Algorithm for Cloud Particle Phase Identification Based on Bayesian Random Forest Method by Fu Tao, Yang Zhipeng, Tao Fa, Hu Shuzhen, Lu Yuxiang, Fu Changqing

“…Results in a high rate of misclassification when employing machine learning techniques for identifying the phase state of cloud particles.To accurately identify phases of cloud particles, a Bayesian Random Forest Method is employed, utilizing co-located millimeter-wave cloud radar and microwave radiometer observations. …”
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The association of origin and environmental conditions with performance in professional IRONMAN triathletes by Beat Knechtle, Mabliny Thuany, David Valero, Elias Villiger, Pantelis T. Nikolaidis, Marilia S. Andrade, Ivan Cuk, Thomas Rosemann, Katja Weiss

“…Data was analyzed using descriptive statistics and machine learning (ML) regression models. The models considered gender, country of origin, event location, water, and air temperature as independent variables to predict the final race time. …”
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Establishment and validation of predictive model of ARDS in critically ill patients by Senhao Wei, Hua Zhang, Hao Li, Chao Li, Ziyuan Shen, Yiyuan Yin, Zhukai Cong, Zhaojin Zeng, Qinggang Ge, Dongfeng Li, Xi Zhu

“…This study aimed to observe the incidence of ARDS among high-risk patients and develop and validate an ARDS prediction model using machine learning (ML) techniques based on clinical parameters. …”
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Differences in Characteristics of Peripartum Patients Who Did and Did Not Require an Upgrade to the Intensive Care Unit: A Propensity Score Matching Study by Jennifer A. Walker, Jerry M. Yang, Saad Pirzada, Manahel Zahid, Samantha Asuncion, Amanda Tuchler, Gillian Cooper, Allison Lankford, Emad Elsamadicy, Quincy K. Tran

“…The Classification And Regression Tree, a machine learning algorithm, was used to identify significant predictors of ICU upgrade. …”
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Relationship between stress hyperglycemia ratio and progression of non target coronary lesions: a retrospective cohort study by Shiqi Liu, Ziyang Wu, Gaoliang Yan, Yong Qiao, Yuhan Qin, Dong Wang, Chengchun Tang

“…Logistic regression models, restricted cubic spline analysis, and machine learning algorithms (LightGBM, decision tree, and XGBoost) were utilized to analyse the relationship of stress hyperglycemia ratio and non target lesion progression. …”
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Harmonizing population health data into OMOP common data model: a demonstration using COVID-19 sero-surveillance data from Nairobi Urban Health and Demographic Surveillance System by Michael Ochola, Sylvia Kiwuwa-Muyingo, Tathagata Bhattacharjee, David Amadi, Maureen Ng’etich, Damazo Kadengye, Henry Owoko, Boniface Igumba, Jay Greenfield, Jim Todd, Agnes Kiragga, Agnes Kiragga

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MLinvitroTox reloaded for high-throughput hazard-based prioritization of high-resolution mass spectrometry data by Katarzyna Arturi, Eliza J. Harris, Lilian Gasser, Beate I. Escher, Georg Braun, Robin Bosshard, Juliane Hollender

“…MLinvitroTox is a machine learning (ML) framework comprising 490 independent XGBoost classifiers trained on molecular fingerprints from chemical structures and target-specific endpoints from the ToxCast/Tox21 invitroDBv4.1 database. …”
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Preoperative prediction of lymph node metastasis in intrahepatic cholangiocarcinoma: an integrative approach combining ultrasound-based radiomics and inflammation-related markers by Yu-ting Peng, Jin-shu Pang, Peng Lin, Jia-min Chen, Rong Wen, Chang-wen Liu, Zhi-yuan Wen, Yu-quan Wu, Jin-bo Peng, Lu Zhang, Hong Yang, Dong-yue Wen, Yun He

“…In the training cohort, we performed a Wilcoxon test to screen for differentially expressed features, and then we used 12 machine learning algorithms to develop 107 models within the cross-validation framework and determine the optimal radiomics model through receiver operating characteristic (ROC) curve analysis. …”
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Identification of macrophage polarisation and mitochondria-related biomarkers in diabetic retinopathy by Weifeng Liu, Bin Tong, Jian Xiong, Yanfang Zhu, Hongwei Lu, Haonan Xu, Xi Yang, Feifei Wang, Peng Yu, Yunwei Hu

“…Key genes were obtained by Mendelian randomisation (MR) analysis, then biomarkers were obtained by machine learning combined with receiver operating characteristic (ROC) and expression validation between DR and control cohorts in GSE221521 and GSE160306 to obtain biomarkers. …”
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Intraoperative haemodynamic optimisation using the Hypotension Prediction Index and its impact on tissular perfusion: a protocol for a randomised controlled trial by Javier Ripollés-Melchor, Ane Abad-motos, Juan Victor Lorente, Ignacio Jimenez, Alejandra Becerra, Wilbert Wesselink, Francesca Reguant, Irene Mojarro, Maria de los Angeles Fuentes, Elizabeth Agudelo, Francisco Herrero-Machancoses, Paula Callejo, Joan Bosch, Manuel Ignacio Monge

“…The Hypotension Prediction Index (HPI) developed using machine learning techniques, allows the prediction of arterial hypotension analysing the arterial pressure waveform. …”
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Automated differentiation of wide QRS complex tachycardia using QRS complex polarity by Adam M. May, Bhavesh B. Katbamna, Preet A. Shaikh, Sarah LoCoco, Elena Deych, Ruiwen Zhou, Lei Liu, Krasimira M. Mikhova, Rugheed Ghadban, Phillip S. Cuculich, Daniel H. Cooper, Thomas M. Maddox, Peter A. Noseworthy, Anthony Kashou

“…Methods In a three-part study, we derive and validate machine learning (ML) models—logistic regression (LR), artificial neural network (ANN), Random Forests (RF), support vector machine (SVM), and ensemble learning (EL)—using engineered (WCT-PC and QRS-PS) and previously established WCT differentiation features. …”
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New Insights into the Role of Inflammatory Pathways and Immune Cell Infiltration in Sleep Deprivation-Induced Atrial Fibrillation: An Integrated Bioinformatics and Experimental Stu... by Liang J, Tang B, Shen J, Rejiepu M, Guo Y, Wang X, Shao S, Guo F, Wang Q, Zhang L

“…The application of machine learning uncovered four crucial genes—CDC5L, MAPK14, RAB5A, and YBX1—with YBX1 becoming the predominant gene in diagnostic processes. …”
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Personalized prediction of anticancer potential of non-oncology drugs through learning from genome derived molecular pathways by Xiaobao Dong, Huanhuan Liu, Ting Tong, Liuxing Wu, Jianhua Wang, Tianyi You, Yongjian Wei, Xianfu Yi, Hongxi Yang, Jie Hu, Haitao Wang, Xiaoyan Wang, Mulin Jun Li

“…Herein we present CHANCE, a supervised machine learning model designed to predict the anticancer activities of non-oncology drugs for specific patients by simultaneously considering personalized coding and non-coding mutations. …”
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Comprehensive pan-cancer analysis reveals NTN1 as an immune infiltrate risk factor and its potential prognostic value in SKCM by Fuxiang Luan, Yuying Cui, Ruizhe Huang, Zhuojie Yang, Shishi Qiao

“…To further elucidate the influence of genes on tumors, we utilized a variety of machine learning techniques and found that NTN1 is strongly linked to multiple cancer types, suggesting it as a potential therapeutic target. …”
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5G Networks Security Mitigation Model: An ANN-ISM Hybrid Approach by Rafiq Ahmad Khan, Habib Ullah Khan, Hathal Salamah Alwageed, Hussein Al Hashimi, Ismail Keshta

“…The proposed model includes state-of-the-art machine learning with traditional information security paradigms to offer an integrated solution to the emerging complex security issues related to 5G. …”
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Patients with Age-related Macular Degeneration Have Increased Susceptibility to Valvular Heart Disease by Natan Lishinsky-Fischer, Itay Chowers, MD, PhD, Yahel Shwartz, MSc, Jaime Levy, MD

“…Moreover, a supervised machine learning model successfully detected the presence of AMD based solemnly on the patient’s history of VHD. …”
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Brain Age Prediction Using a Lightweight Convolutional Neural Network by Fatma Eltashani, Mario Parreno-Centeno, James H. Cole, Joao Paulo Papa, Fumie Costen

“…Much interest has recently been drawn to brain age prediction due to the significant development in machine learning and image processing techniques. Studies based on brain magnetic resonance images showed a strong relationship between the brain ageing process and accelerated brain atrophy, suggesting using brain age prediction models for early diagnosis of neurodegenerative disorders, such as Parkinson’s, Schizophrenia, and Alzheimer’s disease. …”
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Experimental study and model prediction of the influence of different factors on the mechanical properties of saline clay by Hui Cheng, Lingkai Zhang, Chong Shi, Pei Pei Fan

“…The boundary point of the 2% salt content divides the effect of salt ions from promoting free water flow to blocking seepage channels, with the proportion of micropores being the primary influencing factor. (4) Employing statistical theory and machine learning algorithms, dry density, water content, and salinity are used to predict mechanical index values. …”
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Ratio of Skeletal Muscle Mass to Visceral Fat Area Is a Useful Marker for Assessing Left Ventricular Diastolic Dysfunction among Koreans with Preserved Ejection Fraction: An Analys... by Jin Kyung Oh, Yuri Seo, Wonmook Hwang, Sami Lee, Yong-Hoon Yoon, Kyupil Kim, Hyun Woong Park, Jae-Hyung Roh, Jae-Hwan Lee, Minsu Kim

“…This study investigated the association between the ratio of skeletal muscle mass to visceral fat area (SVR) and left ventricular diastolic dysfunction (LVDD) in patients with preserved ejection fraction using random forest machine learning. Methods : In total, 1,070 participants with preserved left ventricular ejection fraction who underwent comprehensive health examinations, including transthoracic echocardiography and bioimpedance body composition analysis, were enrolled. …”
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UAV Hyperspectral Remote Sensing Image Classification: A Systematic Review by Zhen Zhang, Lehao Huang, Qingwang Wang, Linhuan Jiang, Yemao Qi, Shunyuan Wang, Tao Shen, Bo-Hui Tang, Yanfeng Gu

“…This article provides an in-depth and systematic review of UAV HSI classification techniques, systematically examining the evolution from traditional machine learning approaches, such as sparse coding, compressed sensing, and kernel methods, to cutting-edge deep learning frameworks, including convolutional neural networks, Transformer models, recurrent neural networks, graph convolutional networks, generative adversarial networks, and hybrid models. …”
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