Comparing the Effectiveness of Artificial Intelligence Models in Predicting Ovarian Cancer Survival: A Systematic Review by Farkhondeh Asadi, Milad Rahimi, Nahid Ramezanghorbani, Sohrab Almasi

“…Commonly used algorithms for survival prediction included random forest, support vector machines, logistic regression, XGBoost, and various deep learning models. …”
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Predicting Marshall stability and flow parameters in asphalt pavements using explainable machine-learning models by Ibrahim Asi, Yusra I. Alhadidi, Taqwa I. Alhadidi

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Predicting suitable habitats and conservation areas for Suaeda salsa using MaxEnt and Marxan models by Yongji Wang, Zhusong Liu, Kefan Wu, Jiamin Peng, Yanyue Mao, Guanghua Zhao, Fenguo Zhang

“…Using 130 occurrence records and 14 selected environmental variables, this study applied the MaxEnt model to predict suitable habitats of S. salsa across China under current and future climate scenarios. …”
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Artificial Intelligence and Machine Learning Models for Predicting Drug-Induced Kidney Injury in Small Molecules by Mohan Rao, Vahid Nassiri, Sanjay Srivastava, Amy Yang, Satjit Brar, Eric McDuffie, Clifford Sachs

“…This study introduces an AI/ML (artificial intelligence/machine learning) model that integrates both physicochemical properties and off-target interactions to enhance DIKI prediction. …”
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Predicting carbapenem-resistant Pseudomonas aeruginosa infection risk using XGBoost model and explainability by Yan Jiang, Hong-wei Wang, Fang-ying Tian, Yue Guo, Xiu-mei Wang

“…This study aims to identify the risk factors of CRPA infection and construct a machine learning model to provide a prediction tool for clinical prevention and control. …”
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Investigating the Impact of Climate Change on the Effective Indicators in Desertification and Predicting its Spatial Changes by Azam Sadat Hosseini Khezr Abad, Abassali Vali, Amirhossein Halabian, Mohammad Hossein Mokhtari, Seyyed Ali Mousavi

“…Also, based on the IMDPA model, 80.54 percent of the area of ​​the region is in the severe desertification class in the base period. …”
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Clinical Prediction Models for Contact X‐Ray Brachytherapy in Managing Rectal Cancers: A Scoping Review by Muneeb Ul Haq, D. Mark Pritchard, Arthur Sun Myint, Muhammad Ahsan Javed, Carrie A. Duckworth, Ngu Wah Than, Laura J. Bonnett, David M. Hughes

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NSGT: A Plasma Position Prediction Method for the HL-2A Tokamak Device Based on Transformer by Ling Zhou, Bin Yang, Xiangcheng Lin, Kaihao Zhu, Ziran Lan, Xiangwen Li

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Efficient Encoding and Decoding of Voxelized Models for Machine Learning-Based Applications by Damjan Strnad, Stefan Kohek, Borut Zalik, Libor Vasa, Andrej Nerat

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A Predicting Method of the Strong Cooling Process during Winter with Numerical Model Prediction and its Operational Application by Bomin CHEN, Kun ZHOU, Fei XIN, jun MA, Limei JIN

“…At first this paper quantitatively evaluated the low-frequency wave performance of NCEP-CF Sv2 model over the eight key areas on 700 hPa from January to March and from October to December of 2017， and then made operationally the fifteen extended-range operational predictions of strong cooling process for January to April of 2018 and for November to January 2019 with the 1~30 days prediction given by CFSv2 model as well as the low-frequency wave conceptual predicting model.The results show that the phase and evolution trend of the low-frequency wave in the key area predicted by CFSv2 model are highly consistent with the reality， with the correlation coefficients of 0.839 of the predicted low-frequency waves with the observed for the extended-range （11~30 days）， the accuracy of low frequency wave trend by the model over 3~6 pentad up to percent of 83.3 on average， and the percentage of 100-percent accuracy of the trend even up to 45.8.The average accuracy， Cs and Zs scores of 15 strong cooling process operational predicting are 61.2%， 0.149 and 0.158 respectively， and at the same time the occurrence of the two strongest cooling processes at the beginning and the end of 2018 were accurately given with the lead-time of 18 and 16 days in turn， which are significantly higher than those of the operation predicting for the same period of 2015 to 2017 without CFSv2 results.…”
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Development and Validation of a Novel PPAR Signaling Pathway-Related Predictive Model to Predict Prognosis in Breast Cancer by Yingkun Xu, Dan Shu, Meiying Shen, Qiulin Wu, Yang Peng, Li Liu, Zhenrong Tang, Shun Gao, Yuan Wang, Shengchun Liu

“…Finally, to gain insight into the predictive value and protein expression of these risk model genes in breast cancer, we used GEO and HPA databases for validation. …”
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Diabetes Mellitus Disease Prediction and Type Classification Involving Predictive Modeling Using Machine Learning Techniques and Classifiers by B. Shamreen Ahamed, Meenakshi S. Arya, S. K. B. Sangeetha, Nancy V. Auxilia Osvin

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Flare Set-Prediction Transformer: A Transformer-Based Set-Prediction Model for Detailed Solar Flare Forecasting by Liang Qiao, Gang Qin

“…Solar flare prediction models typically use classification, predicting only the probability of categorized events within a time window. …”
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ED-SA-ConvLSTM: A Novel Spatiotemporal Prediction Model and Its Application in Ionospheric TEC Prediction by Yalan Li, Haiming Deng, Jian Xiao, Bin Li, Tao Han, Jianquan Huang, Haijun Liu

“…Existing work based on Convolutional Long Short-Term Memory (ConvLSTM) primarily relies on convolutional operations for spatial feature extraction, which are effective at capturing local spatial correlations, but struggle to model long-range dependencies, limiting their predictive performance. …”
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Prediction of 28-day mortality in patients with sepsis based on a predictive model: A retrospective cohort study by Yi Sun, Tingting Wang, Mengna Zhang, Shuchen Cao, Liwei Hua, Kun Zhang

“…Objective This study aimed to develop and validate a nomogram model for predicting 28-day mortality in patients with sepsis in the intensive care unit. …”
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Radiographic prediction model based on X-rays predicting anterior cruciate ligament function in patients with knee osteoarthritis by Guanghan Gao, Yaonan Zhang, Lei Shi, Lin Wang, Fei Wang, Qingyun Xue

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External validation of the MR PREDICTS@24H model: predicting functional outcome after endovascular treatment in stroke by Michael Sonnberger, Raimund Helbok, Jeanette Tas, Milan R Vosko, Cristina Cerinza Sick, Caterina Kulyk, Bogdan-Andrei Ianosi, Patrizia Spiandorello, Melanie Bergmann

“…Background Chalos et al recently developed the MR PREDICTS@24H model to predict 90 days functional outcomes in ischaemic stroke patients following endovascular treatment (EVT). …”
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Bleeding risk and prediction model to predict bleeding risk of tissue biopsy for definitive diagnosis in patients with suspected amyloidosis by Watsamon Uraiwan, Pirun Saelue

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Predicting and Preventing Crime: A Crime Prediction Model Using San Francisco Crime Data by Classification Techniques by Muzammil Khan, Azmat Ali, Yasser Alharbi

“…The study proposes a crime prediction model by analyzing and comparing three known prediction classification algorithms: Naive Bayes, Random Forest, and Gradient Boosting Decision Tree. …”
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Neural network prediction model based on Levy flight and natural biomimetic technology for its application in cancer prediction. by Ruiyu Zhan

“…Regarding precision, the model achieved accuracies of 0.67, 0.69, and 0.66 for miRNA expression, gene expression, and DNA methylation, respectively. …”
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