Reasoning language models for more transparent prediction of suicide risk by Roy H Perlis, Thomas H McCoy

“…The corresponding c-statistic was 0.64 (0.63–0.65), modestly poorer than the GPT4o model (0.67 (0.66–0.68)). In chain-of-thought reasoning, topics including Substance Abuse, Surgical Procedure, and Age-related Comorbidities were associated with correct predictions, while Fall-related Injury was associated with incorrect prediction.Conclusions Application of a reasoning model using local, consumer-grade hardware only modestly diminished performance in stratifying suicide risk.Clinical implications Smaller models can yield more secure, scalable and transparent risk prediction.…”
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Risk prediction models for diabetic retinopathy: a systematic review by Hui Huang, Yingmin Wu, Hejiang Ye, Jiaoyang Li, Ling Chen, Xuan Huang

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Regression models for productivity prediction in cactus pear cv. Gigante by Bruno V. C. Guimarães, Sérgio L. R. Donato, Ignacio Aspiazú, Alcinei M. Azevedo, Abner J. de Carvalho

“…This study aimed to adjust, through simple and multiple regression analysis, models for predicting the yield of cactus pear cv. …”
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Benchmarking shoreline prediction models over multi-decadal timescales by Yongjing Mao, Giovanni Coco, Sean Vitousek, Jose A. A. Antolinez, Georgios Azorakos, Masayuki Banno, Clément Bouvier, Karin R. Bryan, Laura Cagigal, Kit Calcraft, Bruno Castelle, Xinyu Chen, Maurizio D’Anna, Lucas de Freitas Pereira, Iñaki de Santiago, Aditya N. Deshmukh, Bixuan Dong, Ahmed Elghandour, Amirmahdi Gohari, Eduardo Gomez-de la Peña, Mitchell D. Harley, Michael Ibrahim, Déborah Idier, Camilo Jaramillo Cardona, Changbin Lim, Ivana Mingo, Julian O’Grady, Daniel Pais, Oxana Repina, Arthur Robinet, Dano Roelvink, Joshua Simmons, Erdinc Sogut, Katie Wilson, Kristen D. Splinter

“…The outcomes from this collaborative benchmarking competition critically review the present state-of-the-art in shoreline change prediction as well as reveal model limitations, facilitate improvements, and offer insights for advancing shoreline-prediction capabilities.…”
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Development of novel hybrid models for the prediction of Covid-19 in Kuwait by Ahmad Aldousari, Maria Qurban, Ijaz Hussain, Maha Al-Hajeri

“…There is a need to estimate a prediction model for the estimation of this epidemic size. …”
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Deep Learning Models for Predicting the Recurrence of Idiopathic Granulomatous Mastitis by Li L, Yang W, Jia H

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Optimizing Deep Learning Models with Improved BWO for TEC Prediction by Yi Chen, Haijun Liu, Weifeng Shan, Yuan Yao, Lili Xing, Haoran Wang, Kunpeng Zhang

Subjects: “…TEC prediction…”
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Stock price prediction using combined GARCH-AI models by John Kamwele Mutinda, Amos Kipkorir Langat

“…The non-linear and non-stationary nature of financial time series data poses significant challenges for standalone statistical and neural network methods. While predictive modeling in finance often focuses on volatility, there is a notable lack of research on predicting actual stock prices, particularly in the African market. …”
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Calibrating AI Models for Wireless Communications via Conformal Prediction by Kfir M. Cohen, Sangwoo Park, Osvaldo Simeone, Shlomo Shamai Shitz

“…This paper investigates the application of conformal prediction as a general framework to obtain AI models that produce decisions with formal calibration guarantees. …”
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Hybrid Deep Learning Models for Predicting Student Academic Performance by Kuburat Oyeranti Adefemi, Murimo Bethel Mutanga, Vikash Jugoo

“…This study aims to bridge this gap by proposing a deep learning model to predict student academic performance with greater accuracy. …”
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Machine learning models for predicting tibial intramedullary nail length by Sercan Capkin, Ali Ihsan Kilic, Hakan Cici, Mehmet Akdemir, Mert Kahraman Marasli

“…The correlation between shoe size and the dependent variable was weaker (r = 0.823), and the inclusion of shoe size in the model negatively impacted the prediction accuracy. …”
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Application of Thermodynamic Models to Predict the Solubility of Biologically Active Substances by K. S. Stepanov, G. N. Turmanidze, V. V. Sorokin, A. D. Sakharov

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Connectome-based prediction of functional impairment in experimental stroke models. by Oliver Schmitt, Peter Eipert, Yonggang Wang, Atsushi Kanoke, Gratianne Rabiller, Jialing Liu

“…Dynamic modeling with the weighted bilateral connectome detected changes in signal propagation in the remote hippocampus in all 3 stroke types, predicting the extent of hippocampal hypoactivation and impairment in spatial learning and memory function. …”
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Numerical Permeation Models to Predict the Annulus Composition of Flexible Pipes by João Marcos B. Vieira, José Renato M. de Sousa

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Human genotype-to-phenotype predictions: Boosting accuracy with nonlinear models. by Aleksandr Medvedev, Satyarth Mishra Sharma, Evgenii Tsatsorin, Elena Nabieva, Dmitry Yarotsky

“…First, we ask if state-of-the-art nonlinear predictive models, such as boosted decision trees, can be more efficient for phenotype prediction than conventional linear models. …”
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Hyperparameter optimization of machine learning models for predicting actual evapotranspiration by Chalachew Muluken Liyew, Elvira Di Nardo, Stefano Ferraris, Rosa Meo

“…Consequently, AET data is limited, prompting the use of meteorological and soil features for prediction. This study develops and evaluates machine learning models for AET prediction based on two input combinations. …”
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Machine learning models for predicting spatiotemporal dynamics of groundwater recharge by Azeddine Elhassouny

“…Open public remote sensing datasets were used to develop machine learning prediction models (Random Forest, XGBoost, Keras models, etc.) and time series forecasting models (LSTM, CNN, etc.) for predicting and forecasting groundwater sheet recharge, respectively. …”
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A study on the effectiveness of machine learning models for hepatitis prediction by Popy Khatun, Shafeel Umam, Rubaiya Binte Razzak, Iffat Binta Shamsuddin, Nahid Salma

“…We employed one traditional predictive model, logistic regression, alongside six machine learning models: support vector machine (SVM), K-nearest neighbors (KNN), artificial neural network (ANN), random forest (RF), AdaBoost, and XGBoost. …”
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Predicting Crime Time Intervals Using Machine Learning Models by Arief Deswandi, Widi Hastomo

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Machine learning proteochemometric models for Cereblon glue activity predictions by Francis J. Prael, III, Jiayi Cox, Noé Sturm, Peter Kutchukian, William C. Forrester, Gregory Michaud, Jutta Blank, Lingling Shen, Raquel Rodríguez-Pérez

“…For other drug modalities, predictive modeling has been established to leverage existing activity data and generate quantitative structure-activity relationships (QSAR). …”
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