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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Lightweight Detection Algorithm for Breast-Mass Features in Ultrasound Images by Taojuan Li, Wen Liu, Mingxian Song, Zheng Gu, Ling Hai

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Domain name generation algorithm based on improved Markov chain by QIAN Zhiye, LI Xue, LI Suogang

“…Then, the improved Markov model algorithm was used to analyze the filtered data, and new subdomain names were generated and added to the result set. …”
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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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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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Laryngeal cancer diagnosis based on improved YOLOv8 algorithm by Xin Nie, Xueyan Zhang, Di Wang, Yuankun Liu, Lumin Xing, Wenjian Liu

“…A novel multiscale enhanced convolution module has been introduced to improve the model’s feature extraction capabilities for small-sized targets. …”
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Application of Artificial Intelligence Algorithms in the Field of Antimicrobial Peptide Prediction by QIAN Yuchen, NIE Ting, HUA Yanming, XU Shiying, GUO Sheng, ZHANG Xin, LUO Xiaohu, LIU Yanan

“…Currently, several specialized databases have been established, providing rich resources for algorithmic model training. Furthermore, multi-source bioinformatics data such as genomics, transcriptomics and proteomics are also widely used to predict antimicrobial peptides, with a view to identifying peptides with potential antimicrobial activity more accurately. …”
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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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Electricity Load Forecasting Method Based on the GRA-FEDformer Algorithm by Xin Jin, Tingzhe Pan, Heyang Yu, Zongyi Wang, Wangzhang Cao

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Derivation and external validation of prediction model for hypertensive disorders of pregnancy in twin pregnancies: a retrospective cohort study in southeastern China by Yuting Gao, Na Lin, Shuisen Zheng, Yujuan Chen, Xiaoling Chen

“…Besides, we included twin pregnancies delivered at Fujian Maternity and Child Health Hospital; Women and Children’s Hospital of Xiamen University from January 2020 to December 2021 as temporal validation set and geographical validation set, respectively.Main outcome measures We performed univariate analysis, the least absolute shrinkage and selection operator regression and Boruta algorithm to screen variables. Then, we used multivariate logistic regression to construct a nomogram that predicted the risk of HDP in twin pregnancies. …”
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Bundled assessment to replace on-road test on driving function in stroke patients: a binary classification model via random forest by Lu Huang, Lu Huang, Xin Liu, Jiang Yi, Yu-Wei Jiao, Tian-Qi Zhang, Guang-Yao Zhu, Shu-Yue Yu, Zhong-Liang Liu, Min Gao, Xiao-Qin Duan

“…The subject was classified as either Success or Unsuccess group according to whether they had completed the on-road test. A random forest algorithm was then applied to construct a binary classification model based on the data obtained from the two groups.ResultsCompared to the Unsuccess group, the Success group had higher scores on the OCS scale for “crossing out the intact heart” (p = 0.015) and lower scores for “executive function” (p = 0.009). …”
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Development and validation of a small-sample machine learning model to predict 5–year overall survival in patients with hepatocellular carcinoma by Tingting Jiang, Xingyu Liu, Wencan He, Hepei Li, Xiang Yan, Qian Yu, Shanjun Mao

“…The SVM algorithm demonstrated superior performance and stability in the internal and external validations of the model. …”
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Analysis of E-Commerce Marketing Strategy Based on Xgboost Algorithm by Hong Chen, Wei Wan

“…This paper reviews the current literature on e-commerce marketing and then analyzes the feasibility of precision marketing in e-commerce market in the new media era. In order to screen potential consumers and improve the success rate of precision marketing, this paper establishes a prediction model for precision marketing of bank credit cards. …”
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Blinded Validation of Breath Biomarkers of Lung Cancer, a Potential Ancillary to Chest CT Screening. by Michael Phillips, Thomas L Bauer, Renee N Cataneo, Cassie Lebauer, Mayur Mundada, Harvey I Pass, Naren Ramakrishna, William N Rom, Eric Vallières

“…Outcome modeling: We modeled the expected effects of combining breath biomarkers with chest CT on the sensitivity and specificity of lung cancer screening.…”
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