Development and validation of predictive models for distant metastasis and prognosis of gastroenteropancreatic neuroendocrine neoplasms by Xuan-Peng Zhou, Luan-Biao Sun, Wen-Hao Liu, Xin-Yuan Song, Yang Gao, Jian-Peng Xing, Shuo-Hui Gao

“…This study aims to develop and validate a risk prediction model for the distant metastases and prognosis of GEP-NENs. …”
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PREDICTIVE MODELS FOR EARLY DETECTION OF PARKINSON’S DISEASE: A MACHINE LEARNING APPROACH by S. Jeyantha Jafna Juliet, D. Jasmine David, J. S. Raj Kumar, Angelin Jeba P., R. Golden Nancy, M. Selvarathi, T. Jemima Jebaseeli

“…These methods involve the analysis of various types of data, including clinical assessments, imaging scans, and genetic markers, to develop accurate predictive models. Even in the initial stages of the conditions, machine learning techniques can discriminate between patients who have and do not have PD by identifying minor variations and traits from such multivariate data. …”
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Beyond analytic bounds: Re-evaluating predictive power in risky decision models by Or David Agassi, Ori Plonsky

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Post–Liver Transplant Outcomes: A Comparative Study of 6 Predictive Models by Christof Kaltenmeier, MD, Eishan Ashwat, BA, Hao Liu, MD, PhD, Charbel Elias, MD, Amaan Rahman, BA, Jason Mail-Anthony, BA, Isabel Neckermann, BA, Stalin Dharmayan, MD, Andrew Crane, MD, Godwin Packiaraj, MD, Subhashini Ayloo, MD, MPH, Armando Ganoza, MD, MBA, Vikraman Gunabushanam, MD, Michele Molinari, MD, MHS

“…Conclusions. None of the predictive models demonstrated sufficient precision to reliably identify LT recipients who died within 90 d and 1 y after LT. …”
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Evaluation of predictive accuracy of gene expression in pigs using machine learning models by Tianle ZHOU, Jinyan TENG, Zhiting XU, Zhe ZHANG

“…Subsequently, we evaluated the prediction accuracy of each model.ResultThere was a positive correlation between the prediction accuracy of machine learning models and the cis-h2 of genes. …”
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Influential factors related to feeding disorders in preterm infants and the construction of predictive models by Lishan Chen, Lishan Chen, Huichang Zhou, Zhiming Tang, Haiyin Deng, Zhihao Li

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Predictive performance and uncertainty analysis of ensemble models in gully erosion susceptibility assessment by Congtan Liu, Haoming Fan, Yixuan Wang

“…This study aims to identify the optimal feature datasets and to quantify the uncertainty associated with gully erosion prediction models by developing a novel methodological framework based on ensembles of the three machine learning models: Random Forest (RF), Convolutional Neural Network (CNN), and Transformer models. …”
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Patient perspective on predictive models in healthcare: translation into practice, ethical implications and limitations? by Sarah Markham

“…In this perspective article, we consider the use of predictive models in healthcare and associated challenges. …”
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Analysis of the factors that influence the quality of rapeseed and sunflower seeds and development of predictive models☆ by El Msayryb Abdellatif, Champolivier Luc, Debaeke Philippe, Flénet Francis

“…The model achieved good predictive accuracy, with over 84% of well-predicted values falling within acceptable ranges for rapeseed seed quality (oil and protein concentrations), and the same for sunflower protein concentration. …”
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Evaluating the Performance and Potential Bias of Predictive Models for Detection of Transthyretin Cardiac Amyloidosis by Jonathan Hourmozdi, MD, MSAI, MA, Nicholas Easton, MSAI, Simon Benigeri, MSAI, James D. Thomas, MD, Akhil Narang, MD, David Ouyang, MD, Grant Duffy, BS, Ross Upton, PhD, Will Hawkes, PhD, Ashley Akerman, PhD, Ike Okwuosa, MD, Adrienne Kline, MD, PhD, Abel N. Kho, MD, Yuan Luo, PhD, Sanjiv J. Shah, MD, Faraz S. Ahmad, MD, MS

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Letter: the limitations of gene set-based predictive models: a critical assessment by Lijing Yang, Wenbo Zhao, Min Jiang, Xianqiang Liu

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Predictive models for health-related quality of life built on two telemonitoring datasets. by Matea Tashkovska, Stefan Krsteski, Emilija Kizhevska, Jakob Valič, Hristijan Gjoreski, Mitja Luštrek

“…The results showed that using general predictive models intended for previously unseen patients do not work well. …”
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Predictive models for road traffic sign: Retroreflectivity status, retroreflectivity coefficient, and lifespan by Roxan Saleh, Hasan Fleyeh

“…The results indicate high accuracy in the predictive models, with classification accuracy at 94% and an R2 value of 94% for regression analysis. …”
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Advanced predictive machine and deep learning models for round-ended CFST column by Feng Shen, Ishan Jha, Haytham F. Isleem, Walaa J.K. Almoghayer, Mohammad Khishe, Mohamed Kamel Elshaarawy

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Advancements in Predictive Microbiology: Integrating New Technologies for Efficient Food Safety Models by Oluseyi Rotimi Taiwo, Helen Onyeaka, Elijah K. Oladipo, Julius Kola Oloke, Deborah C. Chukwugozie

“…Predictive models are widely used in food microbiology to estimate the growth of microorganisms in food products. …”
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A new preprocedural predictive risk model for post-endoscopic retrograde cholangiopancreatography pancreatitis: The SuPER model by Mitsuru Sugimoto, Tadayuki Takagi, Tomohiro Suzuki, Hiroshi Shimizu, Goro Shibukawa, Yuki Nakajima, Yutaro Takeda, Yuki Noguchi, Reiko Kobayashi, Hidemichi Imamura, Hiroyuki Asama, Naoki Konno, Yuichi Waragai, Hidenobu Akatsuka, Rei Suzuki, Takuto Hikichi, Hiromasa Ohira

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Predictive Modeling of Water Level in the San Juan River Using Hybrid Neural Networks Integrated with Kalman Smoothing Methods by Jackson B. Renteria-Mena, Eduardo Giraldo

“…This study presents an innovative approach to predicting the water level in the San Juan River, Chocó, Colombia, by implementing two hybrid models: nonlinear auto-regressive with exogenous inputs (NARX) and long short-term memory (LSTM). …”
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A Multi‐Model Ensemble of Baseline and Process‐Based Models Improves the Predictive Skill of Near‐Term Lake Forecasts by Freya Olsson, Tadhg N. Moore, Cayelan C. Carey, Adrienne Breef‐Pilz, R. Quinn Thomas

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Improving Hepatitis B outcome prediction with ensemble machine learning: A study on predictive models and interpretability by Abid Bin Ahosan, Forhadul Islam, Khandaker Mohammad Mohi Uddin, Nahid Hasan, Md Ashraf Uddin

“…This study investigates the effectiveness of various machine learning (ML) techniques in predicting patient outcomes in HBV infection. Methods The Chi-squared test was used for feature selection to find the most important factors, which were later applied to train and evaluate various ML models. …”
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Predicting the Future Failures of Urban Water Systems: Integrating Climate Change and Machine Learning Prediction Models by Melica Khashei, Fatemeh Boloukasli ahmadgourabi, Rebecca Dziedzic

“…However, the integration of these climate-related factors into watermain failure prediction models, with a specific focus on climate change impacts, remains insufficiently explored. …”
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