Retrospective validation of the postnatal growth and retinopathy of prematurity criteria in a Chinese cohort by Li Li, Yanlin Gao, Yuhan Lu, Wei Chen, Mei Han

“…Application of the G-ROP prediction model can improve the sensitivity and specificity of ROP screening. …”
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All-Cause Mortality Risk in Elderly Patients with Femoral Neck and Intertrochanteric Fractures: A Predictive Model Based on Machine Learning by Min A, Liu Y, Fu M, Hou Z, Wang Z

“…Cox proportional hazards regression is used to explore the association between fractures type and mortality. Boruta algorithm was used to screen the risk factors related to death. …”
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The Utility of Follow-up Transthoracic Echocardiogram to Screen for Severe Portopulmonary Hypertension (POPH) in Patients Granted POPH Model for End-stage Liver Disease (MELD) Exce... by Kathryn T. del Valle, MD, Dana Kay, MD, Michael J. Krowka, MD, James R. Runo, MD, Corey Sadd, MD, Julie K. Heimbach, MD, Rodrigo Cartin-Ceba, MD, Hector R. Cajigas, MD, Charles D. Burger, MD, John E. Moss, MD, Hilary M. DuBrock, MD

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Immunogenic cell death genes in single-cell and transcriptome analyses perspectives from a prognostic model of cervical cancer by Li Ning, Li Ning, Xiu Li, Xiu Li, Yating Xu, Yating Xu, Yu Si, Yu Si, Hongting Zhao, Qinling Ren, Qinling Ren

“…This study sought to investigate the significance of ICD in CESC and to establish an ICDRs prognostic model to improve immunotherapy efficacy for patients with cervical cancer.MethodsICD-associated genes were screened at the single-cell and transcriptome levels based on AddModuleScore, single-sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network (WGCNA) analysis. …”
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Prediction of pulmonary embolism by an explainable machine learning approach in the real world by Qiao Zhou, Ruichen Huang, Xingyu Xiong, Zongan Liang, Wei Zhang

“…To address this, we employed an artificial intelligence–based machine learning algorithm (MLA) to construct a robust predictive model for PE. …”
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Few-shot hotel industry site selection prediction method based on meta learning algorithms and transportation accessibility by Na Li, Huaishi Wu

“…Then, a transportation accessibility calculation model is constructed using spatial syntax for secondary screening. …”
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Efficient Design Optimization Assisted by Sequential Surrogate Models by Emiliano Iuliano

“…The paper proposes a global optimization algorithm employing surrogate modeling and adaptive infill criteria. …”
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QSAR Models for Predicting the Antioxidant Potential of Chemical Substances by Sofia Ghironi, Edoardo Luca Viganò, Gianluca Selvestrel, Emilio Benfenati

“…Different machine learning algorithms were applied to build regression models, and the goodness-of-fit of each model was assessed using the statistical parameters of R squared (R²), the Root-Mean-Squared Error, and the Mean Absolute Error. …”
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Machine learning models for predicting metabolic dysfunction-associated steatotic liver disease prevalence using basic demographic and clinical characteristics by Gangfeng Zhu, Yipeng Song, Zenghong Lu, Qiang Yi, Rui Xu, Yi Xie, Shi Geng, Na Yang, Liangjian Zheng, Xiaofei Feng, Rui Zhu, Xiangcai Wang, Li Huang, Yi Xiang

“…This study aimed to explore the feasibility of utilising machine learning models to accurately screen for MASLD in large populations based on a combination of essential demographic and clinical characteristics. …”
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Development and validation of a predictive model for new HIV infection screening among persons 15 years and above in primary healthcare settings in Kenya: a study protocol by Simon Karanja, Amos Otieno Olwendo, Gideon Kikuvi

“…Introduction This study seeks to determine incidence, comorbidities and drivers for new HIV infections to develop, test and validate a risk prediction model for screening for new cases of HIV.Methods and analysis The study has two components: a cross-sectional study to develop the prediction model using the HIV dataset from the Kenya AIDS and STI Control Programme and a 15-month prospective study for the validation of the model. …”
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Diagnostic accuracy of artificial intelligence models in detecting congenital heart disease in the second-trimester fetus through prenatal cardiac screening: a systematic review an... by Lies Dina Liastuti, Lies Dina Liastuti, Yosilia Nursakina, Yosilia Nursakina

“…Most studies utilized deep learning models using either ultrasound or echocardiographic images. …”
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Identification of immune-related biomarkers linked to systemic lupus erythematosus and dilated cardiomyopathy through integrated bioinformatics analysis and multiple machine learni... by Gaijie Li, Liwen Lin, Shushu Wang, Kachun Lu, KaMan Szeto, Guiting Zhou, Xianwen Tang, Chuanjin Luo, Chuanjin Luo

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Machine learning-based prediction of in-hospital mortality for critically ill patients with sepsis-associated acute kidney injury by Tianyun Gao, Zhiqiang Nong, Yuzhen Luo, Manqiu Mo, Zhaoyan Chen, Zhenhua Yang, Ling Pan

“…Ensemble stepwise feature selection method was used to screen for effective features. The prediction models of short-term mortality were developed by seven machine learning algorithms. …”
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Medical laboratory data-based models: opportunities, obstacles, and solutions by Jiaojiao Meng, Moxin Wu, Fangmin Shi, Ying Xie, Hui Wang, You Guo

“…Abstract Medical Laboratory Data (MLD) models, which combine artificial intelligence with big medical data, have great potential in disease screening, diagnosis, personalized medicine, and health management. …”
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River floating object detection with transformer model in real time by Chong Zhang, Jie Yue, Jianglong Fu, Shouluan Wu

“…Building upon this foundation, we introduce the LR-DETR, a lightweight evolution of RT-DETR for river floating object detection. This model incorporates the High-level Screening-feature Path Aggregation Network (HS-PAN), which refines feature fusion through a novel bottom-up fusion path, significantly enhancing its expressive power. …”
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Automatic segmentation model and machine learning model grounded in ultrasound radiomics for distinguishing between low malignant risk and intermediate-high malignant risk of adnex... by Lu Liu, Wenjun Cai, Feibo Zheng, Hongyan Tian, Yanping Li, Ting Wang, Xiaonan Chen, Wenjing Zhu

“…Critical relevance statement The ultrasound radiomics-based machine learning model holds the potential to elevate the professional ability of less-experienced radiologists and can be used to assist in the clinical screening of ovarian cancer. …”
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Predicting the generalization of computer aided detection (CADe) models for colonoscopy by Joel Shor, Carson McNeil, Yotam Intrator, Joseph R. Ledsam, Hiro-o Yamano, Daisuke Tsurumaru, Hiroki Kayama, Atsushi Hamabe, Koji Ando, Mitsuhiko Ota, Haruei Ogino, Hiroshi Nakase, Kaho Kobayashi, Masaaki Miyo, Eiji Oki, Ichiro Takemasa, Ehud Rivlin, Roman Goldenberg

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Machine learning-driven development of a stratified CES-D screening system: optimizing depression assessment through adaptive item selection by Ruo-Fei Xu, Zhen-Jing Liu, Shunan Ouyang, Qin Dong, Wen-Jing Yan, Dong-Wu Xu

“…We employed a two-stage machine learning approach: first applying Recursive Feature Elimination with multiple linear regression to identify core predictive items for total depression scores, followed by logistic regression for optimizing depression classification (CES-D ≥ 16). Model performance was systematically evaluated through discrimination (ROC analysis), calibration (Brier score), and clinical utility analyses (decision curve analysis), with additional validation using random forest and support vector machine algorithms across independent samples. …”
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Applications of AI-Based Models for Online Fraud Detection and Analysis by Antonis Papasavva, Samantha Lundrigan, Ed Lowther, Shane Johnson, Enrico Mariconti, Anna Markovska, Nilufer Tuptuk

“…Results We discuss the state-of-the-art AI and NLP techniques used to analyse various online fraud categories; the data sources used for training the AI and NLP models; the AI and NLP algorithms and models built; and the performance metrics employed for model evaluation. …”
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Discovery, Biological Evaluation and Binding Mode Investigation of Novel Butyrylcholinesterase Inhibitors Through Hybrid Virtual Screening by Lizi Li, Puchen Zhao, Can Yang, Qin Yin, Na Wang, Yan Liu, Yanfang Li

“…This study employed a quantitative structure–activity relationship (QSAR) model based on ECFP4 molecular fingerprints with several machine learning algorithms (XGBoost, RF, SVM, KNN), among which the XGBoost model showed the best performance (AUC = 0.9740). …”
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