Prognostic models for predicting patient arrivals in emergency departments: an updated systematic review and research agenda by Luka Petravić, Kaja Gril Rogina, Tit Albreht, Andreja Kukec, Janez Žibert

Get full text

Novel machine learning models for the prediction of acute respiratory distress syndrome after liver transplantation by Weijie Wu, Weijie Wu, Zheng Zhang, Shuailei Wang, Ru Xin, Dong Yang, Weifeng Yao, Ziqing Hei, Chaojin Chen, Gangjian Luo

Subjects: Get full text

Time-Adaptive Machine Learning Models for Predicting the Severity of Heart Failure with Reduced Ejection Fraction by Trevor Winger, Cagri Ozdemir, Shanti L. Narasimhan, Jaideep Srivastava

“…<b>Results:</b> With the progressive introduction of patient-specific data, the model demonstrated significant improvements in predictive capabilities. …”
Get full text

Prediction of Reservoir Flow Capacity in Sandstone Formations: A Comparative Analysis of Machine Learning Models by Micheal Ayodeji Ogundero, Taiwo Adelakin, Kehinde Orolu, Isaac Femi Johnson, Theophilus Akinfenwa Fashanu, Kingsley Abhulimen

“…The SVR model had large variations from the actual values and hence was not very useful for our predictions. …”
Get full text

Going concern prediction – A horse race between traditional and regularization machine learning models by Tina Vuko, Slavko Šodan, Ivana Perica

“…Regularization machine learning (ML) methods have been increasingly applied in accounting research, offering new possibilities in predictive modeling. Their forte lies in the effective regularization methods for resolving the biggest concern of generalization, which is the risk of overfitting the training data. …”
Get full text

Perspective: How complex in vitro models are addressing the challenges of predicting drug-induced liver injury by K. Taylor, R. Ram, R. Ram, L. Ewart, C. Goldring, G. Russomanno, G. P. Aithal, T. Kostrzewski, C. Bauch, J. M. Wilkinson, J. M. Wilkinson, S. Modi, J. G. Kenna, J. G. Kenna, J. Bailey, J. Bailey

“…Predicting which drugs might have the potential to cause drug-induced liver injury (DILI) is highly complex and the current methods, 2D cell-based models and animal tests, are not sensitive enough to prevent some costly failures in clinical trials or to avoid all patient safety concerns for DILI post-market. …”
Get full text

Shoreline dynamics prediction using machine learning models: from process learning to probabilistic forecasting by Afshar Adeli, Afshar Adeli, Ali Dastgheib, Ali Dastgheib, Dano Roelvink, Dano Roelvink, Dano Roelvink

Subjects: Get full text

Using Monte Carlo conformal prediction to evaluate the uncertainty of deep-learning soil spectral models by Y.-C. Huang, J. Padarian, B. Minasny, A. B. McBratney

“…This study introduces an innovative application of Monte Carlo conformal prediction (MC-CP) to quantify uncertainty in deep-learning models for predicting clay content from mid-infrared spectroscopy. …”
Get full text

Advanced Default Risk Prediction in Small and Medum-Sized Enterprises Using Large Language Models by Haonan Huang, Jing Li, Chundan Zheng, Sikang Chen, Xuanyin Wang, Xingyan Chen

“…However, data on the commercial bills of SMEs are scarce and challenging to gather, which has impeded research on risk prediction for these businesses. This study aims to address this gap by leveraging 38 multi-dimensional, non-financial features collected from 1972 real SMEs in China to predict bill default risk. …”
Get full text

Log BB Prediction Models Using TLC and HPLC Retention Values as Protein Affinity Data by Karolina Wanat, Klaudia Michalak, Elżbieta Brzezińska

“…Methods: Predictive models were created using the physicochemical properties of drugs, and multiple linear regression and a data mining method, i.e., MARSplines, were used to build them. …”
Get full text

Machine learning models for clinical and structural knee osteoarthritis prediction: Recent advancements and future directions by Gabby B. Joseph, Charles E. McCulloch, Michael C. Nevitt, Nancy E. Lane, Sharmila Majumdar, Thomas M. Link

Subjects: Get full text

Clinical characteristics of bronchopulmonary dysplasia and the risk of sepsis onset prediction via machine learning models by Yanhua Wang, Yanhua Wang, Yanhua Wang, Yanhua Wang, Yi Wang, Yi Wang, Yi Wang, Linhong Song, Jun Li, Jun Li, Jun Li, Yuanyuan Xie, Yuanyuan Xie, Yuanyuan Xie, Lei Yan, Lei Yan, Lei Yan, Siqi Hu, Siqi Hu, Siqi Hu, Zhichun Feng, Zhichun Feng, Zhichun Feng, Zhichun Feng

Subjects: Get full text

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. …”
Get full text

Molecular surface descriptors to predict antibody developability: sensitivity to parameters, structure models, and conformational sampling by Eliott Park, Saeed Izadi

“…In silico assessment of antibody developability during early lead candidate selection and optimization is of paramount importance, offering a rapid and material-free screening approach. However, the predictive power and reproducibility of such methods depend heavily on the selection of molecular descriptors, model parameters, accuracy of predicted structure models, and conformational sampling techniques. …”
Get full text

Predicting Marshall stability and flow parameters in asphalt pavements using explainable machine-learning models by Ibrahim Asi, Yusra I. Alhadidi, Taqwa I. Alhadidi

Subjects: Get full text

XGBoost models based on non imaging features for the prediction of mild cognitive impairment in older adults by Miguel A. Fernández-Blázquez, José M. Ruiz-Sánchez de León, Rubén Sanz-Blasco, Emilio Verche, Marina Ávila-Villanueva, María José Gil-Moreno, Mercedes Montenegro-Peña, Carmen Terrón, Cristina Fernández-García, Jaime Gómez-Ramírez

“…The aim of this study is to develop and validate machine learning (ML) models based on non-imaging features to predict the risk of MCI conversion in cognitively healthy older adults over a three-year period. …”
Get full text

Comparing efficacy of different scoring models to predict hepatic encephalopathy after TIPS in cirrhotic patients by Xin-Jian Xu, Liang Yin, Yi-Jiang Zhu, Dong Lu, Xiang-Zhong Huang, Wei-Fu Lv, Chun-Ze Zhou, De-Lei Cheng

“…This study compares the predictive performance of Child-Pugh and Model for End-Stage Liver Disease (MELD), CLIFC-AD and Freiburg index of post-TIPS survival (FIPS) scores for overt and severe HE. …”
Get full text

Solar Radiation Prediction Using Decision Tree and Random Forest Models in Open-Source Software by Tucumbi Lisbeth, Guano Jefferson, Salazar-Achig Roberto, Jiménez J. Diego L.

“…The metrics used to identify the effectiveness of the models in predicting solar radiation were the coefficient (R2), the mean square error (MSE), and the mean absolute error (MAE). …”
Get full text

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. …”
Get full text

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. …”
Get full text