Prediction of lung overdistension during mechanical ventilation using micro-RNA and gene expression by Cecilia López-Martínez, Paula Martín-Vicente, Laura Amado-Rodríguez, Inés López-Alonso, Margarita Fernández-Rodríguez, Adrián González-López, Pablo Martínez-Camblor, Juan Gómez, Andrew J. Boyle, Cecilia M. O’Kane, Daniel F. McAuley, James N. Tsoporis, Claudia dos Santos, Guillermo M. Albaiceta

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TelescopeML. II. Convolutional Neural Networks for Predicting Brown Dwarf Atmospheric Parameters by Ehsan (Sam) Gharib-Nezhad, Hamed Valizadegan, Natasha E. Batalha, Miguel J. S. Martinho, Ben W.P. Lew

“…Accurately and swiftly predicting the parameters of brown dwarf atmospheres from observational spectra is crucial for understanding their atmospheric composition and guiding future follow-up observations. …”
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Identification and validation of prognostic biomarkers in ccRCC: immune-stromal score and survival prediction by Fang Lyu, Yuxin Zhong, Qingliu He, Wen Xiao, Xiaoping Zhang

“…The risk score model exhibited a high degree of predictive accuracy for survival outcomes in ccRCC. …”
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Predicting vector distribution in Europe: at what sample size are species distribution models reliable? by Lianne Mitchel, Lianne Mitchel, Guy Hendrickx, Ewan T. MacLeod, Cedric Marsboom, Cedric Marsboom

“…IntroductionSpecies distribution models can predict the spatial distribution of vector-borne diseases by forming associations between known vector distribution and environmental variables. …”
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External Validation of Persistent Severe Acute Kidney Injury Prediction With Machine Learning Model by Simone Zappalà, PhD, Francesca Alfieri, MS, Andrea Ancona, PhD, Antonio M. Dell’Anna, MD, Kianoush B. Kashani, MD, MS

“…The performance of the PersEA model, a boosted tree algorithm fed by hourly patient data via electronic health records to provide real-time psAKI predictions, was evaluated using specific metrics that penalize late alarms. …”
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Development, deployment, and feature interpretability of a three-class prediction model for pulmonary diseases by Zhenyu Cao, Gang Xu, Yuan Gao, Jianying Xu, Fengjuan Tian, Hengfeng Shi, Dengfa Yang, Zongyu Xie, Jian Wang

“…Abstract Purpose To develop a high-performance machine learning model for predicting and interpreting features of pulmonary diseases. …”
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Models based on dietary nutrients predicting all-cause and cardiovascular mortality in people with diabetes by Fang Wang, Yukang Mao, Jinyu Sun, Jiaming Yang, Li Xiao, Qingxia Huang, Chenchen Wei, Zhongshan Gou, Kerui Zhang

“…The least absolute shrinkage and selection operator (LASSO) regression and random forest (RF) algorithm were applied to identify key mortality-related dietary factors, which were subsequently incorporated into risk prediction nomogram models. …”
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Predicting pediatric patient rehabilitation outcomes after spinal deformity surgery with artificial intelligence by Wenqi Shi, Felipe O. Giuste, Yuanda Zhu, Ben J. Tamo, Micky C. Nnamdi, Andrew Hornback, Ashley M. Carpenter, Coleman Hilton, Henry J. Iwinski, J. Michael Wattenbarger, May D. Wang

“…Moreover, we enable responsible AI by calibrating model confidence for human intervention and mitigating health disparities for algorithm fairness. Results The best prediction model achieves an area under receiver operating curve (AUROC) performance of 0.86, 0.85, and 0.83 for individual SRS-22R question response prediction over three-time horizons from pre-operation to 6-month, 1-year, and 2-year post-operation, respectively. …”
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Toward generalizable prediction of antibody thermostability using machine learning on sequence and structure features by Ameya Harmalkar, Roshan Rao, Yuxuan Richard Xie, Jonas Honer, Wibke Deisting, Jonas Anlahr, Anja Hoenig, Julia Czwikla, Eva Sienz-Widmann, Doris Rau, Austin J. Rice, Timothy P. Riley, Danqing Li, Hannah B. Catterall, Christine E. Tinberg, Jeffrey J. Gray, Kathy Y. Wei

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Prediction of soil chemical properties using multispectral satellite images and wavelet transforms methods by Chaitanya B. Pande, Sunil A. Kadam, Rajesh Jayaraman, Sunil Gorantiwar, Mukund Shinde

“…In this study, the neural network wavelet model was used to predicted values related to soil chemical properties in the semi-arid region. …”
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Interpretable Machine Learning for Legume Yield Prediction Using Satellite Remote Sensing Data by Theodoros Petropoulos, Lefteris Benos, Remigio Berruto, Gabriele Miserendino, Vasso Marinoudi, Patrizia Busato, Chrysostomos Zisis, Dionysis Bochtis

“…Accurate crop yield prediction is vital towards optimizing agricultural productivity. …”
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Emerging and Pioneering AI Technologies in Aesthetic Dermatology: Sketching a Path Toward Personalized, Predictive, and Proactive Care by Diala Haykal

“…Objectives: Artificial intelligence (AI) is transforming aesthetic dermatology, introducing new opportunities for personalized, predictive, and adaptive approaches in skin diagnostics, treatment planning, and patient management. …”
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Explainable machine learning models predicting the risk of social isolation in older adults: a prospective cohort study by Mingfei Jiang, Xiaoran Li, YongLu

“…Abstract Introduction This study aimed to develop a machine learning system to predict social isolation risk in older adults. Methods Data from a sample of 6588 older adults in China were analyzed using information from China Health and Retirement Longitudinal Study from 2015 to 2018. …”
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Development and validation of a decision tree model for prediction of insomnia risk among ischemic stroke convalescence patients by Xuefeng Sun, Zilin Wang, Yuqing Song, Deyu Cong, Shu Sun, Xinye Zhang, Ye Zhang, Hongshi Zhang

“…While the interaction of clinical, psychological, and social factors remains unclear, developing a predictive model system is urgently needed. Currently, few studies have established insomnia risk prediction models. …”
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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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A novel approach for the effective prediction of cardiovascular disease using applied artificial intelligence techniques by Azka Mir, Attique Ur Rehman, Tahir Muhammad Ali, Sabeen Javaid, Maram Fahaad Almufareh, Mamoona Humayun, Momina Shaheen

“…Methods In this paper, we have utilized machine learning algorithms to predict cardiovascular disease on the basis of symptoms such as chest pain, age and blood pressure. …”
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A machine learning model for predicting anatomical response to Anti-VEGF therapy in diabetic macular edema by Wenrui Lu, Kunhong Xiao, Xuemei Zhang, Yuqing Wang, Wenbin Chen, Xierong Wang, Yunxi Ye, Yan Lou, Li Li

“…PurposeTo develop a machine learning model to predict anatomical response to anti-VEGF therapy in patients with diabetic macular edema (DME).MethodsThis retrospective study included patients with DME who underwent intravitreal anti-VEGF treatment between January 2023 and February 2025. …”
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A Novel Evolutionary Deep Learning Approach for PM_2.5 Prediction Using Remote Sensing and Spatial–Temporal Data: A Case Study of Tehran by Mehrdad Kaveh, Mohammad Saadi Mesgari, Masoud Kaveh

“…Concurrently, long short-term memory (LSTM) models have shown considerable promise in enhancing air quality predictions, often outperforming other prediction techniques. …”
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Construction of a sleep staging prediction model for obstructive sleep apnea patients based on hypoxia parameters by YANG Mengdie，PENG Cheng，CUI Yiran，XU Shaorong，WANG Yan

“…The ANN model was constructed using a feed-forward structure incorporating a multilayer perceptron（MLP）with a back-propagation algorithm. Predictive performance was assessed using receiver operating characteristic（ROC）curves.Results Compared with the hypoxia parameters in NREM stage，e-minSpO2 and r.DSpO2 were lower，and ΔSpO2，d.DSpO2，ODR，ORR，T90，d.T90，r.T90，and ST90，d.ST90，r.ST90 were higher in REM stage（P<0.05）. …”
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Pan-cancer predictive survival model development and evaluation using electronic health record and genetic data across 10 cancer types by Jurgita Gammall, Alvina G. Lai

“…We compare the performance of different machine learning algorithms and assess the added value of genetic information in cancer prognosis. …”
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