A latent variable transformation model approach for exploring dysphagia

Snavely, Anna C.; Harrington, David P.; Li, Yang

doi:10.1002/sim.6239

Cited by 4 publications

(3 citation statements)

References 31 publications

(65 reference statements)

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“…Thus, instead of studies with small population samples that suffer from inherent bias, we have artificial intelligence, 102,103,163 computer modeling, 164,165 and big data statistical approaches that can "see" things in the data that were previously elusive to us as humans. 79,[166][167][168] Finally, our ability to use technology to connect patients and clinicians allows for clinical care under circumstances that were not previously possible. With these advances come growing pains.…”

Section: Emerging Approachesmentioning

confidence: 99%

Dysphagia Practice in 2035: Beyond Fluorography, Thickener, and Electrical Stimulation

Ciucci¹,

Jones

Malandraki

et al. 2016

Semin Speech Lang

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Dysphagia evaluation and management has rapidly become the primary practice area of medical speech pathologists since its adoption in our field less than three decades ago. As a specialty, swallowing and swallowing disorders comprise the largest represented discipline with 10,059 specialty interest group members within the American Speech-Language-Hearing Association and 298 board-certified specialists in the American Speech Hearing Association. There are national and international organizations, such as the Dysphagia Research Society and its interdisciplinary journal Dysphagia, that provide continuing education for clinicians and a platform for dysphagia researchers. Despite this rapid growth, herein we identify some significant needs for improving the science and practice of dysphagia clinical care, including a deeper understanding of physiology and neurophysiology, standardization of evaluation, consensus on core sets of dysphagia parameters for clinical and research reporting, personalized algorithms for implementation of evidenced-based practice, metrics for therapy efficacy, and increased buy-in and funding from agencies. The goals of this article are to summarize the status quo of dysphagia research, evaluation, and treatment as well as to make predictions about the future. Medical trends that we speculate will influence dysphagia research and care in the future include, among others, imaging advances, personalized medicine, regenerative medicine, and telehealth.

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Section: Emerging Approachesmentioning

confidence: 99%

Dysphagia Practice in 2035: Beyond Fluorography, Thickener, and Electrical Stimulation

Ciucci¹,

Jones

Malandraki

et al. 2016

Semin Speech Lang

View full text Add to dashboard Cite

show abstract

“…These models have been generalized to a slightly larger class of multivariate parametric likelihoods that can be solved using weighted least squares (Yee, ), but the VGLM methods are not applicable to censored or truncated data. Estimation procedures have also been proposed for nSTM to model multiple phenotypes that are potentially of mixed types and/or subject to censoring (Othus and Li, ; Snavely, Harrington and Li, ; Zhou et al, ). These previous approaches to nSTM have featured restrictive assumptions about the covariance structure and cumbersome iterative methods for estimation while lacking asymptotic results for censored outcomes.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Multiple Diverse Phenotypes via Semiparametric Canonical Correlation Analysis

Agniel

Cai

2017

Biometrics

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Summary Studying multiple outcomes simultaneously allows researchers to begin to identify underlying factors that affect all of a set of diseases (i.e., shared etiology) and what may give rise to differences in disorders between patients (i.e., disease subtypes). In this work, our goal is to build risk scores that are predictive of multiple phenotypes simultaneously and identify subpopulations at high risk of multiple phenotypes. Such analyses could yield insight into etiology or point to treatment and prevention strategies. The standard canonical correlation analysis (CCA) can be used to relate multiple continuous outcomes to multiple predictors. However, in order to capture the full complexity of a disorder, phenotypes may include a diverse range of data types, including binary, continuous, ordinal, and censored variables. When phenotypes are diverse in this way, standard CCA is not possible and no methods currently exist to model them jointly. In the presence of such complications, we propose a semi-parametric CCA method to develop risk scores that are predictive of multiple phenotypes. To guard against potential model mis-specification, we also propose a nonparametric calibration method to identify subgroups that are at high risk of multiple disorders. A resampling procedure is also developed to account for the variability in these estimates. Our method opens the door to synthesizing a wide array of data sources for the purposes of joint prediction.

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“…LVM has been increasingly studied and currently is a booming field of research both in theoretical developments and in applications. Examples of LVM in recent literature, to quote a few, include the following: covariate measurement error in nonlinear models [42]; spatial statistics [208]; learning harmonium models with infinite latent features using graphical undirected LVM [52]; tensor decomposition for LVM [11]; semiparametric and parametric LVM in the presence of measurable outcomes of mixed type (continuous and ordinal) in medicine [248]; error and bias estimation in sources and components using Monte Carlo estimators [271]; selection of latent variables for multiple mixed-outcome models [289]; hierarchical Bayesian models using LVM for network data [194]; fast algorithms for computing deviance information criterion (DIC) of high-dimensional LVM [46]; and extending the factor analysis from individual to groups of variables [137].…”

Section: Latent Variable Modelingmentioning

confidence: 99%

New Insights in Prediction and Dynamic Modeling from Non-Gaussian Mixture Processing Methods

Armero¹

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This thesis considers new applications of non-Gaussian mixtures in the framework of statistical signal processing and pattern recognition. The non-Gaussian mixtures were implemented by mixtures of independent component analyzers (ICA). The fundamental hypothesis of ICA is that the observed signals can be expressed as a linear transformation of a set of hidden variables, usually referred to as sources, which are statistically independent. This independence allows factoring the original M-dimensional probability density function (PDF) of the data as a product of one-dimensional probability densities, greatly simplifying the modeling of the data. ICA mixture models (ICAMM) provide further flexibility by alleviating the independency requirement of ICA, thus allowing the model to obtain local projections of the data without compromising its generalization capabilities. Here are explored new possibilities of ICAMM for the purposes of estimation and classification of signals.The thesis makes several contributions to the research in non-Gaussian mixtures: (i) a method for maximum-likelihood estimation of missing data, based on the maximization of the PDF of the data given the ICAMM; (ii) a method for Bayesian estimation of missing data that minimizes the mean squared error and can obtain the confidence interval of the prediction; (iii) a generalization of the sequential dependence model for ICAMM to semi-supervised or supervised learning and multiple chains of dependence, thus allowing the use of multimodal data; and (iv) introduction of ICAMM in diverse novel applications, both for estimation and for classification.The developed methods were validated via an extensive number of simulations that covered multiple scenarios. These tested the sensitivity of the proposed methods with respect to the following parameters: number of values to estimate; kinds of source distributions; correspondence of the data with respect to the assumptions of the model; number of classes in the mixture model; and unsupervised, semi-supervised, and supervised learning. The performance of the proposed methods was evaluated using several figures of merit, and compared with the performance of multiple classical and state-of-the-art techniques for estimation and classification.Aside from the simulations, the methods were also tested on several sets of real data from different types: data from seismic exploration studies; ground penetrating radar surveys; and biomedical data. These data correspond to the following applications: reconstruction of damaged or missing data from ground-penetrating radar surveys of historical walls; reconstruction of damaged or missing data from a seismic exploration survey; reconstruction of artifacted or missing electroencephalographic (EEG) data; diagnosis of sleep disorders; modeling of the brain response during memory tasks; and exploration of EEG data from subjects performing a battery of neuropsychological tests. The obtained results demonstrate the capability of the proposed methods to work on problems wi...

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A latent variable transformation model approach for exploring dysphagia

Cited by 4 publications

References 31 publications

Dysphagia Practice in 2035: Beyond Fluorography, Thickener, and Electrical Stimulation

Dysphagia Practice in 2035: Beyond Fluorography, Thickener, and Electrical Stimulation

Analysis of Multiple Diverse Phenotypes via Semiparametric Canonical Correlation Analysis

New Insights in Prediction and Dynamic Modeling from Non-Gaussian Mixture Processing Methods

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