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Prevalence of Multiple Chronic Conditions Among Adults in the All of Us Research Program: Exploratory Analysis

Xintong Li, Caitlin Dreisbach, Carolina M Gustafson, Komal Patel Murali, Theresa A Koleck

JMIR Form Res 2025;9:e69138

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Machine Learning Model for Predicting Coronary Heart Disease Risk: Development and Validation Using Insights From a Japanese Population–Based Study

Thien Vu, Yoshihiro Kokubo, Mai Inoue, Masaki Yamamoto, Attayeb Mohsen, Agustin Martin-Morales, Research Dawadi, Takao Inoue, Jie Ting Tay, Mari Yoshizaki, Naoki Watanabe, Yuki Kuriya, Chisa Matsumoto, Ahmed Arafa, Yoko M Nakao, Yuka Kato, Masayuki Teramoto, Michihiro Araki

JMIR Cardio 2025;9:e68066

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Nonpharmacological Multimodal Interventions for Cognitive Functions in Older Adults With Mild Cognitive Impairment: Scoping Review

Reference 12: Sovremennye tendentsii v épidemiologii dementsii i vedenii patsientov s kognitivnymi narusheniiami

Raffy Chi-Fung Chan, Joson Hao-Shen Zhou, Yuan Cao, Kenneth Lo, Peter Hiu-Fung Ng, David Ho-Keung Shum, Arnold Yu-Lok Wong

JMIR Aging 2025;8:e70291

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Peer Review of “The Order in Speech Disorder: A Scoping Review of State of the Art Machine Learning Methods for Clinical Speech Classification (Preprint)”

Vanessa Fairhurst, Sylvester Sakilay, Randa Salah Gomaa Mahmoud, Shailee Rasania, J Moonga, Toba Isaac Olatoye, Rameshwari Prasad, Prasakthi Venkatesan, Vasco Medeiros, Uday Kumar Chalwadi

JMIRx Med 2025;6:e76836

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Population-Wide Depression Incidence Forecasting Comparing Autoregressive Integrated Moving Average and Vector Autoregressive Integrated Moving Average to Temporal Fusion Transformers: Longitudinal Observational Study

Deliang Yang, Yiyi Tang, Vivien Kin Yi Chan, Qiwen Fang, Sandra Sau Man Chan, Hao Luo, Ian Chi Kei Wong, Huang-Tz Ou, Esther Wai Yin Chan, David Makram Bishai, Yingyao Chen, Martin Knapp, Mark Jit, Dawn Craig, Xue Li

J Med Internet Res 2025;27:e67156

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Clinical Value of ChatGPT for Epilepsy Presurgical Decision-Making: Systematic Evaluation of Seizure Semiology Interpretation

An NPIR close to 0 indicates that Chat GPT-4’s interpretations often include at least one correctly identified region but may also involve one misleading region. Given that all papers used to compile the publicly sourced database are available online, some may have been included in Chat GPT’s training corpus, potentially making the evaluation results less objective and convincing. To address this concern, we used a database from a private source for external validation of Chat GPT’s performance.

Yaxi Luo, Meng Jiao, Neel Fotedar, Jun-En Ding, Ioannis Karakis, Vikram R Rao, Melissa Asmar, Xiaochen Xian, Orwa Aboud, Yuxin Wen, Jack J Lin, Fang-Ming Hung, Hai Sun, Felix Rosenow, Feng Liu