10,639 research outputs found
TeamSTEPPS and Organizational Culture
Patient safety issues remain despite several strategies developed for their deterrence. While many safety initiatives bring about improvement, they are repeatedly unsustainable and short-lived. The index hospital’s goal was to build an organizational culture within a groundwork that improves teamwork and continuing healthcare team engagement. Teamwork influences the efficiency of patient care, patient safety, and clinical outcomes, as it has been identified as an approach for enhancing collaboration, decreasing medical errors, and building a culture of safety in healthcare. The facility implemented Team Strategies and Tools to Enhance Performance and Patient Safety (TeamSTEPPS), an evidence-based framework which was used for team training to produce valuable and needed changes, facilitating modification of organizational culture, increasing patient safety compliance, or solving particular issues. This study aimed to identify the correlation between TeamSTEPPS enactment and improved organizational culture in the ambulatory care nursing department of a New York City public hospital
Machine Learning Approaches for the Prioritisation of Cardiovascular Disease Genes Following Genome- wide Association Study
Genome-wide association studies (GWAS) have revealed thousands of genetic loci, establishing itself as a valuable method for unravelling the complex biology of many diseases. As GWAS has grown in size and improved in study design to detect effects, identifying real causal signals, disentangling from other highly correlated markers associated by linkage disequilibrium (LD) remains challenging. This has severely limited GWAS findings and brought the method’s value into question. Although thousands of disease susceptibility loci have been reported, causal variants and genes at these loci remain elusive. Post-GWAS analysis aims to dissect the heterogeneity of variant and gene signals. In recent years, machine learning (ML) models have been developed for post-GWAS prioritisation. ML models have ranged from using logistic regression to more complex ensemble models such as random forests and gradient boosting, as well as deep learning models (i.e., neural networks). When combined with functional validation, these methods have shown important translational insights, providing a strong evidence-based approach to direct post-GWAS research. However, ML approaches are in their infancy across biological applications, and as they continue to evolve an evaluation of their robustness for GWAS prioritisation is needed. Here, I investigate the landscape of ML across: selected models, input features, bias risk, and output model performance, with a focus on building a prioritisation framework that is applied to blood pressure GWAS results and tested on re-application to blood lipid traits
Optimal neighbourhood selection in structural equation models
We study the optimal sample complexity of neighbourhood selection in linear
structural equation models, and compare this to best subset selection (BSS) for
linear models under general design. We show by example that -- even when the
structure is \emph{unknown} -- the existence of underlying structure can reduce
the sample complexity of neighbourhood selection. This result is complicated by
the possibility of path cancellation, which we study in detail, and show that
improvements are still possible in the presence of path cancellation. Finally,
we support these theoretical observations with experiments. The proof
introduces a modified BSS estimator, called klBSS, and compares its performance
to BSS. The analysis of klBSS may also be of independent interest since it
applies to arbitrary structured models, not necessarily those induced by a
structural equation model. Our results have implications for structure learning
in graphical models, which often relies on neighbourhood selection as a
subroutine
Task-specific experimental design for treatment effect estimation
Understanding causality should be a core requirement of any attempt to build
real impact through AI. Due to the inherent unobservability of counterfactuals,
large randomised trials (RCTs) are the standard for causal inference. But large
experiments are generically expensive, and randomisation carries its own costs,
e.g. when suboptimal decisions are trialed. Recent work has proposed more
sample-efficient alternatives to RCTs, but these are not adaptable to the
downstream application for which the causal effect is sought. In this work, we
develop a task-specific approach to experimental design and derive sampling
strategies customised to particular downstream applications. Across a range of
important tasks, real-world datasets, and sample sizes, our method outperforms
other benchmarks, e.g. requiring an order-of-magnitude less data to match RCT
performance on targeted marketing tasks.Comment: To appear in ICML 2023; 8 pages, 7 figures, 4 appendice
A New Paradigm for Generative Adversarial Networks based on Randomized Decision Rules
The Generative Adversarial Network (GAN) was recently introduced in the
literature as a novel machine learning method for training generative models.
It has many applications in statistics such as nonparametric clustering and
nonparametric conditional independence tests. However, training the GAN is
notoriously difficult due to the issue of mode collapse, which refers to the
lack of diversity among generated data. In this paper, we identify the reasons
why the GAN suffers from this issue, and to address it, we propose a new
formulation for the GAN based on randomized decision rules. In the new
formulation, the discriminator converges to a fixed point while the generator
converges to a distribution at the Nash equilibrium. We propose to train the
GAN by an empirical Bayes-like method by treating the discriminator as a
hyper-parameter of the posterior distribution of the generator. Specifically,
we simulate generators from its posterior distribution conditioned on the
discriminator using a stochastic gradient Markov chain Monte Carlo (MCMC)
algorithm, and update the discriminator using stochastic gradient descent along
with simulations of the generators. We establish convergence of the proposed
method to the Nash equilibrium. Apart from image generation, we apply the
proposed method to nonparametric clustering and nonparametric conditional
independence tests. A portion of the numerical results is presented in the
supplementary material
Mediation Analysis with Graph Mediator
This study introduces a mediation analysis framework when the mediator is a
graph. A Gaussian covariance graph model is assumed for graph representation.
Causal estimands and assumptions are discussed under this representation. With
a covariance matrix as the mediator, parametric mediation models are imposed
based on matrix decomposition. Assuming Gaussian random errors,
likelihood-based estimators are introduced to simultaneously identify the
decomposition and causal parameters. An efficient computational algorithm is
proposed and asymptotic properties of the estimators are investigated. Via
simulation studies, the performance of the proposed approach is evaluated.
Applying to a resting-state fMRI study, a brain network is identified within
which functional connectivity mediates the sex difference in the performance of
a motor task
A unified recipe for deriving (time-uniform) PAC-Bayes bounds
We present a unified framework for deriving PAC-Bayesian generalization
bounds. Unlike most previous literature on this topic, our bounds are
anytime-valid (i.e., time-uniform), meaning that they hold at all stopping
times, not only for a fixed sample size. Our approach combines four tools in
the following order: (a) nonnegative supermartingales or reverse
submartingales, (b) the method of mixtures, (c) the Donsker-Varadhan formula
(or other convex duality principles), and (d) Ville's inequality. Our main
result is a PAC-Bayes theorem which holds for a wide class of discrete
stochastic processes. We show how this result implies time-uniform versions of
well-known classical PAC-Bayes bounds, such as those of Seeger, McAllester,
Maurer, and Catoni, in addition to many recent bounds. We also present several
novel bounds. Our framework also enables us to relax traditional assumptions;
in particular, we consider nonstationary loss functions and non-i.i.d. data. In
sum, we unify the derivation of past bounds and ease the search for future
bounds: one may simply check if our supermartingale or submartingale conditions
are met and, if so, be guaranteed a (time-uniform) PAC-Bayes bound.Comment: 46 page
Conditional Feature Importance for Mixed Data
Despite the popularity of feature importance (FI) measures in interpretable
machine learning, the statistical adequacy of these methods is rarely
discussed. From a statistical perspective, a major distinction is between
analyzing a variable's importance before and after adjusting for covariates -
i.e., between and measures. Our work
draws attention to this rarely acknowledged, yet crucial distinction and
showcases its implications. Further, we reveal that for testing conditional FI,
only few methods are available and practitioners have hitherto been severely
restricted in method application due to mismatching data requirements. Most
real-world data exhibits complex feature dependencies and incorporates both
continuous and categorical data (mixed data). Both properties are oftentimes
neglected by conditional FI measures. To fill this gap, we propose to combine
the conditional predictive impact (CPI) framework with sequential knockoff
sampling. The CPI enables conditional FI measurement that controls for any
feature dependencies by sampling valid knockoffs - hence, generating synthetic
data with similar statistical properties - for the data to be analyzed.
Sequential knockoffs were deliberately designed to handle mixed data and thus
allow us to extend the CPI approach to such datasets. We demonstrate through
numerous simulations and a real-world example that our proposed workflow
controls type I error, achieves high power and is in line with results given by
other conditional FI measures, whereas marginal FI metrics result in misleading
interpretations. Our findings highlight the necessity of developing
statistically adequate, specialized methods for mixed data
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