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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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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Integrated multi-omics analysis and predictive modeling of heart failure using sepsis-related gene signature. by Yiping Lang, Tianyu Liang, Fei Li

“…<h4>Conclusion</h4>The model constructed through sepsis-related characteristic genes provides a highly advantageous method for predicting HF, and the characteristic genes we have screened may be potential biomarkers for predicting HF. …”
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A negative combined effect of exposure to maternal Mn-Cu-Rb-Fe metal mixtures on gestational anemia, and the mediating role of creatinine in the Guangxi Birth Cohort Study (GBCS):... by Yuen Zhong, Yu Bao, Hong Cheng, Chaoqun Liu, Shengzhu Huang, Hualong Qiu, Honglin Huang, Jiajun Ren, Hailiu Jin, Caitong He, Long Tian, Yu Zhang, Bangzhu Luo, Tao Liang, Mujun Li, Zengnan Mo, Longman Li, Xiaobo Yang

“…We utilized twelve machine learning (ML) algorithms to independently screen for effective metal mixtures, assess their combined impacts and dose-response relationships on gestational anemia, and estimate the mediating role of kidney function. …”
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Comparative Analysis of Osteoarthritis Therapeutics: A Justification for Harnessing Retrospective Strategies via an Inverted Pyramid Model Approach by Quinn T. Ehlen, Jacob Jahn, Ryan C. Rizk, Thomas M. Best

“…In comparison to the prospective approach, the retrospective strategy is likely more cost-effective, more widely applicable, and does not necessitate thorough and invasive genetic screening. …”
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RCFGL: Rapid Condition adaptive Fused Graphical Lasso and application to modeling brain region co-expression networks. by Souvik Seal, Qunhua Li, Elle Butler Basner, Laura M Saba, Katerina Kechris

“…We use a more efficient algorithm in the iterative steps compared to CFGL, enabling faster computation with complexity of O(p2K) and making it easily generalizable for more than three conditions. …”
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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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Fuzzy Decision-Making Analysis of Quantitative Stock Selection in VR Industry Based on Random Forest Model by Jia-Ming Zhu, Yu-Gan Geng, Wen-Bo Li, Xia Li, Qi-Zhi He

“…Different from the analysis of quantitative stock selection by constructing a logistics multifactor stock selection model in the existing research, the research mainly adopts the random forest algorithm based on fuzzy mathematics to construct the initial investment strategy portfolio. …”
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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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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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Development and validation of an explainable machine learning model for predicting osteoporosis in patients with type 2 diabetes mellitus by Qipeng Wei, Zihao Liu, Xiaofeng Chen, Hao Li, Weijun Guo, Qingyan Huang, Jinxiang Zhan, Shiji Chen, Dongling Cai, Dongling Cai

“…Potential predictive features were identified using univariate analysis, least absolute shrinkage and selection operator (LASSO) regression, and the Boruta algorithm. Eight supervised ML algorithms were applied to construct predictive models. …”
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Research Trends on Metabolic Syndrome in Digital Health Care Using Topic Modeling: Systematic Search of Abstracts by Kiseong Lee, Yoongi Chung, Ji-Su Kim

“…The methodological approach included text preprocessing, text network analysis, and topic modeling using the BERTopic algorithm. …”
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AI driven cardiovascular risk prediction using NLP and Large Language Models for personalized medicine in athletes by Ang Li, Yunxin Wang, Hongxu Chen

“…This study explores the innovative applications of Natural Language Processing (NLP) and Large Language Models (LLMs) in biomedical diagnostics, particularly for AI-driven arrhythmia detection, hypertrophic cardiomyopathy (HCM) in athletes, and personalized medicine. …”
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Remote Sensing for Urban Biodiversity: A Review and Meta-Analysis by Michele Finizio, Federica Pontieri, Chiara Bottaro, Mirko Di Febbraro, Michele Innangi, Giovanna Sona, Maria Laura Carranza

“…Our analysis incorporated technical (e.g., sensor, platform, algorithm), geographic (e.g., country, city extent, population) and ecological (biodiversity target, organization level, biome) meta-data, examining their frequencies, temporal trends (Generalized Linear Model—GLM), and covariations (Cramer’s V). …”
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Hyperspectral Imaging Combined with a Dual-Channel Feature Fusion Model for Hierarchical Detection of Rice Blast by Yuan Qi, Tan Liu, Songlin Guo, Peiyan Wu, Jun Ma, Qingyun Yuan, Weixiang Yao, Tongyu Xu

“…The DCFM model based on SPA screening obtained the best results, with an OA of 96.72% and a Kappa of 95.97%. …”
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Design and refinement of a clinical trial staffing model within the evolving landscape of oncology clinical trials by Ellen Siglinsky, Hannah Phan, Silviya Meletath, Amber Neal, David E. Gerber, Asal Rahimi, Erin L. Williams

“…We developed and evaluated a staffing model designed to meet this need. Methods: To address individual protocol acuity, the model's algorithms include metrics to account for visit frequency, and the quantity, and types of research-related procedures. …”
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Predictive models of sepsis-associated acute kidney injury based on machine learning: a scoping review by Jie Li, Manli Zhu, Li Yan

“…Background With the development of artificial intelligence, the application of machine learning to develop predictive models for sepsis-associated acute kidney injury has made potential breakthroughs in early identification, grading, diagnosis, and prognosis determination.Methods Here, we conducted a systematic search of the PubMed, Cochrane Library, Embase (Ovid), Web of Science, and Scopus databases on April 28, 2023, and screened relevant literature. …”
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