High triglyceride-glucose index in young adulthood is associated with incident cardiovascular disease and mortality in later life: insight from the CARDIA study by Xinghao Xu, Rihua Huang, Yifen Lin, Yue Guo, Zhenyu Xiong, Xiangbin Zhong, Xiaomin Ye, Miaohong Li, Xiaodong Zhuang, Xinxue Liao

“…The TyG index was calculated as ln (fasting TG [mg/dl] × fasting glucose [mg/dl]/2), and the TyG index trajectories were identified by using the latent class growth mixture model. We evaluated the association between the baseline and trajectories of the TyG index with incident CVD events and all-cause mortality using Cox proportional hazards regression analysis. …”
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Trajectory Groups of 72-Hour Heart Rate After Mechanical Thrombectomy and Outcomes by Wang H, Zhang C, Xu L, Xu J, Xiao G

“…Latent variable mixture modeling was used to separate subjects into five groups with distinct HR trajectories. …”
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Segmentation of Dynamic Total-Body [18F]-FDG PET Images Using Unsupervised Clustering by Maria K. Jaakkola, Maria Rantala, Anna Jalo, Teemu Saari, Jaakko Hentilä, Jatta S. Helin, Tuuli A. Nissinen, Olli Eskola, Johan Rajander, Kirsi A. Virtanen, Jarna C. Hannukainen, Francisco López-Picón, Riku Klén

“…These criteria filtered out most of the commonly used approaches, leaving only two clustering methods, k-means and Gaussian mixture model (GMM), for further analyses. We combined k-means with two different preprocessing approaches, namely, principal component analysis (PCA) and independent component analysis (ICA). …”
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Estimation of Potato Chlorophyll Content from UAV Multispectral Images with Stacking Ensemble Algorithm by Huanbo Yang, Yaohua Hu, Zhouzhou Zheng, Yichen Qiao, Kaili Zhang, Taifeng Guo, Jun Chen

“…First, a combination of support vector machines (SVM) and a gaussian mixture model (GMM) thresholding method was proposed to estimate fractional vegetation cover (FVC) during the potato growing period, and the proposed method produced efficient estimates of FVC; among all the selected vegetation indices (VIs), the soil adjusted vegetation index (SAVI) had the highest accuracy. …”
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Resourcefulness Among Initial Ischemic Stroke Patients: A Longitudinal Study of 12 Months by Guo L, Zauszniewski JA, Zhang G, Lei X, Zhang M, Wei M, Ma K, Yang C, Liu Y, Guo Y

“…The Resourcefulness Scale© was evaluated at 6 time points. Growth mixture modeling was used to identify trajectory patterns of resourcefulness. …”
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Murine sepsis phenotypes and differential treatment effects in a randomized trial of prompt antibiotics and fluids by Christopher W. Seymour, Samantha J. Kerti, Anthony J. Lewis, Jason Kennedy, Emily Brant, John E. Griepentrog, Xianghong Zhang, Derek C. Angus, Chung-Chou H. Chang, Matthew R. Rosengart

“…Results Latent class mixture modeling derived a two-class model in cohort 1. …”
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Deep Learning for Delineation of the Spinal Canal in Whole-Body Diffusion-Weighted Imaging: Normalising Inter- and Intra-Patient Intensity Signal in Multi-Centre Datasets by Antonio Candito, Richard Holbrey, Ana Ribeiro, Christina Messiou, Nina Tunariu, Dow-Mu Koh, Matthew D. Blackledge

“…The algorithm was further semi-quantitatively validated on four additional datasets (three internal, one external, 207 scans total) by comparing the distributions of average apparent diffusion coefficient (ADC) and volume of the spinal cord derived from a two-component Gaussian mixture model of segmented regions. Our pipeline subsequently standardises WBDWI signal intensity through two stages: (i) normalisation of signal between imaging stations within each patient through histogram equalisation of slices acquired on either side of the station gap, and (ii) inter-scan normalisation through histogram equalisation of the signal derived within segmented spinal canal regions. …”
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Pediatric Chronic Postsurgical Pain And Functional Disability: A Prospective Study Of Risk Factors Up To One Year After Major Surgery by Rosenbloom BN, Pagé MG, Isaac L, Campbell F, Stinson JN, Wright JG, Katz J

“…Three percent (95% CI 1.17% to 6.23%) and 4% (95% CI 1.45% to 6.55%) of children reported using opioids to manage pain at 6 and 12 months, respectively. Growth mixture modeling revealed a two-class trajectory model with a quadratic slope best fit the data for both pain intensity (Bayesian information criterion [BIC] = 3977.03) and pain unpleasantness (BIC = 3644.45) over the 12 months. …”
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Acute postoperative opioid consumption trajectories and long-term outcomes in pediatric patients after spine surgery by Li MMJ, Ocay DD, Teles AR, Ingelmo PM, Ouellet JA, Pagé MG, Ferland CE

“…At 6 months after surgery, medication use, pain and functional activity were evaluated. Growth mixture modeling was used to identify opioid trajectories.Results: One hundred and six patients were included in the study. …”
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Exploration of RNA-Sequencing Data from Knee and Ankle Synovium using Machine Learning by Sara E. Buckley DO, Michael A. David PhD, Michael A. Hewitt BA, Michael Zuscik PhD, Kenneth Hunt MD

“…ML methods were employed on the total gene read counts using Python coding language to perform data i) standardization via unit variance, ii) reduction via principal component analysis, t-distributed stochastic neighbor embedding, and uniform manifold approximation and projection (UMAP) techniques, and iii) clustering via k-means, fuzzy c-means, and gaussian mixture model. The number of principal components and clusters were determined via skree plot and elbow method, respectively. …”
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Defining Southern Ocean fronts using unsupervised classification by S. D. A. Thomas, S. D. A. Thomas, D. C. Jones, A. Faul, E. Mackie, E. Mackie, E. Pauthenet

“…Here, we present a complementary new approach for calculating fronts using an unsupervised classification method called Gaussian mixture modelling (GMM) and a novel inter-class parameter called the <span class="inline-formula"><i>I</i></span>-metric. …”
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Global, regional, and national trends in haemoglobin concentration and prevalence of total and severe anaemia in children and pregnant and non-pregnant women for 1995–2011: a syste... by Gretchen A Stevens, DSc, Mariel M Finucane, PhD, Luz Maria De-Regil, DrSc, Christopher J Paciorek, PhD, Seth R Flaxman, BA, Francesco Branca, PhD, Juan Pablo Peña-Rosas, MD, Prof. Zulfiqar A Bhutta, PhD, Prof. Majid Ezzati, PhD

“…We used a Bayesian hierarchical mixture model to estimate haemoglobin distributions and systematically addressed missing data, non-linear time trends, and representativeness of data sources. …”
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Associations Between Healthy Lifestyle Trajectories and the Incidence of Cardiovascular Disease With All-Cause Mortality: A Large, Prospective, Chinese Cohort Study by Xiong Ding, Wei Fang, Xiaojie Yuan, Samuel Seery, Ying Wu, Shuohua Chen, Hui Zhou, Guodong Wang, Yun Li, Xiaodong Yuan, Shouling Wu

“…Background: Lifestyles generally change across the life course yet no prospective study has examined direct associations between healthy lifestyle trajectories and subsequent cardiovascular disease (CVD) or all-cause mortality risk.Methods: Healthy lifestyle score trajectories during 2006–2007, 2008–2009, and 2010–2011 were collated through latent mixture modeling. An age-scale based Cox proportional hazard regression model was implemented to calculate hazard ratios (HR) with corresponding 95% confidence intervals (CI) for developing CVD or all-cause mortality across healthy lifestyle trajectories.Results: 52,248 participants were included with four distinct trajectories identified according to healthy lifestyle scores over 6 years i.e., low-stable (n = 11,248), high-decreasing (n = 7,374), low-increasing (n = 7,828), and high-stable (n = 25,799). …”
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Age Differences in the Association of Sleep Duration Trajectory With Cancer Risk and Cancer-Specific Mortality: Prospective Cohort Study by Chenan Liu, Qingsong Zhang, Chenning Liu, Tong Liu, Mengmeng Song, Qi Zhang, Hailun Xie, Shiqi Lin, Jiangshan Ren, Yue Chen, Xin Zheng, Jinyu Shi, Li Deng, Hanping Shi, Shouling Wu

“…The sleep duration of participants was continuously recorded in 2006, 2008, and 2010. Latent mixture modeling was used to identify shared sleep duration trajectories. …”
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Multi-parametric PET/MRI for enhanced tumor characterization of patients with cervical cancer by Sahar Ahangari, Flemming Littrup Andersen, Naja Liv Hansen, Trine Jakobi Nøttrup, Anne Kiil Berthelsen, Jesper Folsted Kallehauge, Ivan Richter Vogelius, Andreas Kjaer, Adam Espe Hansen, Barbara Malene Fischer

“…The ability of multi-parametric imaging to identify tumor tissue classes was explored using an unsupervised 3D Gaussian mixture model-based cluster analysis. Results Functional MRI and PET of cervical cancers appeared heterogeneous both between patients and spatially within the tumors, and the relations between parameters varied strongly within the patient cohort. …”
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Trajectories of depression in sepsis survivors: an observational cohort study by Monique Boede, Jochen S. Gensichen, James C. Jackson, Fiene Eißler, Thomas Lehmann, Sven Schulz, Juliana J. Petersen, Florian P. Wolf, Tobias Dreischulte, Konrad F. R. Schmidt

“…Statistical analyses comprised descriptive analysis, univariate and multivariate, linear and logistic regression models and Growth Mixture Modeling. Results A total of 224 patients were included into this analysis. …”
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Feature-based clustering of the left ventricular strain curve for cardiovascular risk stratification in the general population by Evangelos Ntalianis, Nicholas Cauwenberghs, František Sabovčik, Everton Santana, Everton Santana, Francois Haddad, Piet Claus, Tatiana Kuznetsova

“…Therefore, in this longitudinal study, we applied an unsupervised machine learning approach based on time-series-derived features from the LV strain curve to identify distinct strain phenogroups that might be related to the risk of adverse cardiovascular events in the general population.MethodWe prospectively studied 1,185 community-dwelling individuals (mean age, 53.2 years; 51.3% women), in whom we acquired clinical and echocardiographic data including LV strain traces at baseline and collected adverse events on average 9.1 years later. A Gaussian Mixture Model (GMM) was applied to features derived from LV strain curves, including the slopes during systole, early and late diastole, peak strain, and the duration and height of diastasis. …”
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High order stochastic transport and Lagrangian data assimilation by Dutt, Arkopal

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Adaptive Stochastic Reduced-Order Modeling for Autonomous Ocean Platforms by Ryu, Young Hyun (Tony)

“…For stochastic forecasting and data assimilation onboard the unmanned autonomous ocean platforms, we combine the stochastic ensemble DMD method with the Gaussian Mixture Model - Dynamically Orthogonal equations (GMM-DO) filter. …”
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Informing decision-making in single-objective, mixed-variable design problems by Fang, Demi L.

“…The same samples are evaluated using the hybrid technique previously proposed by the author, which trains the data on a conditional variational autoencoder (cVAE), approximates gradients on the model, and summarizes gradients into “influence metrics” using a Gaussian mixture model (GMM) (in contrast to a mean absolute value). …”
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