AI models for the identification of prognostic and predictive biomarkers in lung cancer: a systematic review and meta-analysis by Hind M. AlOsaimi, Aseel M. Alshilash, Layan K. Al-Saif, Jannat M. Bosbait, Roaa S. Albeladi, Dalal R. Almutairi, Alwaleed A. Alhazzaa, Tariq A. Alluqmani, Saud M. Al Qahtani, Sara A. Almohammadi, Razan A. Alamri, Abdullah A. Alkurdi, Waleed K. Aljohani, Raghad H. Alraddadi, Mohammed K. Alshammari

“…AI models, particularly deep learning and machine learning algorithms demonstrated high accuracy in predicting biomarker status. …”
Get full text

Obstructive sleep apnea severity and pathophysiological traits in overlap syndrome: Insights from the SNOOzzzE cohort by Janna Raphelson, Ana Sanchez‐Azofra, Jeremy E. Orr, Gabriela Parra, Lana McGinnis, Alexis Salinas, Steven Luu, Scott A. Sands, Ali Azarbarzin, Robert L. Owens, Jose M. Marin, Atul Malhotra, Christopher N. Schmickl

“…OSA severity and mechanisms differ in OVS versus OSA‐only. Future research should seek to evaluate these differences for their prognostic ability.…”
Get full text

Predictive modeling and interpretative analysis of risks of instability in patients with Myasthenia Gravis requiring intensive care unit admission by Chao-Yang Kuo, Emily Chia-Yu Su, Hsu-Ling Yeh, Jiann-Horng Yeh, Hou-Chang Chiu, Chen-Chih Chung

Get full text

Prognostic factors of disability progression in multiple sclerosis in real life: the OFSEP-high definition (OFSEP-HD) prospective cohort in France by Francis Guillemin, Jonathan Epstein, Pierre Labauge, Christine Lebrun-Frénay, Eric Thouvenot, Laurent Magy, Jerome De Sèze, Pierre Clavelou, Pierre Branger, Alain Créange, Elisabeth Maillart, Thibault Moreau, Hamza Achit, Bruno Stankoff, Emmanuelle Leray, Gilles Edan, Sandra Vukusic, Olivier Heinzlef, Aurélie Ruet, Yohann Foucher, Philippe Cabre, Olivier Casez, Guillaume Mathey, Jonathan Ciron, Eric Berger, Caroline Papeix, Helene Zephir, David Laplaud, Inès Doghri, Amélie Dos Santos, Romain Casey, Fabien Rollot

Get full text

Identification and validation of glycolysis-related diagnostic signatures in diabetic nephropathy: a study based on integrative machine learning and single-cell sequence by Xiaoyin Wu, Xiaoyin Wu, Buyu Guo, Buyu Guo, Xingyu Chang, Xingyu Chang, Yuxuan Yang, Yuxuan Yang, Qianqian Liu, Qianqian Liu, Jiahui Liu, Jiahui Liu, Yichen Yang, Yichen Yang, Kang Zhang, Yumei Ma, Songbo Fu, Songbo Fu, Songbo Fu

Get full text

A mobile app to improve adherence to colorectal cancer screening and post polypectomy surveillance guidelines by María A. Casas, Francisco Schlottmann, Leandro Steinberg, Xavier Bessa, Anna Serradesanferm, Angels Pozo, Sonia Torres, Antoni Castells, Francesc Balaguer, Jaume Grau, Lisandro Pereyra, María Pellisé

Get full text

A multicenter pragmatic implementation study of AI-ECG-based clinical decision support software to identify low LVEF: Clinical trial design and methods by Francisco Lopez-Jimenez, Heather M. Alger, Zachi I. Attia, Barbara Barry, Ranee Chatterjee, Rowena Dolor, Paul A. Friedman, Stephen J. Greene, Jason Greenwood, Vinay Gundurao, Sarah Hackett, Prerak Jain, Anja Kinaszczuk, Ketan Mehta, Jason O'Grady, Ambarish Pandey, Christopher Pullins, Arjun R. Puranik, Mohan Krishna Ranganathan, David Rushlow, Mark Stampehl, Vinayak Subramanian, Kitzner Vassor, Xuan Zhu, Samir Awasthi

Get full text

Evaluating and Forecasting the Probability of Lightning Occurrence in Rasht City by Afsaneh Ghasemi, Jamil Amanollahi

“…Finally, SPSS Modeler software was used to predict occurrence and non-occurrence of lightning in future using by CART, CHAID, C5 trees and Multi Layer Perceptron (MLP), Radial Basis Function (RBF) and Support Vector Machine (SVM). In this research, the training data set contains 70% of the data and testing data set contains 30% of the data. …”
Get full text

Integrative multi-omics analysis reveals the interaction mechanisms between gut microbiota metabolites and ferroptosis in rheumatoid arthritis by Lifang Liang, Lifang Liang, Lifang Liang, Huaguo Liang, Min He, Min He, Min He, Huiling Zhang, Huiling Zhang, Huiling Zhang, Peifeng Ke, Peifeng Ke, Peifeng Ke

Get full text

Predicting hepatocellular carcinoma outcomes and immune therapy response with ATP-dependent chromatin remodeling-related genes, highlighting MORF4L1 as a promising target by Chao Xu, Litao Liang, Guoqing Liu, Yanzhi Feng, Bin Xu, Deming Zhu, Wenbo Jia, Jinyi Wang, Wenhu Zhao, Xiangyu Ling, Yongping Zhou, Wenzhou Ding, Lianbao Kong

“…We utilized data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO), applying machine learning algorithms to develop a prognostic model based on ACRRGs’ expression. …”
Get full text

Implementation of Machine Vision Methods for Cattle Detection and Activity Monitoring by Roman Bumbálek, Tomáš Zoubek, Jean de Dieu Marcel Ufitikirezi, Sandra Nicole Umurungi, Radim Stehlík, Zbyněk Havelka, Radim Kuneš, Petr Bartoš

“…The goal of this research was to implement machine vision algorithms in a cattle stable to detect cattle in stalls and determine their activities. …”
Get full text

Design and Implementation of a Deep Learning-Based Hand Gesture Recognition System for Rehabilitation Internet-of-Things (RIoT) Environments Using MediaPipe by Nurul Hanis Mohd Dhuzuki, Ahmad Anwar Zainuddin, Nur Anis Sofea Kamarul Zaman, Alin Nur Maisarah Ahmad Razmi, Wonderful Shammah Kaitane, Asmarani Ahmad Puzi, Mohd Naqiuddin Johar, Maslina Yazid, Nor Azlin Mohd Nordin, Shahrul Naim Sidek, Hasan Firdaus Mohd Zaki

“…Several limitations, including latency and distance sensitivity, are addressed in this system with edge computing alongside adaptive algorithms. The key contributions of this research are as follows: First, developing a real-time and cost-effective solution for remote stroke rehabilitation. …”
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

Get full text

Interpretable prediction of hospital mortality in bleeding critically ill patients based on machine learning and SHAP by Bingkui Ren, Yuping Zhang, Siying Chen, Jinglong Dai, Junci Chong, Yifei Zhong, Mengkai Deng, Shaobo Jiang, Zhigang Chang

“…Methods In this retrospective cohort study, we derived data from the eICU Collaborative Research Database (eICU-CRD) to develop and evaluate a predictive model. ​…”
Get full text

Combined assessment of stress hyperglycemia ratio and glycemic variability to predict all-cause mortality in critically ill patients with atherosclerotic cardiovascular diseases ac... by Fuxu Wang, Yu Guo, Yuru Tang, Shuangmei Zhao, Kaige Xuan, Zhi Mao, Ruogu Lu, Rongyao Hou, Xiaoyan Zhu

Get full text

Application of machine learning for the analysis of peripheral blood biomarkers in oral mucosal diseases: a cross-sectional study by Huiyu Yao, Zixin Cao, Liangfu Huang, Haojie Pan, Xiaomin Xu, Fucai Sun, Xi Ding, Wan Wu

“…The Random Forest model demonstrated high accuracy (94.68%) in classifying disease groups, emphasizing the potential of machine learning to enhance diagnostic precision in oral mucosal diseases. Future research should focus on validating these findings in larger cohorts and exploring alternative machine-learning algorithms to improve diagnostic accuracy further.…”
Get full text

WOMEN'S ENGAGEMENT IN LAW AND DIGITAL TECHNOLOGIES: by William Mokofe Mokofe

“…By synthesizing existing research and offering insights into future directions, this paper contributes to a deeper understanding of how digital technologies are reshaping the landscape of law and its impact on women. …”
Get full text

Development of machine learning predictive models and risk scoring system for survival in breast cancer patients with type II diabetes: a retrospective cohort study by Claire Chenwen Zhong, Junjie Huang, Zhaojun Li, Yu Jiang, Zehuan Yang, Ziwei Huang, Qi Dou, Yu Li, Martin C.S. Wong

“…Five machine learning algorithms were employed to develop predictive models for the survival of breast cancer patients with T2DM: Cox proportional hazards regression, LASSO Cox regression, boosting, survival tree, and random survival forest (RSF). …”
Get full text

Analisis Kinerja Model Deteksi Objek Yolo, Ssd, dan Faster R-Cnn pada Citra Penglihatan Malam untuk Pengenalan Tindak Kejahatan by Hendriyawan Achmad, Arif Pramudwiatmoko, Muhammad Satrio Gumilang, Bahrul Al Karim, Hadi Wiyono

“…In an effort to address this pressing concern, the present research advocates for the deployment of advanced security technologies, specifically emphasizing the utilization of night vision surveillance cameras in conjunction with machine learning algorithms to proficiently identify klitih-related offenses, particularly during nocturnal hours. …”
Get full text

Machine-Learning Parsimonious Prediction Model for Diagnostic Screening of Severe Hematological Adverse Events in Cancer Patients Treated with PD-1/PD-L1 Inhibitors: Retrospective... by Seok Jun Park, Seungwon Yang, Suhyun Lee, Sung Hwan Joo, Taemin Park, Dong Hyun Kim, Hyeonji Kim, Soyun Park, Jung-Tae Kim, Won Gun Kwack, Sung Wook Kang, Yun-Kyoung Song, Jae Myung Cha, Sang Youl Rhee, Eun Kyoung Chung

Get full text