Development and validation of a risk prediction model for depression in patients with chronic obstructive pulmonary disease by Tong Feng, PeiPei Li, Ran Duan, Zhi Jin

“…Objective This study aimed to develop a machine learning-based model to predict depression risk in COPD patients, utilizing interpretable features from clinical and demographic data to support early intervention. …”
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Airfoil Optimization Design of Vertical-Axis Wind Turbine Based on Kriging Surrogate Model and MIGA by Quan Wang, Zhaogang Zhang

“…In response to this challenge, this study constructed a collaborative optimization framework based on the Kriging surrogate model and the multi-island genetic algorithm (MIGA). …”
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Artificial Intelligence and Machine Learning Models for Predicting Drug-Induced Kidney Injury in Small Molecules by Mohan Rao, Vahid Nassiri, Sanjay Srivastava, Amy Yang, Satjit Brar, Eric McDuffie, Clifford Sachs

“…Machine learning (ML) models were developed using four algorithms: Ridge Logistic Regression (RLR), Support Vector Machine (SVM), Random Forest (RF), and Neural Network (NN). …”
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A warning model for predicting patient admissions to the intensive care unit (ICU) following surgery by Li Li, Hongye He, Linjun Xiang, Yongxiang Wang

“…LASSO regression and random forest algorithms were used to screen clinical variables related to postoperative ICU admission. …”
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New Approaches to AI Methods for Screening Cardiomegaly on Chest Radiographs by Patrycja S. Matusik, Zbisław Tabor, Iwona Kucybała, Jarosław D. Jarczewski, Tadeusz J. Popiela

“…Conclusion: The use of AI may optimize the screening process for cardiomegaly on CXRs. Future studies should focus on improving the accuracy of AI algorithms and on assessing the usefulness both of CTR and TCD measurements in screening for cardiomegaly.…”
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Machine learning-based prognostic prediction model of pneumonia-associated acute respiratory distress syndrome by Jing Lv, Juan Chen, Meijun Liu, Xue Dai, Wang Deng

“…The AUC value, AP value, accuracy, sensitivity, specificity, Brier score, and F 1 score were used to evaluate the performance of the models and pick the optimal model. Finally, the SHAP feature importance map was drawn to explain the optimal model.Results10 key variables, namely LAR, Lac, pH, age, PO2/FiO2, ALB, BMI, TP, PT, DBIL were screened using the filtration method. …”
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An Updated Systematic Review on Asthma Exacerbation Risk Prediction Models Between 2017 and 2023: Risk of Bias and Applicability by Liu A, Zhang Y, Yadav CP, Chen W

“…We then applied the Prediction Risk of Bias Assessment tool (PROBAST) to assess the risk of bias and applicability of the included models.Results: Of 415 studies screened, 10 met eligibility criteria, comprising 41 prediction models. …”
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Deep Learning Classification of Systemic Sclerosis Skin Using the MobileNetV2 Model by Metin Akay, Yong Du, Cheryl L. Sershen, Minghua Wu, Ting Y. Chen, Shervin Assassi, Chandra Mohan, Yasemin M. Akay

“…We also utilized the MobileNetV2 model to analyze an additional dataset of images and classified them as normal, early (mid and moderate) SSc or late (severe) SSc skin images. …”
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A deep learning model to predict Ki-67 positivity in oral squamous cell carcinoma by Francesco Martino, Gennaro Ilardi, Silvia Varricchio, Daniela Russo, Rosa Maria Di Crescenzo, Stefania Staibano, Francesco Merolla

“…Aside from classification, detection, and segmentation models, predictive models are gaining traction since they can impact diagnostic processes and laboratory activity, lowering consumable usage and turnaround time. …”
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Machine learning-based risk prediction model for pertussis in children: a multicenter retrospective study by Juan Xie, Run-wei Ma, Yu-jing Feng, Yuan Qiao, Hong-yan Zhu, Xing-ping Tao, Wen-juan Chen, Cong-yun Liu, Tan Li, Kai Liu, Li-ming Cheng

“…The model was constructed using machine learning techniques based on multicenter data and screened for key features. …”
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Screening biomarkers related to cholesterol metabolism in osteoarthritis based on transcriptomics by ChenDeng Lao, Wei Wei, JianWen Cheng, ShiJie Liao, XiaoLin Luo, Qian Huang, HengZhen Huang, JinMin Zhao

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Research Progress in the Screening of Antimicrobial Substances Based on Machine Learning by HOU Jiangxia, JIANG Jinhui, WANG Chenxin, WANG Lan, SHI Liu, WU Wenjin, GUO Xiaojia, CHEN Sheng, CHEN Lang, CAO Feng, SUN Li, ZHOU Zhi

“…As a branch of artificial intelligence, machine learning algorithms have demonstrated exceptional capabilities in processing large-scale data, feature extraction, and model optimization, leading to their increasing application in the screening of antimicrobial substances. …”
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The Effect of Extended Smartphone Screen Time on Continuous Partial Attention by M. Fırat

“…Students attributed this finding to hypnotic algorithms, distracting redundancy, marketing and advertising, passive receiver mode, short video flow, and surprising content. …”
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Machine learning-based coronary heart disease diagnosis model for type 2 diabetes patients by Yingxi Chen, Chunyu Wang, Chunyu Wang, Xiaozhu Liu, Minjie Duan, Tianyu Xiang, Haodong Huang, Haodong Huang

“…Five machine learning algorithms, including Logistic regression, Support Vector Machine (SVM), Random Forest (RF), eXtreme gradient boosting (XgBoost), and Light Gradient Boosting Machine (LightGBM), were selected for modeling. …”
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A Predictive Model for Secondary Posttonsillectomy Hemorrhage in Pediatric Patients: An 8‐Year Retrospective Study by Yuting Ge, Wenchuan Chang, Lixiao Xie, Yan Gao, Yue Xu, Huie Zhu

“…Univariate logistic regression analysis was used to screen features. Multivariate logistic regression and seven machine learning algorithms were used to construct predictive models. …”
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Practical applications of methods to incorporate patient preferences into medical decision models: a scoping review by Jakub Fusiak, Kousha Sarpari, Inger Ma, Ulrich Mansmann, Verena S. Hoffmann

“…Abstract Background Algorithms and models increasingly support clinical and shared decision-making. …”
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Accurate prediction of mediolateral episiotomy risk during labor: development and verification of an artificial intelligence model by Tingting Hu, Liheng Zhao, Xueling Zhao, Lin He, Xiaoli Zhong, Zhe Yin, Junjie Chen, Yanting Han, Ka Li

“…Results Twenty eight factors influencing mediolateral episiotomy were screened. The model evaluation results showed that the SVM model has the best prediction ability among the six models, with an accuracy of 0.793, a recall rate of 0.981, a precision rate of 0.790, and a F1 value of 0.875. …”
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Novel exosome-associated LncRNA model predicts colorectal cancer prognosis and drug response by Chi Zhou, Qian Qiu, Xinyu Liu, Tiantian Zhang, Leilei Liang, Yihang Yuan, Yufo Chen, Weijie Sun

“…Next, we further provide colony formation assay, Transwell assay and xenograft models to understand the carcinogenic effect of MIR4713HG. …”
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Development of an ensemble prediction model for acute graft-versus-host disease in allogeneic transplantation based on machine learning by Lin Song, Xingwei Wu, Mengjia Xu, Ling Xue, Xun Yu, Zongqi Cheng, Chenrong Huang, Liyan Miao

“…Then fifteen algorithms were used to establish models, and an ensemble model was established through soft voting based on the top five performance algorithms. …”
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Artificial Intelligence in Cardiovascular Diagnosis: Innovations and Impact on Disease Screenings by Amber Ahmad, Sahil Ahmad, Rida Ahmad, Jahnavi Bodi, Abdulla Mohamed, Ahmad Wasim

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