14,908 research outputs found
Exploratory Mediation Analysis with Many Potential Mediators
Social and behavioral scientists are increasingly employing technologies such
as fMRI, smartphones, and gene sequencing, which yield 'high-dimensional'
datasets with more columns than rows. There is increasing interest, but little
substantive theory, in the role the variables in these data play in known
processes. This necessitates exploratory mediation analysis, for which
structural equation modeling is the benchmark method. However, this method
cannot perform mediation analysis with more variables than observations. One
option is to run a series of univariate mediation models, which incorrectly
assumes independence of the mediators. Another option is regularization, but
the available implementations may lead to high false positive rates. In this
paper, we develop a hybrid approach which uses components of both filter and
regularization: the 'Coordinate-wise Mediation Filter'. It performs filtering
conditional on the other selected mediators. We show through simulation that it
improves performance over existing methods. Finally, we provide an empirical
example, showing how our method may be used for epigenetic research.Comment: R code and package are available online as supplementary material at
https://github.com/vankesteren/cmfilter and
https://github.com/vankesteren/ema_simulation
Heritability of pain catastrophizing and associations with experimental pain outcomes: a twin study
This study used a twin paradigm to examine genetic and environmental contributions to pain catastrophizing and the observed association between pain Catastrophizing and cold-pressor task (CPT) outcomes. Male and female monozygotic (n = 206) and dizygotic twins (n = 194) torn the University of Washington Twin Registry completed a measure of pain catastrophizing and performed a CPT challenge, As expected, pain catastrophizing emerged as a significant predictor of several CPT outcomeS, including cold-pressor Immersion Tolerance, Pain Tolerance, and Delayed Pain Rating. The heritability estimate for pain catastrophizing was found to be 37% with the remaining 63% of variance attributable to unique environmental influence. Additionally, the Observed associations between pain catastrophizing and CPT outcomes were not found attributable to shared genetics or environmental exposure, which suggests a direct relationship between catastrophizing and experimental pain. outcomes. This Study is the first to examine the heritability of pain catastrophizing and potential processes by which pain catastrophizing is related to experimental pain response
Multi-score Learning for Affect Recognition: the Case of Body Postures
An important challenge in building automatic affective state
recognition systems is establishing the ground truth. When the groundtruth
is not available, observers are often used to label training and testing
sets. Unfortunately, inter-rater reliability between observers tends to
vary from fair to moderate when dealing with naturalistic expressions.
Nevertheless, the most common approach used is to label each expression
with the most frequent label assigned by the observers to that expression.
In this paper, we propose a general pattern recognition framework
that takes into account the variability between observers for automatic
affect recognition. This leads to what we term a multi-score learning
problem in which a single expression is associated with multiple values
representing the scores of each available emotion label. We also propose
several performance measurements and pattern recognition methods for
this framework, and report the experimental results obtained when testing
and comparing these methods on two affective posture datasets
Hybrid statistical and mechanistic mathematical model guides mobile health intervention for chronic pain
Nearly a quarter of visits to the Emergency Department are for conditions
that could have been managed via outpatient treatment; improvements that allow
patients to quickly recognize and receive appropriate treatment are crucial.
The growing popularity of mobile technology creates new opportunities for
real-time adaptive medical intervention, and the simultaneous growth of big
data sources allows for preparation of personalized recommendations. Here we
focus on the reduction of chronic suffering in the sickle cell disease
community. Sickle cell disease is a chronic blood disorder in which pain is the
most frequent complication. There currently is no standard algorithm or
analytical method for real-time adaptive treatment recommendations for pain.
Furthermore, current state-of-the-art methods have difficulty in handling
continuous-time decision optimization using big data. Facing these challenges,
in this study we aim to develop new mathematical tools for incorporating mobile
technology into personalized treatment plans for pain. We present a new hybrid
model for the dynamics of subjective pain that consists of a dynamical systems
approach using differential equations to predict future pain levels, as well as
a statistical approach tying system parameters to patient data (both personal
characteristics and medication response history). Pilot testing of our approach
suggests that it has significant potential to predict pain dynamics given
patients' reported pain levels and medication usages. With more abundant data,
our hybrid approach should allow physicians to make personalized, data driven
recommendations for treating chronic pain.Comment: 13 pages, 15 figures, 5 table
Cumulative Burden of Morbidity Among Testicular Cancer Survivors After Standard Cisplatin-Based Chemotherapy: A Multi-Institutional Study
Purpose In this multicenter study, we evaluated the cumulative burden of morbidity (CBM) among > 1,200 testicular cancer survivors and applied factor analysis to determine the co-occurrence of adverse health outcomes (AHOs). Patients and Methods Participants were ≤ 55 years of age at diagnosis, finished first-line chemotherapy ≥ 1 year previously, completed a comprehensive questionnaire, and underwent physical examination. Treatment data were abstracted from medical records. A CBM score encompassed the number and severity of AHOs, with ordinal logistic regression used to assess associations with exposures. Nonlinear factor analysis and the nonparametric dimensionality evaluation to enumerate contributing traits procedure determined which AHOs co-occurred. Results Among 1,214 participants, approximately 20% had a high (15%) or very high/severe (4.1%) CBM score, whereas approximately 80% scored medium (30%) or low/very low (47%). Increased risks of higher scores were associated with four cycles of either ifosfamide, etoposide, and cisplatin (odds ratio [OR], 1.96; 95% CI, 1.04 to 3.71) or bleomycin, etoposide, and cisplatin (OR, 1.44; 95% CI, 1.04 to 1.98), older attained age (OR, 1.18; 95% CI, 1.10 to 1.26), current disability leave (OR, 3.53; 95% CI, 1.57 to 7.95), less than a college education (OR, 1.44; 95% CI, 1.11 to 1.87), and current or former smoking (OR, 1.28; 95% CI, 1.02 to 1.63). CBM score did not differ after either chemotherapy regimen ( P = .36). Asian race (OR, 0.41; 95% CI, 0.23 to 0.72) and vigorous exercise (OR, 0.68; 95% CI, 0.52 to 0.89) were protective. Variable clustering analyses identified six significant AHO clusters (χ2 P < .001): hearing loss/damage, tinnitus (OR, 16.3); hyperlipidemia, hypertension, diabetes (OR, 9.8); neuropathy, pain, Raynaud phenomenon (OR, 5.5); cardiovascular and related conditions (OR, 5.0); thyroid disease, erectile dysfunction (OR, 4.2); and depression/anxiety, hypogonadism (OR, 2.8). Conclusion Factors associated with higher CBM may identify testicular cancer survivors in need of closer monitoring. If confirmed, identified AHO clusters could guide the development of survivorship care strategies
A Survey on Bayesian Deep Learning
A comprehensive artificial intelligence system needs to not only perceive the
environment with different `senses' (e.g., seeing and hearing) but also infer
the world's conditional (or even causal) relations and corresponding
uncertainty. The past decade has seen major advances in many perception tasks
such as visual object recognition and speech recognition using deep learning
models. For higher-level inference, however, probabilistic graphical models
with their Bayesian nature are still more powerful and flexible. In recent
years, Bayesian deep learning has emerged as a unified probabilistic framework
to tightly integrate deep learning and Bayesian models. In this general
framework, the perception of text or images using deep learning can boost the
performance of higher-level inference and in turn, the feedback from the
inference process is able to enhance the perception of text or images. This
survey provides a comprehensive introduction to Bayesian deep learning and
reviews its recent applications on recommender systems, topic models, control,
etc. Besides, we also discuss the relationship and differences between Bayesian
deep learning and other related topics such as Bayesian treatment of neural
networks.Comment: To appear in ACM Computing Surveys (CSUR) 202
- …