Development of a Predictive Model for Metabolic Syndrome Using Noninvasive Data and its Cardiovascular Disease Risk Assessments: Multicohort Validation Study by Jin-Hyun Park, Inyong Jeong, Gang-Jee Ko, Seogsong Jeong, Hwamin Lee

“…Five machine learning algorithms were compared, and the best-performing model was selected based on the area under the receiver operating characteristic curve. …”
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Acoustic Features for Identifying Suicide Risk in Crisis Hotline Callers: Machine Learning Approach by Zhengyuan Su, Huadong Jiang, Ying Yang, Xiangqing Hou, Yanli Su, Li Yang

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Predictive Models Using Machine Learning to Identify Fetal Growth Restriction in Patients With Preeclampsia: Development and Evaluation Study by Qing Hua, Fengchun Yang, Yadan Zhou, Fenglian Shi, Xiaoyan You, Jing Guo, Li Li

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Advanced prediction of rice yield gaps under climate uncertainty using machine learning techniques in Eastern India by Satiprasad Sahoo, Chiranjit Singha, Ajit Govind

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Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews by He-Li Xu, Ting-Ting Gong, Xin-Jian Song, Qian Chen, Qi Bao, Wei Yao, Meng-Meng Xie, Chen Li, Marcin Grzegorzek, Yu Shi, Hong-Zan Sun, Xiao-Han Li, Yu-Hong Zhao, Song Gao, Qi-Jun Wu

“…ResultsIn a comprehensive analysis of 158 included studies evaluating the performance of AI algorithms in noninvasive imaging diagnosis across 8 major human system cancers, the accuracy of the classifiers for central nervous system cancers varied widely (ranging from 48% to 100%). …”
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The Prognostic Significance of Sleep and Circadian Rhythm for Myocardial Infarction Outcomes: Case-Control Study by Wei-Chih Chin, Pao-Hsien Chu, Lung-Sheng Wu, Kuang-Tso Lee, Chen Lin, Chien-Te Ho, Wei-Sheng Yang, I-Hang Chung, Yu-Shu Huang

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Development and Validation of a Machine Learning Model for Early Prediction of Delirium in Intensive Care Units Using Continuous Physiological Data: Retrospective Study by Chanmin Park, Changho Han, Su Kyeong Jang, Hyungjun Kim, Sora Kim, Byung Hee Kang, Kyoungwon Jung, Dukyong Yoon

“…ResultsThe κ coefficient between labels generated by ICU nurses and prospectively verified by qualified researchers was 0.81, indicating reliable CAM-ICU results. …”
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Machine Learning and Deep Learning for Diagnosis of Lumbar Spinal Stenosis: Systematic Review and Meta-Analysis by Tianyi Wang, Ruiyuan Chen, Ning Fan, Lei Zang, Shuo Yuan, Peng Du, Qichao Wu, Aobo Wang, Jian Li, Xiaochuan Kong, Wenyi Zhu

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Predictive Modeling of Acute Respiratory Distress Syndrome Using Machine Learning: Systematic Review and Meta-Analysis by Jinxi Yang, Siyao Zeng, Shanpeng Cui, Junbo Zheng, Hongliang Wang

“…ConclusionsThis study evaluates prediction models constructed using various ML algorithms, with results showing that ML demonstrates high performance in ARDS prediction. …”
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Burden of chronic spontaneous urticaria in Italy through healthcare resource utilization and direct costs: a retrospective analysis of real-world using administrative healthcare da... by Giulia Ronconi, Letizia Dondi, Silvia Calabria, Leonardo Dondi, Irene Dell’Anno, Lucia Casoli, Diletta Valsecchi, Ornella Bonavita, Eustachio Nettis, Nello Martini, Carlo Piccinni

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Identifying low-risk breast cancer patients for axillary biopsy exemption: a multimodal preoperative predictive model by Jiaqi Zhang, Jianing Zhang, Zhihao Liu, Yudong Zhou, Xiaoni Zhao, Yalong Wang, Danni Li, Jinsui Du, Chenglong Duan, Yi Pan, Qi Tian, Feiqian Wang, Ke Wang, Lizhe Zhu, Bin Wang

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Rates of lobar atrophy in asymptomatic MAPT mutation carriers by Qin Chen, Bradley F. Boeve, Matthew Senjem, Nirubol Tosakulwong, Timothy G. Lesnick, Danielle Brushaber, Christina Dheel, Julie Fields, Leah Forsberg, Ralitza Gavrilova, Debra Gearhart, Jonathan Graff‐Radford, Neill R. Graff‐Radford, Clifford R. Jack Jr., David T. Jones, David S. Knopman, Walter K. Kremers, Maria Lapid, Rosa Rademakers, Jeremy Syrjanen, Adam L. Boxer, Howie Rosen, Zbigniew K. Wszolek, Kejal Kantarci, LEFFTDS Consortium

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Machine Learning–Based Prediction of Early Complications Following Surgery for Intestinal Obstruction: Multicenter Retrospective Study by Pinjie Huang, Jirong Yang, Dizhou Zhao, Taojia Ran, Yuheng Luo, Dong Yang, Xueqin Zheng, Shaoli Zhou, Chaojin Chen

“…The best model was validated through 2 independent medical centers, a publicly available perioperative dataset the Informative Surgical Patient dataset for Innovative Research Environment (INSPIRE), and a mixed cohort consisting of the above 3 datasets, involving 50, 66, 48, and 164 cases, respectively. …”
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Linguistic Markers of Pain Communication on X (Formerly Twitter) in US States With High and Low Opioid Mortality: Machine Learning and Semantic Network Analysis by ShinYe Kim, Winson Fu Zun Yang, Zishan Jiwani, Emily Hamm, Shreya Singh

“…Chronic pain, affecting 1 in 5 US adults, is a key contributor to opioid use and misuse. While previous research has explored clinical and behavioral predictors of opioid risk, less attention has been given to large-scale linguistic patterns in public discussions of pain. …”
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Tomografía geoeléctrica: dos ejemplos en la Sabana de Bogotá by Briceno Guarupe Luis Alberto, Sanín Camilo Ernesto

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Classification of Individuals With COVID-19 and Post–COVID-19 Condition and Healthy Controls Using Heart Rate Variability: Machine Learning Study With a Near–Real-Time Monitoring C... by Carlos Alberto Sanches, Andre Felipe Henriques Librantz, Luciana Maria Malosá Sampaio, Peterson Adriano Belan

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CPAP management of COVID-19 respiratory failure: a first quantitative analysis from an inpatient service evaluation by Abdul Ashish, Alison Unsworth, Jane Martindale, Kanishka Kavuri, Luigi Sedda, Martin Farrier

“…This evaluation should stimulate further research questions and larger study designs on the potential benefit of CPAP for COVID-19 infections. …”
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Explainable Machine Learning Model for Predicting Persistent Sepsis-Associated Acute Kidney Injury: Development and Validation Study by Wei Jiang, Yaosheng Zhang, Jiayi Weng, Lin Song, Siqi Liu, Xianghui Li, Shiqi Xu, Keran Shi, Luanluan Li, Chuanqing Zhang, Jing Wang, Quan Yuan, Yongwei Zhang, Jun Shao, Jiangquan Yu, Ruiqiang Zheng

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Diagnostic Performance of Artificial Intelligence–Based Methods for Tuberculosis Detection: Systematic Review by Seng Hansun, Ahmadreza Argha, Ivan Bakhshayeshi, Arya Wicaksana, Hamid Alinejad-Rokny, Greg J Fox, Siaw-Teng Liaw, Branko G Celler, Guy B Marks

“…ObjectiveWe aimed to provide a comprehensive evaluation of AI-based algorithms for TB detection across various data modalities. …”
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Subsurface cavity detection in a karst environment using electrical resistivity (er): a case study from yongweol-ri, South Korea by Farooq Muhammad, et al

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