Determining optimal strategies for primary prevention of cardiovascular disease: a synopsis of an evidence synthesis study by Olalekan A Uthman, Lena Al-Khudairy, Chidozie Nduka, Rachel Court, Jodie Enderby, Seun Anjorin, Hema Mistry, G J Melendez-Torres, Sian Taylor-Phillips, Aileen Clarke

“…An umbrella review summarised evidence from 95 systematic reviews. A machine learning study developed a parallel Convolutional Neural Network algorithm with 96.4% recall and 99.1% precision for study screening. …”
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Correlation between metformin use and mortality in acute respiratory failure: a retrospective ICU cohort study by Yunlin Yang, Jinfeng Liu, Yi Hou, Yuxun Wei, Liang Huang, Liang Huang, Wei Wei

“…Propensity score matching (PSM) and machine learning algorithms were used for confounder adjustment and feature selection.ResultsAfter PSM, 1,429 patients with ARF were included (374 metformin users; 1,055 non-users). …”
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Enhancing Attention Network Spatiotemporal Dynamics for Motor Rehabilitation in Parkinson’s Disease by Guangying Pei, Mengxuan Hu, Jian Ouyang, Zhaohui Jin, Kexin Wang, Detao Meng, Yixuan Wang, Keke Chen, Li Wang, Li-Zhi Cao, Shintaro Funahashi, Tianyi Yan, Boyan Fang

“…The identified brain spatiotemporal neural markers were validated using machine learning models to assess the efficacy of MIRT in motor rehabilitation for PD patients, achieving an average accuracy rate of 86%. …”
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Prenatal exposure to criteria air pollution and traffic-related air toxics and risk of autism spectrum disorder: A population-based cohort study of California births (1990–2018) by Karl O’Sharkey, Sanjali Mitra, Ting Chow, Seung-a Paik, Laura Thompson, Jason Su, Myles Cockburn, Beate Ritz

“…Methods: Utilizing CA Department of Public Health birth registry data from 1990 to 2018, linked with ASD diagnoses from the CA Department of Developmental Services (n = 13,591,003 children; ASD cases = 138,460, identified from birth year through 2022, allowing for a follow-up ranging from a minimum of 4 to a maximum of 32 years) we assessed prenatal exposure to PM2.5, NO2, O3, and six traffic-related air toxics (benzene, 1,3-butadiene, chromium, lead, nickel, zinc) using machine learning-enhanced land-use regression models. …”
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