Genome-wide expression in human whole blood for diagnosis of latent tuberculosis infection: a multicohort research by Fan Jiang, Fan Jiang, Fan Jiang, Yanhua Liu, Linsheng Li, Linsheng Li, Ruizi Ni, Ruizi Ni, Yajing An, Yajing An, Yufeng Li, Yufeng Li, Lingxia Zhang, Wenping Gong

“…A Naive Bayes (NB) model incorporating these two markers demonstrated robust diagnostic performance: training set AUC: median = 0.8572 (inter-quartile range 0.8002, 0.8708), validation AUC = 0.5719 (0.51645, 0.7078), and subgroup AUC = 0.8635 (0.8212, 0.8946).ConclusionOur multicohort analysis established an NB-based diagnostic model utilizing S100A12/S100A8, which maintains diagnostic accuracy across diverse geographic, ethnic, and clinical variables (including HIV co-infection), highlighting its potential for clinical translation in LTBI/ATB differentiation.…”
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High-throughput screening and machine learning classification of van der Waals dielectrics for 2D nanoelectronics by Yuhui Li, Guolin Wan, Yongqian Zhu, Jingyu Yang, Yan-Fang Zhang, Jinbo Pan, Shixuan Du

“…Here, we employed a topology-scale algorithm to screen vdW materials consisting of zero-dimensional (0D), one-dimensional (1D), and 2D motifs from Materials Project database. …”
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Analysis and Prediction of CET4 Scores Based on Data Mining Algorithm by Hongyan Wang

“…In order to detect potential risk graduating students earlier, this paper proposes an appropriate and timely early warning and preschool K-nearest neighbor algorithm classification model. Taking test scores or make-up exams and re-learning as input features, the classification model can effectively predict ordinary students who have not graduated.…”
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Birdsong Recognition Based on Attention Hash Algorithm Combined with Contrastive Loss by WANG Yuwei, CHEN Aibin, ZHOU Guoxiong, ZHANG Zhiqiang

“…Aiming at the problems of length misalignment, redundancy, noise and large intra-class differences in birdsong data collected in the natural environment, an automatic birdsong recognition model composed of a two-stage hash algorithm based on multi- level attention and a lightweight classifier based on fusion contrastive loss is proposed. …”
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Machine learning prediction model with shap interpretation for chronic bronchitis risk assessment based on heavy metal exposure: a nationally representative study by Tiansheng Xia, Kaiyu Han

“…Methods Weighted logistic regression was used to assess the association of 14 blood and urine heavy metals with CB based on nationally representative samples from the 2005–2015 National Health and Nutrition Examination Survey (NHANES). The Boruta algorithm was further applied to screen the characteristic variables and construct 10 ML models. …”
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Cost-effectiveness of advanced hepatic fibrosis screening in individuals with suspected MASLD identified by serologic noninvasive tests by Huiyul Park, Eileen L. Yoon, Mimi Kim, Ji-hyeon Park, Ramsey Cheung, Jeong-Yeon Cho, Hye-Lin Kim, Dae Won Jun

“…We applied a decision tree and Markov model from a healthcare system perspective to estimate life-years, quality-adjusted life-years (QALYs), costs, and the incremental cost-effectiveness ratio (ICER) for screening versus no screening in the United States. …”
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Virtual Screening of Conjugated Polymers for Organic Photovoltaic Devices Using Support Vector Machines and Ensemble Learning by Fang-Chung Chen

“…Additionally, the predictive performance could be further improved by “blending” the results of the SVM and random forest models. The resulting ensemble learning algorithm might open up a new opportunity for more precise, high-throughput virtual screening of conjugated polymers for OPV devices.…”
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Mitochondrial autophagy-related gene signatures associated with myasthenia gravis diagnosis and immunity by Shan Jin, Junbin Yin, Wei Li, Ni Mao

“…Multiple machine learning algorithms were applied to screen and verify the diagnostic genes of intersection genes. …”
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Machine Learning-Assisted NIR Spectroscopy for Dynamic Monitoring of Leaf Potassium in Korla Fragrant Pear by Mingyang Yu, Weifan Fan, Junkai Zeng, Yang Li, Lanfei Wang, Hao Wang, Feng Han, Jianping Bao

“…By measuring leaf potassium content at the fruit setting, expansion, and maturity stages (decreasing from 1.60% at fruit setting to 1.14% at maturity), this study reveals its dynamic change pattern and establishes a high-precision prediction model by combining near-infrared spectroscopy (NIRS) with machine learning algorithms. …”
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Review of Josh Simons’ Book "Algorithms for the People – Democracy in the Age of AI" by Thomas Klikauer

“… Increasingly, artificial intelligence, algorithms and machine learning models guide what Internet users see and read on their screens. …”
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Prediction of hypertensive disorders in pregnant women in the «gray» risk zone following combined first-trimester screening by N. V. Mostova, V. V. Kovalev, E. V. Kudryavtseva

“…Aim: to develop a prognostic model for risk stratification in female patients with borderline to high developing PE risk based on combined first-trimester screening.   …”
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Changed definition of disease and broader screening criteria had little impact on prevalence of gestational diabetes mellitus by Lina Grønvall, Finn Egil Skjeldestad

“…Conclusions The introduction of broader screening criteria and a more liberal case definition increased the population eligible for GDM screening by 45%. …”
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Analysis of imaging differences between high-resolution CT and digital radiography chest films in pneumoconiosis screening by Lijuan LIU, Fenghong WANG

“…HRCT enables systematic observation of the evolution and progression of pneumoconiosis, providing reliable evidence for diagnosis.ObjectiveTo provide reliable evidences for the early screening of pneumoconiosis, By analyzing the imaging difference between HRCT and DR chestfilms in pneumoconiosis screening.MethodsSix casting workers in a casting forging company suspected of early stage of pneumoconiosis through regular occupational health examination screening were recruited , and 64 rows of spiral CT thin layer were scanned and reconstructed by high-resolution bone algorithm. …”
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AI-based Assessment of Risk Factors for Coronary Heart Disease in Patients With Diabetes Mellitus and Construction of a Prediction Model for a Treatment Regimen by Zhen Gao, Qiyuan Bai, Mingyu Wei, Hao Chen, Yan Yan, Jiahao Mao, Xiangzhi Kong, Yang Yu

“…Conclusions: Using machine-learning algorithms, we built a prediction model of a treatment plan for patients with concomitant DM and CHD by integrating patients' information and screened the best feature set containing 15 features, which provides help and strategies to develop the best treatment plan for patients with concomitant DM and CHD.…”
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Identifying USP1 Inhibitors with Allosteric Effect on Its Triple Catalytic Center through Virtual Screening by Jinhong Xu, Yongxia Li, Tao Jiang, Yi Zhu, Jinyan Zhu, Tao Xu, Longji Fang, Zujun Hong, Yuying Jia, Fang Bao

“…In this study, we performed virtual screening on a database containing about 1.37 million molecules using the pharmacophore model, multiple precision molecular docking algorithms, molecular mechanics/generalized born surface area (MM/GBSA), strain energy, and ADMET screening methods. …”
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Low-cost and scalable machine learning model for identifying children and adolescents with poor oral health using survey data: An empirical study in Portugal. by Susana Lavado, Eduardo Costa, Niclas F Sturm, Johannes S Tafferner, Octávio Rodrigues, Pedro Pita Barros, Leid Zejnilovic

“…Such a model could enable scalable and cost-effective screening and targeted interventions, optimizing limited resources to improve oral health outcomes. …”
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A novel prediction model for the prognosis of non-small cell lung cancer with clinical routine laboratory indicators: a machine learning approach by Yuli Wang, Na Mei, Ziyi Zhou, Yuan Fang, Jiacheng Lin, Fanchen Zhao, Zhihong Fang, Yan Li

“…Finally, critical variables in the optimal model were screened based on the interpretable algorithms to build a decision tree to facilitate clinical application. …”
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The Automatable Activity–Based Approach to Complexity Unit Scoring as a task-specific model approach to monetizing outcomes of pathology artificial intelligence solutions by Stavros Pantelakos, Martha Nifora, Georgios Agrogiannis

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Screening of endoplasmic reticulum stress characteristic genes and immune infiltration manifestations in chronic obstructive pulmonary disease by ZHANG Shuang, LUO Chenyang, HE Zhiyi

“…Three machine learning algorithms, LASSO, SVM-RFE, and RF, were used to screen the characteristic genes, and their diagnostic performance was verified and evaluated in the GSE10006. …”
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Development and validation of an early predictive model for hemiplegic shoulder pain: a comparative study of logistic regression, support vector machine, and random forest by Qiang Wu, Qiang Wu, Fang Zhang, Yuchang Fei, Zhenfen Sima, Shanshan Gong, Qifeng Tong, Qingchuan Jiao, Hao Wu, Jianqiu Gong, Jianqiu Gong

“…ObjectiveIn this study, we aim to identify the predictive variables for hemiplegic shoulder pain (HSP) through machine learning algorithms, select the optimal model and predict the occurrence of HSP.MethodsData of 332 stroke patients admitted to a tertiary hospital in Zhejiang Province from January 2022 to January 2023 were collected. …”
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