Artificial intelligence in primary aldosteronism: current achievements and future challenges by Yisi Xu, Benjin Liu, Xuqi Huang, Xudong Guo, Ning Suo, Shaobo Jiang, Hanbo Wang

“…Recent advances in artificial intelligence (AI) are reshaping the diagnostic and therapeutic of primary aldosteronism (PA). For screening, machine learning models integrate multidimensional data to improve the efficiency of PA detection, facilitating large-scale population screening. …”
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Assessment of food toxicology by Alexander Gosslau

“…Integration of food toxicology data obtained throughout biochemical and cell-based in vitro, animal in vivo and human clinical settings has enabled the establishment of alternative, highly predictable in silico models. These systems utilize a combination of complex in vitro cell-based models with computer-based algorithms. …”
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Prediction of recurrence after surgery for pituitary adenoma using machine learning- based models: systematic review and meta-analysis by Ibrahim Mohammadzadeh, Bardia Hajikarimloo, Behnaz Niroomand, Nasira Faizi, Pooya Eini, Mohammad Amin Habibi, Alireza Mohseni, Mohammadmahdi Sabahi, Abdulrahman Albakr, Michael Karsy, Hamid Borghei-Razavi

“…For the comparison between Logistic Regression (LR) and non-LR algorithms, LR-based algorithms exhibited numerically higher AUC and sensitivity; however, these differences were not statistically significant. …”
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Identification of hub immune-related genes and construction of predictive models for systemic lupus erythematosus by bioinformatics combined with machine learning by Su Zhang, Weitao Hu, Yuchao Tang, Hongjie Lin, Xiaoqing Chen

“…Three machine learning algorithms were applied to DE-IRGs to screen for hub DE-IRGs. …”
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Development and validation of machine learning-based risk prediction models for ICU-acquired weakness: a prospective cohort study by Yimei Zhang, Yu Wang, Jingran Yang, Qinglan Li, Min Zhou, Jiafei Lu, Qiulan Hu, Fang Ma

“…Eighteen features screened through a previous umbrella review informed the models. …”
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Identification of developmental and reproductive toxicity of biocides in consumer products using ToxCast bioassays data and machine learning models by Donghyeon Kim, Siyeol Ahn, Jinhee Choi

“…This study aimed to identify ToxCast bioassays relevant to DART and develop machine learning models to screen biocides in consumer products for their DART potential. …”
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Construction and analysis of a prognostic risk scoring model for gastric cancer anoikis-related genes based on LASSO regression by Ai CHEN, Xiaowei CHEN, Yanan WANG, Xiaobing SHEN

“…ResultsSix key ARGs (VCAN, FEN1, BRIP1, CNTN1, P3H2, DUSP1) were screened out based on LASSO regression analysis, and a prognostic risk scoring model was constructed. …”
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Construction and Validation of a Hospital Mortality Risk Model for Advanced Elderly Patients with Heart Failure Based on Machine Learning by Shang S, Wei M, Lv H, Liang X, Lu Y, Tang B

“…Shuai Shang,1,2,* Meng Wei,1,2,* Huasheng Lv,1,2,* Xiaoyan Liang,1,2 Yanmei Lu,1,2 Baopeng Tang1,2 1Department of Cardiac Pacing and Electrophysiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, People’s Republic of China; 2Xinjiang Key Laboratory of Cardiac Electrophysiology and Remodeling, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, People’s Republic of China*These authors contributed equally to this workCorrespondence: Baopeng Tang, Department of Cardiac Pacing and Electrophysiology, Xinjiang Key Laboratory of Cardiac Electrophysiology and Remodeling, The First Affiliated Hospital of Xinjiang Medical University, No. 137, South Liyushan Road, Xinshi Zone, Urumqi, People’s Republic of China, Email tangbaopeng1111@163.com Yanmei Lu, Department of Cardiac Pacing and Electrophysiology, Xinjiang Key Laboratory of Cardiac Electrophysiology and Remodeling, The First Affiliated Hospital of Xinjiang Medical University, No. 137, South Liyushan Road, Xinshi Zone, Urumqi, People’s Republic of China, Email gracy@189.cnPurpose: This study aimed to develop and validate a model based on machine learning algorithms to predict the risk of in-hospital death among advanced elderly patients with Heart Failure (HF).Methods: A total of 4580 advanced elderly patients who were admitted to the hospital and diagnosed with HF from May 2012 to September 2023 were included in this study, among whom 552 cases (12.5%) died. …”
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Learning from the machine: is diabetes in adults predicted by lifestyle variables? A retrospective predictive modelling study of NHANES 2007–2018 by Efrain Riveros Perez, Bibiana Avella-Molano

“…This study is innovative in its integration of machine learning algorithms to predict type 2 diabetes based solely on non-invasive, easily accessible lifestyle and anthropometric variables, demonstrating the potential of data-driven models for early risk assessment without requiring laboratory tests. …”
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Developing a logistic regression model to predict spontaneous preterm birth from maternal socio-demographic and obstetric history at initial pregnancy registration by Brenda F. Narice, Mariam Labib, Mengxiao Wang, Victoria Byrne, Joanna Shepherd, Z. Q. Lang, Dilly OC Anumba

“…Abstract Background Current predictive machine learning techniques for spontaneous preterm birth heavily rely on a history of previous preterm birth and/or costly techniques such as fetal fibronectin and ultrasound measurement of cervical length to the disadvantage of those considered at low risk and/or those who have no access to more expensive screening tools. Aims and objectives We aimed to develop a predictive model for spontaneous preterm delivery < 37 weeks using socio-demographic and clinical data readily available at booking -an approach which could be suitable for all women regardless of their previous obstetric history. …”
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Machine learning-assisted multi-dimensional transcriptomic analysis of cytoskeleton-related molecules and their relationship with prognosis in hepatocellular carcinoma by Yuxuan Li, Mingbo Cao, Xiaorui Su, Gaoyuan Yang, Yupeng Ren, Zhiwei He, Zheng Shi, Ziyi Hu, Guirong Liang, Qi Zhang, Zhicheng Yao, Meihai Deng

“…In this study, transcriptomic data from the TCGA-LIHC dataset were used to identify differentially expressed cytoskeleton-related genes associated with overall survival (OS). Prognostic models were constructed using LASSO regression and random forest algorithms, and validated in two independent cohorts (ICGC LIRI-JP and CHCC-HBV). …”
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An early lung cancer diagnosis model for non-smokers incorporating ct imaging analysis and circulating genetically abnormal cells (CACs) by Ran Ni, Yongjie Huang, Lei Wang, Hongjie Chen, Guorui Zhang, Yali Yu, Yinglan Kuang, Yuyan Tang, Xing Lu, Hong Liu

“…Five artificial intelligence (AI) algorithms were used to build two kinds of models and identify which one was better at diagnosing non-smoking pulmonary nodules patients. …”
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Hybrid closed-loop systems for managing blood glucose levels in type 1 diabetes: a systematic review and economic modelling by Asra Asgharzadeh, Mubarak Patel, Martin Connock, Sara Damery, Iman Ghosh, Mary Jordan, Karoline Freeman, Anna Brown, Rachel Court, Sharin Baldwin, Fatai Ogunlayi, Chris Stinton, Ewen Cummins, Lena Al-Khudairy

“…The studies’ authors clearly stated their research question, the viewpoint of their analyses and their modelling objectives. Studies that used the iQVIA model described the model as one with a complex semi-Markov model structure with interdependent sub-models, so more thorough, easier access to its reported features would be of benefit to the intended audience. …”
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Machine learning models to predict osteoporosis in patients with chronic kidney disease stage 3–5 and end-stage kidney disease by Chia-Tien Hsu, Chin-Yin Huang, Cheng-Hsu Chen, Ya-Lian Deng, Shih-Yi Lin, Ming-Ju Wu

“…Calibration and decision curve analyses further demonstrated the reliability and applicability of the ANN model. The ANN model demonstrated the potential for clinical implementation in screening high-risk patients for osteoporosis.…”
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PLOD3 as a novel oncogene in prognostic and immune infiltration risk model based on multi-machine learning in cervical cancer by Lingling Qiu, Xiuchai Qiu, Xiaoyi Yang

“…We identified 112 key metabolic genes, which were used to construct and validate a prognostic model through various machine learning algorithms. …”
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The Effect of Family-Centered Empowerment Model Based on Multimedia Education on the Quality of Life of Children Aged 8-12 Years with Thalassemia by Ayoub Mohamadian, Ali Moeini, Mahnaz Sanjari, Zahra Abdullahzade

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Application of iLogic technology in Autodesk inventor to create parametric 3D-model of a gear wheel and conduct research by Petrakova E.A., Samoilova A.S.

“…The article presents an algorithm and tools for creating controlled 3D models using iLogic on the example of a 3D model of a gear wheel. …”
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Analysis of immune status and prognostic model incorporating lactate metabolism and immune-related genes in clear cell renal cell carcinoma by Jun Wu, Yuqian Wu, Yefeng Sun, Jianhang You, Wenjie Zhang, Tao Zhao

“…The Cox proportional hazards regression model and the LASSO algorithm were combined to screen the core genes related to prognosis. …”
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Utilizing SMOTE-TomekLink and machine learning to construct a predictive model for elderly medical and daily care services demand by Guangmei Yang, Guangdong Wang, Leping Wan, Xinle Wang, Yan He

“…The LightGBM algorithm emerged as the superior prediction model for estimating the service needs of the elderly. …”
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Multiple automated machine-learning prediction models for postoperative reintubation in patients with acute aortic dissection: a multicenter cohort study by Shuyu Wen, Chao Zhang, Junwei Zhang, Ying Zhou, Yin Xu, Minghui Xie, Jinchi Zhang, Zhu Zeng, Long Wu, Weihua Qiao, Xingjian Hu, Xingjian Hu, Nianguo Dong, Nianguo Dong

“…The least absolute shrinkage and selection operator (LASSO) was used for screening risk variables associated with reintubation for subsequent model construction. …”
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