Predictive Performance of Machine Learning for Suicide in Adolescents: Systematic Review and Meta-Analysis by Lingjiang Liu, Zhiyuan Li, Yaxin Hu, Chunyou Li, Shuhan He, Shibei Zhang, Jie Gao, Huaiyi Zhu, Guoping Huang

“…PubMed, Embase, Cochrane, and Web of Science databases were rigorously searched until April 20, 2024, and a multivariate prediction model was employed to assess the risk of bias. …”
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Transplantation of Patients with Hepatocellular Carcinoma Through Increased Utilization of Machine Perfusion Technology by Lauren E. Matevish, MD, Jason Guo, BS, Andrew D. Shubin, MD, PhD, Malcolm MacConmara, MD, Christine S. Hwang, MD, Nathanael Raschzok, MD, Nicole E. Rich, MD, MSCS, Arjmand R. Mufti, MD, Amit G. Singal, MD, MS, Parsia A. Vagefi, MD, Madhukar S. Patel, MD, MBA, ScM

“…With the intent to mitigate waitlist disparities, the median model for end-stage liver disease (MELD) at transplant minus 3 policy nevertheless decreased access to liver transplant for patients with hepatocellular carcinoma (HCC). …”
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Methodological and reporting quality of machine learning studies on cancer diagnosis, treatment, and prognosis by Aref Smiley, David Villarreal-Zegarra, C. Mahony Reategui-Rivera, Stefan Escobar-Agreda, Joseph Finkelstein

“…This study aimed to evaluate the quality and transparency of reporting in studies using machine learning (ML) in oncology, focusing on adherence to the Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Models (CREMLS), TRIPOD-AI (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis), and PROBAST (Prediction Model Risk of Bias Assessment Tool). …”
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An Advanced Approach for Predicting Workpiece Surface Roughness Using Finite Element Method and Image Processing Techniques by Taoming Chen, Chun Li, Zhexiang Zou, Qi Han, Bing Li, Fengshou Gu, Andrew D. Ball

“…Thus, the proposed model provides a precise predictive tool for surface roughness, offering valuable guidance for optimizing machining parameters and supporting proactive control in the turning process, ultimately enhancing machining efficiency and quality.…”
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Phthalate Metabolites Were Related to the Risk of High-Frequency Hearing Loss: A Cross-Sectional Study of National Health and Nutrition Examination Survey by You LM, Zhang DC, Lin CS, Lan Q

“…In the model, gender, diabetes, and MBZP were the top predictors of HFHL.Conclusion: The study identified a significant association between MBZP exposure and HFHL, highlighting the need to reduce phthalate exposure.Keywords: hearing loss, phthalate metabolites, monobenzyl phthalate, machine learning models, cross-sectional…”
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Application of machine learning assisted multi-variate UV spectrophotometric models augmented by kennard stone clustering algorithm for quantifying recently approved nasal spray co... by Ahmed Emad F. Abbas, Mohammed Gamal, Ibrahim A. Naguib, Michael K. Halim, Basmat Amal M. Said, Mohammed M. Ghoneim, Mohmeed M. A. Mansour, Yomna A. Salem

“…The robustness of this approach was rigorously tested using five distinct chemometric models: principal component regression, classical least squares, partial least squares, genetic algorithm-partial least squares, and multivariate curve resolution-alternating least squares, demonstrating its broad applicability across diverse modeling techniques. …”
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Analyzing mental stress in Indian students through advanced machine learning and wearable technologies by Shruti Gedam, Sandip Dutta, Ritesh Jha

“…The findings reveal that the suggested model detects mental stress with an accuracy of 96.17%, with the XGBoost method outperforming other algorithms in multivariate analysis. …”
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Prognostic prediction of gastric cancer based on H&E findings and machine learning pathomics by Guoda Han, Xu Liu, Tian Gao, Lei Zhang, Xiaoling Zhang, Xiaonan Wei, Yecheng Lin, Bohong Yin

“…Aim: In this research, we aimed to develop a model for the accurate prediction of gastric cancer based on H&E findings combined with machine learning pathomics. …”
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Gaitformer: a spatial-temporal attention-enhanced network without softmax for Parkinson’s disease early detection by Shupei Jiao, Hua Huo, Wei Liu, Changwei Zhao, Lan Ma, Jinxuan Wang, Ningya Xu, Chen Zhang, Dongfang Li

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Machine Learning for Just-In-Time Adaptive Mental Health Interventions Using Smartwatch Data by Douglas Talbert, Katherine Phillips

“…This study hypothesizes that predictive modeling of mood states from multivariate time-series data collected via mobile sensors can be enhanced by leveraging sequence-aware models over non-sequential alternatives. …”
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Using Machine Learning to Understand Injuries in Female Agricultural Operators in the Central United States by Cheryl L. Beseler, Risto H. Rautiainen

“…XGBoost identified the total number of musculoskeletal symptoms, age, sleep deprivation, high work-related stress, and exposure to respiratory irritants as being important to injury. The multivariate logistic regression model identified higher income, higher stress, younger age, and number of musculoskeletal symptoms as being significantly associated with injury. …”
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Interpretable machine learning analysis of immunoinflammatory biomarkers for predicting CHD among NAFLD patients by Wenyuan Dong, Hongcheng Jiang, Yu Li, Luo Lv, Yuxin Gong, Bao Li, Hongjie Wang, Hesong Zeng

“…To interpret the diagnostic model built by Random Forest, the SHapley Additive exPlanations (SHAP) method was employed, and features were ranked according to their SHAP values. …”
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What factors enhance students' achievement? A machine learning and interpretable methods approach. by Hui Mao, Ribesh Khanal, ChengZhang Qu, HuaFeng Kong, TingYao Jiang

“…This study addresses these limitations by employing an ensemble of five machine learning algorithms (SVM, DT, ANN, RF, and XGBoost) to model multivariate relationships between four behavioral and six instructional predictors, using final exam performance as our outcome variable. …”
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Machine learning algorithm based on combined clinical indicators for the prediction of infertility and pregnancy loss by Rui Zhang, Yuanbing Guo, Xiaonan Zhai, Juan Wang, Xiaoyan Hao, Liu Yang, Lei Zhou, Jiawei Gao, Jiayun Liu

“…Three methods were used for screening 100+ clinical indicators, and five machine learning algorithms were used to develop and evaluate diagnostic models based on the most relevant indicators.ResultsMultivariate analysis revealed significant differences in several factors between the patients and the control group. 25-hydroxy vitamin D3 (25OHVD3) was the factor exhibiting the most prominent difference, and most patients presented deficiency in the levels of this vitamin. 25OHVD3 is associated with blood lipids, hormones, thyroid function, human papillomavirus infection, hepatitis B infection, sedimentation rate, renal function, coagulation function, and amino acids in patients with infertility. …”
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Clinical subtypes identification and feature recognition of sepsis leukocyte trajectories based on machine learning by ShengHui Miao, YiJing Liu, Min Li, Jing Yan

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Machine learning-based process quality control of screen-printed titanium dioxide electrodes by Anesu Nyabadza, Lola Azoulay-Younes, Mercedes Vazquez, Dermot Brabazon

“…A dataset comprising ∼300 electrodes was created to train the AI models. The SVM model demonstrated the best performance, achieving 100 % accuracy and recall, followed by the FNN model with 99 % accuracy. …”
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Application of the Different Machine Learning Algorithms to Predict Dry Matter Intake in Feedlot Cattle by Hayati Köknaroğlu, Özgür Koşkan, Malik Ergin

“…Due to the development of computing technology and different machine learning models, big data sets have gained importance in animal science as well as in many disciplines. …”
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Development of an mPBPK machine learning framework for early target pharmacology assessment of biotherapeutics by Krutika Patidar, Nikhil Pillai, Saroj Dhakal, Lindsay B. Avery, Panteleimon D. Mavroudis

“…In the present work, we propose a machine learning-based target pharmacology assessment framework that utilizes minimal physiologically based pharmacokinetic (mPBPK) modeling and machine learning (ML) to infer optimal physicochemical properties of antibodies and their targets. …”
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PREDICTION INTERVALS IN MACHINE LEARNING: RESIDUAL BOOTSTRAP AND QUANTILE REGRESSION FOR CASH FLOW ANALYSIS by Wa Ode Rahmalia Safitri, Farit Mochamad Afendi, Budi Susetyo

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Machine Learning Reveals Microbial Taxa Associated with a Swim across the Pacific Ocean by Garry Lewis, Sebastian Reczek, Osayenmwen Omozusi, Taylor Hogue, Marc D. Cook, Jarrad Hampton-Marcell

“…The V4 region of the 16S rRNA gene was sequenced, generating 6.2 million amplicon sequence variants. Multivariate analysis was used to analyze the microbial community structure, and machine learning (random forest) was used to model the microbial dynamics over time using R statistical programming. …”
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