Comparison of 7 artificial intelligence models in predicting venous thromboembolism in COVID-19 patients by Indika Rajakaruna, Mohammad Hossein Amirhosseini, Mike Makris, Mike Laffan, Yang Li, Deepa J. Arachchillage

“…Background: An artificial intelligence (AI) approach can be used to predict venous thromboembolism (VTE). Objectives: To compare different AI models in predicting VTE using data from patients with COVID-19. …”
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Utilizing patient data: A tutorial on predicting second cancer with machine learning models by Hossein Sadeghi, Fatemeh Seif, Erfan Hatamabadi Farahani, Soraya Khanmohammadi, Shahla Nahidinezhad

“…To instruct and assess ML models for predicting the occurrence of SC based on patient data, the paper utilizes a dataset consisting of instances and attributes. …”
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Machine learning models for predicting survival in lung cancer patients undergoing microwave ablation by Yufan Liu, Zihang Wang, Xiaowen Cao, Miaoyan Liu, Lou Zhong

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Evaluation of multiple machine learning models predicting the results of hybrid imaging in primary hyperparathyroidism by Anna Drynda, Jacek Podlewski, Karolina Kucharczyk, Grzegorz Sokołowski, Anna Sowa-Staszczak, Alicja Hubalewska-Dydejczyk, Małgorzata Trofimiuk- Müldner

“…Random forest (RF) exhibited higher sensitivity (62.7%), but lower specificity (74.2%) and accuracy (68.6%). Other models demonstrated subpar performance. CONCLUSIONS: Logistic regression and RF models were the most effective in predicting radiotracer uptake in pre-operative hybrid imaging of the parathyroids, suggesting their suitability as the foundation for software to be used in clinical settings. …”
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Predicting the time to get back to work using statistical models and machine learning approaches by George Bouliotis, M. Underwood, R. Froud

“…Objectives To compare model performance and predictive accuracy of classic regressions and machine learning approaches using data from the Inspiring Families programme. …”
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Development and application of advanced learning models for predicting the land subsidence due to coal mining by Shirin Jahanmiri, Majid Noorian-Bidgoli

“…Three hybrid models—BBO-GEP, GWO-GEP, and SSA-GEP—were developed and tested to enhance prediction accuracy and reduce model uncertainty. …”
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Prediction by simulation in plant breeding by Huihui Li, Luyan Zhang, Shang Gao, Jiankang Wang

Subjects: “…Prediction by simulation…”
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Perinatal artificial intelligence in ultrasound (PAIR) study: predicting delivery timing by Neil Patel, John O’Brien, Robert Bunn, Brandon Schanbacher, John Bauer, Garrett K. Lam

“…OBJECTIVE To evaluate the ability of a proprietary artificial intelligence (AI) model to predict the number of days until delivery using ultrasound images alone and to assess the continuous improvement of prediction accuracy, particularly for preterm births, through model retraining.METHODS An AI software was developed and trained using de-identified ultrasound images from a cohort of women who delivered at the University of Kentucky from 2017 to 2021. …”
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Prediction models of iron level in beef muscle tissue toward ecological well-being by K. Narozhnykh

“…A high correlation existed between independent variables.FINDINGS: An optimum model for predicting the iron level in the muscle tissue of Hereford cattle was established. …”
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Tip of the Spear: Developing Predictive Military Planning Tools Using Hidden Markov Models by Matthew Litvinas

“…This paper explores the application of Hidden Markov Models (HMMs) to enhance existing Correlation of Forces and Means (COFM) calculators as predictive tools in military planning. …”
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Digital generation in the context of predicting the professional future by E. F. Zeer, N. G. Tserkovnikova, V. S. Tretyakova

Subjects: “…professional future predicting model…”
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Heuristic Model of Forecasting of Operating State of Semiconductor Devices by V. O. Kaziuchyts, S. M. Borovikov, E. N. Shneiderov

“…It is explained how to convert parameters to codes 1, 0 and R and get a prediction model in the form of a logical table built from these codes. …”
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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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Predictive for patients with pneumonia in pediatric intensive care unit by Mingxuan Jia, Xiyan Hu, Lin Ji, Jiawen Lin, Jialin Liu, Yong Wang

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Adaptive opportunistic routing protocol based on forwarding-utility for delay tolerant networks by WANG Bo1, HUANG Chuan-he1, YANG Wen-zhong2

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Impact of large language models and vision deep learning models in predicting neoadjuvant rectal score for rectal cancer treated with neoadjuvant chemoradiation by Hyun Bin Kim, Hong Qi Tan, Wen Long Nei, Ying Cong Ryan Shea Tan, Yiyu Cai, Fuqiang Wang

“…Abstract This study aims to explore Deep Learning methods, namely Large Language Models (LLMs) and Computer Vision models to accurately predict neoadjuvant rectal (NAR) score for locally advanced rectal cancer (LARC) treated with neoadjuvant chemoradiation (NACRT). …”
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AdditiveLLM: Large language models predict defects in metals additive manufacturing by Peter Pak, Amir Barati Farimani

“…In this work we investigate the ability of large language models to predict additive manufacturing defect regimes given a set of process parameter inputs. …”
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STFGCN: Spatio-Temporal Fusion Graph Convolutional Networks for Subway Traffic Prediction by Xiaoxi Zhang, Zhanwei Tian, Yan Shi, Qingwen Guan, Yan Lu, Yujie Pan

“…Experimental results on the Hangzhou Metro’s inbound and outbound passenger flow datasets demonstrate that the STFGCN model exhibits significant superiority over baseline models and shows excellent performance in metro passenger flow prediction. …”
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Novel hybrid and weighted ensemble models to predict river discharge series with outliers by Maha Shabbir, Sohail Chand, Farhat Iqbal

“…Lastly, using the mean absolute error (MAE) weights of HVK, HVA and HEM predictions, the HVK-HEM and HVA-HEM models were formulated. …”
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