.Results: In experiments, the algorithms achieved over 88% accuracy on training and test sets. …”
Get full text

A novel, rapid, and practical prognostic model for sepsis patients based on dysregulated immune cell lactylation by Chang Li, Mei He, PeiChi Shi, Lu Yao, XiangZhi Fang, XueFeng Li, QiLan Li, XiaoBo Yang, JiQian Xu, You Shang, You Shang

“…Patients were stratified into subgroups using k-means clustering based on lactylation levels. Machine learning algorithms, integrated with pseudotime trajectory reconstruction, were employed to map the temporal dynamics of lactylation. …”
Get full text

Spatial patterns and MRI-based radiomic prediction of high peritumoral tertiary lymphoid structure density in hepatocellular carcinoma: a multicenter study by Juan Chen, Xiong Chen, Kai Fu, Lan Zhou, Shichao Long, Mengsi Li, Linhui Zhong, Aerzuguli Abudulimu, Wenguang Liu, Deng Pan, Ganmian Dai, Yigang Pei, Wenzheng Li

“…Background Tertiary lymphoid structures (TLS) within the tumor microenvironment have been associated with cancer prognosis and therapeutic response. …”
Get full text

Comparative evaluation of machine learning models versus TIMI score in ST-segment-elevation myocardial infarction patients by Mohit D. Gupta, Dixit Goyal, Shekhar Kunal, Manu Kumar Shetty, M.P. Girish, Vishal Batra, Ankit Bansal, Prashant Mishra, Mansavi Shukla, Vanshika Kohli, Akul Chadha, Arisha Fatima, Subrat Muduli, Anubha Gupta, Jamal Yusuf

“…Six ML algorithms (Extra Tree, Random Forest, Multiple Perceptron, CatBoost, Logistic Regression and XGBoost) were used to train and tune the ML model and to determine the predictors of worse outcomes using feature selection. …”
Get full text

Artificial Intelligence–Enabled ECG Screening for LVSD in LBBB by Hak Seung Lee, MD, Sooyeon Lee, MD, Sora Kang, MS, Ga In Han, MS, Ah-Hyun Yoo, MS, Jong-Hwan Jang, PhD, Yong-Yeon Jo, PhD, Jeong Min Son, MD, Min Sung Lee, MD, MS, Joon-myoung Kwon, MD, MS, Kyung-Hee Kim, MD, PhD

“…Background: Left bundle branch block (LBBB) is a common electrocardiogram (ECG) abnormality associated with left ventricular systolic dysfunction (LVSD). …”
Get full text

Construction of a stromal cell-related prognostic signature based on a 101-combination machine learning framework for predicting prognosis and immunotherapy response in triple-nega... by Fanrong Li, Congnan Jin, Yacheng Pan, Zheng Zhang, Liying Wang, Jieqiong Deng, Yifeng Zhou, Yifeng Zhou, Binbin Guo, Shenghua Zhang, Shenghua Zhang

“…A consensus MVP cell-related signature (MVPRS) was developed using 10 machine learning algorithms and 101 model combinations and validated in training and validation cohorts. …”
Get full text

Dynamic and interpretable deep learning model for predicting respiratory failure following cardiac surgery by Man Xu, Hao Liu, Anran Dai, Qilian Tan, Xinlong Zhang, Rui Ding, Chen Chen, Jianjun Zou, Yongjun Li, Yanna Si

“…Feature selection was conducted via the Least Absolute Shrinkage and Selection Operator (LASSO) and Boruta algorithms. Five machine learning models, including logistic regression, multilayer perceptron, extreme gradient boosting, categorical boosting, and deep neural network (DNN), were trained using preoperative and intraoperative variables. …”
Get full text

Machine learning–guided single-cell multiomics uncovers GDF15-driven immunosuppressive niches in NSCLC: A translational framework for overcoming anti-PD-1 resistance by Xianfei Zhang, Zhengxin Yin, Xueyu Chen, Nengchong Zhang, Shengjia Yu, Congcong Zhu, Lianggang Zhu, Liulan Shao, Bin Li, Runsen Jin, Hecheng Li

“…Comparative evaluation of 22 survival algorithms across four NSCLC cohorts (n=156) led to the development of an Accelerated Oblique Random Survival Forest model, which outperformed conventional Cox regression and deep learning methods in predictive accuracy (training C-index=0.864; test C-index=0.748). …”
Get full text

Prediction of Parkinson Disease Using Long-Term, Short-Term Acoustic Features Based on Machine Learning by Mehdi Rashidi, Serena Arima, Andrea Claudio Stetco, Chiara Coppola, Debora Musarò, Marco Greco, Marina Damato, Filomena My, Angela Lupo, Marta Lorenzo, Antonio Danieli, Giuseppe Maruccio, Alberto Argentiero, Andrea Buccoliero, Marcello Dorian Donzella, Michele Maffia

“…<b>Result:</b> Among all the algorithms used in this research, random forest (RF) was the best-performing model, achieving an accuracy of 82.72% with a ROC-AUC score of 89.65%. …”
Get full text

Fibrosis-4plus score: a novel machine learning-based tool for screening high-risk varices in compensated cirrhosis (CHESS2004): an international multicenter study by Bingtian Dong, Ruiling He, Shenghong Ju, Yuping Chen, Ivica Grgurevic, Jianzhong Ma, Ying Guo, Huizhen Fan, Qiang Yan, Chuan Liu, Huixiong Xu, Anita Madir, Kristian Podrug, Jia Wang, Linxue Qian, Zhengzi Geng, Shanghao Liu, Tao Ren, Guo Zhang, Kun Wang, Meiqin Su, Fei Chen, Sumei Ma, Liting Zhang, Zhaowei Tong, Yonghe Zhou, Xin Li, Fanbin He, Hui Huan, Wenjuan Wang, Yunxiao Liang, Juan Tang, Fang Ai, Tingyu Wang, Liyun Zheng, Zhongwei Zhao, Jiansong Ji, Wei Liu, Jiaojiao Xu, Bo Liu, Xuemei Wang, Yao Zhang, Qiong Yan, Hui Liu, Xiaomei Chen, Shuhua Zhang, Yihua Wang, Yang Liu, Li Yin, Yanni Liu, Yanqing Huang, Li Bian, Ping An, Xin Zhang, Shaoting Zhang, Jinhua Shao, Xiangman Zhang, Wei Rao, Chaoxue Zhang, Christoph Frank Dietrich, Won Kim, Xiaolong Qi

Get full text

A novel stemness-related lncRNA signature predicts prognosis, immune infiltration and drug sensitivity of clear cell renal cell carcinoma by Jia Liu, Lin Yao, Yong Yang, Jinchao Ma, Ruijian You, Ziyi Yu, Peng Du

“…Multiple machine learning algorithms were employed to construct a prognostic signature. …”
Get full text

Integrative review of artificial intelligence applications in nursing: education, clinical practice, workload management, and professional perceptions by Rabie Adel El Arab, Omayma Abdulaziz Al Moosa, Mette Sagbakken, Ahmed Ghannam, Fuad H. Abuadas, Joel Somerville, Joel Somerville, Abbas Al Mutair, Abbas Al Mutair, Abbas Al Mutair, Abbas Al Mutair, Abbas Al Mutair

“…These efficiencies allowed nursing teams to devote more time to direct patient care and were associated with reductions in burnout and improved workplace morale.Nursing perceptionsAcross practice settings, nursing students and practicing nurses broadly welcomed AI’s ability to streamline workflows and support decision-making, recognizing its potential to elevate patient care and professional practice.Ethical implicationsSimultaneously, nurses voiced significant ethical concerns—chiefly around safeguarding patient data privacy, mitigating algorithmic bias, and preserving the compassionate, human-centered essence of nursing in an increasingly automated environment.Framework and recommendationsThe Nursing AI Integration Roadmap (NAIIR) was developed, emphasizing transformational education, advanced clinical integration, ethical governance, robust organizational infrastructure, participatory design, and rigorous economic evaluation. …”
Get full text