Synergizing human insight and machine learning: A dual-lens approach to uncovering healthcare research and innovation outcomes by Stijn Horck, Sanne Steens, Jermain Kaminski

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A machine learning approach to assess the climate change impacts on single and dual-axis tracking photovoltaic systems by Udit Mamodiya, Indra Kishor, Priyam Ganguly, Isha Mukherjee, Nithesh Naik

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Multimodal machine learning enables AI chatbot to diagnose ophthalmic diseases and provide high-quality medical responses by Ruiqi Ma, Qian Cheng, Jing Yao, Zhiyu Peng, Mingxu Yan, Jie Lu, Jingjing Liao, Lejin Tian, Wenjun Shu, Yunqiu Zhang, Jinghan Wang, Pengfei Jiang, Weiyi Xia, Xiaofeng Li, Lu Gan, Yue Zhao, Jiang Zhu, Bing Qin, Qin Jiang, Xiawei Wang, Xintong Lin, Haifeng Chen, Weifang Zhu, Dehui Xiang, Baoqing Nie, Jingtao Wang, Jie Guo, Kang Xue, Hongguang Cui, Jinwei Cheng, Xiangjia Zhu, Jiaxu Hong, Fei Shi, Rui Zhang, Xinjian Chen, Chen Zhao

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Utilizing machine learning to predict the risk factors of episiotomy in parturient womenAJOG Global Reports at a Glance by Mojdeh Banaei, PhD, Nasibeh Roozbeh, PhD, Fatemeh Darsareh, PhD, Vahid Mehrnoush, MD, Mohammad Sadegh Vahidi Farashah, PhD, Farideh Montazeri, BSc

“…Objective: The present study used a machine learning model to predict the factors that put women at the risk of having episiotomy using intrapartum data. …”
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Estimating Aboveground Biomass and Carbon Sequestration in Afforestation Areas Using Optical/SAR Data Fusion and Machine Learning by Kashif Khan, Shahid Nawaz Khan, Anwar Ali, Muhammad Fahim Khokhar, Junaid Aziz Khan

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Machine learning-guided integration of fixed and mobile sensors for high resolution urban PM2.5 mapping by Tianshuai Li, Xin Huang, Qingzhu Zhang, Xinfeng Wang, Xianfeng Wang, Anbao Zhu, Zhaolin Wei, Xinyan Wang, Haolin Wang, Jiaqi Chen, Min Li, Qiao Wang, Wenxing Wang

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A Review of OBD-II-Based Machine Learning Applications for Sustainable, Efficient, Secure, and Safe Vehicle Driving by Emmanouel T. Michailidis, Antigoni Panagiotopoulou, Andreas Papadakis

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Screening and Validation of Potential Biomarkers of Immune Cells in Childhood Asthma Patients via Mendelian Randomization and Machine Learning by Zhang Y, Hai Y, Song B, Xu J, Cao L, Yasen R, Xu W, Zhang J, Hu J

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Smartphone Video Imaging Combined with Machine Learning: A Cost-Effective Method for Authenticating Whey Protein Supplements by Xuan Tang, Wenjiao Du, Weiran Song, Weilun Gu, Xiangzeng Kong

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Exploring the link between the ZJU index and sarcopenia in adults aged 20–59 using NHANES and machine learning by Huan Chen, Ning Du, Hong Xiao, Zhao Wang

“…Subgroup analysis showed notable interactions with gender and diabetes (p < 0.05). Machine learning models consistently ranked ZJU, education level, and race as the most influential predictors of sarcopenia, emphasizing the interplay between metabolic health and socioeconomic factors. …”
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Identifying Compton-thick Active Galactic Nuclei with a Machine Learning Algorithm in Chandra Deep Field-South by Rui Zhang, Xiaotong Guo, Qiusheng Gu, Guanwen Fang, Jun Xu, Hai-Cheng Feng, Yongyun Chen, Rui Li, Nan Ding, Hongtao Wang

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Implemented machine learning tools to inform decision-making for patient care in hospital settings: a scoping review by Sharon E Straus, Andrea C Tricco, Sonia M Thomas, P Alison Paprica, Vera Nincic, Marco Ghassemi, Amanda Parker, Areej Hezam, Charmalee Harris, Orna Fennelly, Jessie McGowan

“…Data were summarised descriptively using simple content analysis.Setting Hospital setting.Participant Any type of clinician caring for any type of patient.Intervention Machine learning tools used by clinicians to inform decision-making for patient care, such as AI-based computerised decision support systems or “‘model-based’” decision support systems.Primary and secondary outcome measures Patient and study characteristics, as well as intervention characteristics including the type of machine learning tool, implementation strategies, target population. …”
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Post-Anesthesia Care Unit (PACU) readiness predictions using machine learning: a comparative study of algorithms by Shahnam Sedigh Maroufi, Maryam Soleimani Movahed, Azar Ejmalian, Maryam Sarkhosh, Ali Behmanesh

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Identification of aging-related biomarkers for intervertebral disc degeneration in whole blood samples based on bioinformatics and machine learning by Zi-hang Li, Shi-pian Li, Shi-pian Li, Ya-hao Li, Yu-cheng Wang, Zhen-yu Tang, Kai-yang Xu, Xiao-rong Li, Zhen Tan, Jiao-yi Pan, Jin-tao Liu, Hong Jiang, Zhi-jia Ma, Yu-xiang Dai, Yu-xiang Dai, Yu-xiang Dai, Peng-fei Yu

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A machine learning framework for predicting cognitive impairment in aging populations using urinary metal and demographic data by Fengchun Ren, Xiao Zhao, Qin Yang, Huaqiang Liao, Yudong Zhang, Xuemei Liu

“…Cognitive status was classified using data-driven quartile thresholds on the Digit Symbol Substitution Test, CERAD Word-Learning Test, and Animal Fluency tests. Six machine learning algorithms were trained and evaluated using sensitivity (SN), specificity (SP), accuracy (ACC), Matthews correlation coefficient (MCC) and AUC.ResultsThe eXtreme gradient boosting (XGBoost) model demonstrated superior performance across all metrics (SN = 0.78, SP = 0.84, ACC = 0.81, MCC = 0.62, AUC = 0.90), and was selected for subsequent interpretation. …”
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Deciphering the complex links between inflammatory bowel diseases and NAFLD through advanced statistical and machine learning analysis by Deepak Kumar, Brijesh Bakariya, Chaman Verma, Zoltán Illés

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Machine-Learning-Powered Information Systems: A Systematic Literature Review for Developing Multi-Objective Healthcare Management by Maryam Bagheri, Mohsen Bagheritabar, Sohila Alizadeh, Mohammad (Sam) Salemizadeh Parizi, Parisa Matoufinia, Yang Luo

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Leveraging Machine Learning for Pediatric Appendicitis Diagnosis: A Retrospective Study Integrating Clinical, Laboratory, and Imaging Data by Mahdi Navaei, Zohre Doogchi, Fatemeh Gholami, Moein Kermanizadeh Tavakoli

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PassAI: An Explainable Machine Learning Framework for Predicting Soccer Pass Outcomes Using Multimodal Match Data by Ryota Takamido, Jun Ota, Hiroki Nakamoto

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An annotated morphological dataset for Uzbek word forms: Towards rule-based and machine learning approachesMendeley Data by Nilufar Abdurakhmonova, Raima Shirinova, Rano Sayfullayeva, Davlatyor Mengliev, Bahodir Ibragimov, Manzura Ernazarova

“…Two morphological analysis approaches were implemented and compared: a user-defined rule-based stemming algorithm and a conditional random fields (CRF)-based machine learning model. Additionally, comprehensive genre testing was conducted on legal, political-economic, and educational texts to assess generalizability. …”
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