167 research outputs found
Development of the patient experience questionnaire for parents of pediatric patients (PEQP)
Patient experience (PX) is an important evaluation criterion for quality in healthcare. Compared to patient satisfaction, however less research has focused on the development of instruments to measure experiences of patients and their families. In the article, we describe the process of developing a PX questionnaire for the parents of pediatric patients in the context of children's hospital and illustrate the questionnaire items for measuring PX. The phases of the development process included retrospective interviews, description of the themes influencing PX and the metrics for measuring PX, as well as iterative development of three versions of questionnaires including data gathering and factor analysis. The final versions of the surveys suggested for implementation at the hospitals include eight PX statements for the outpatient clinic and five statements for the ward. Compared to satisfaction surveys, the developed surveys emphasize the aspects of parent's attitude towards the illness, support for families, and daily arrangements with a child patient. © 2019 American Psychological Association Inc. All rights reserved.Peer reviewe
Sensitivity analysis and variance reduction in a stochastic NDT problem
In this paper, we present a framework to deal with uncertainty quantification in case where the ranges of variability of the random parameters are ill-known. Namely the physical properties of the corrosion product (magnetite) which frequently clogs the tube support plate of steam generator, which is inaccessible in nuclear power plants. The methodology is based on Polynomial Chaos (PC) for the direct approach and on Bayesian inference for the inverse approach. The direct Non-Intrusive Spectral Projection (NISP) method is first employed by considering prior probability densities and therefore constructing a PC surrogate model of the large-scale NDT finite element model. To face the prohibitive computational cost underlying the high dimensional random space, an adaptive sparse grid technique is applied on NISP resulting in drastic time reduction. The PC surrogate model, with reduced dimensionality, is used as a forward model in the Bayesian procedure. The posterior probability densities are then identified by inferring from few noisy experimental data. We demonstrate effectiveness of the approach by identifying the most influential parameter in the clogging detection as well as a variability range reduction
Retrieval of process rate parameters in the general dynamic equation for aerosols using Bayesian state estimation: BAYROSOL1.0
The uncertainty in the radiative forcing caused by aerosols and its effect on climate change calls for research to improve knowledge of the aerosol
particle formation and growth processes. While experimental research has
provided a large amount of high-quality data on aerosols over the last 2Â decades, the inference of the process rates is still inadequate, mainly due to
limitations in the analysis of data. This paper focuses on developing
computational methods to infer aerosol process rates from size distribution
measurements. In the proposed approach, the temporal evolution of aerosol
size distributions is modeled with the general dynamic equation (GDE) equipped with
stochastic terms that account for the uncertainties of the process rates. The
time-dependent particle size distribution and the rates of the underlying
formation and growth processes are reconstructed based on time series of
particle analyzer data using Bayesian state estimation â which not only
provides (point) estimates for the process rates but also enables quantification of
their uncertainties. The feasibility of the proposed computational framework
is demonstrated by a set of numerical simulation studies.</p
A Subset of Secreted Proteins in Ascites Can Predict Platinum-Free Interval in Ovarian Cancer
The time between the last cycle of chemotherapy and recurrence, the platinum-free interval (PFI), predicts overall survival in high-grade serous ovarian cancer (HGSOC). To identify secreted proteins associated with a shorter PFI, we utilized machine learning to predict the PFI from ascites composition. Ascites from stage III/IV HGSOC patients treated with neoadjuvant chemotherapy (NACT) or primary debulking surgery (PDS) were screened for secreted proteins and Lasso regression models were built to predict the PFI. Through regularization techniques, the number of analytes used in each model was reduced; to minimize overfitting, we utilized an analysis of model robustness. This resulted in models with 26 analytes and a root-mean-square error (RMSE) of 19 days for the NACT cohort and 16 analytes and an RMSE of 7 days for the PDS cohort. High concentrations of MMP-2 and EMMPRIN correlated with a shorter PFI in the NACT patients, whereas high concentrations of uPA Urokinase and MMP-3 correlated with a shorter PFI in PDS patients. Our results suggest that the analysis of ascites may be useful for outcome prediction and identified factors in the tumor microenvironment that may lead to worse outcomes. Our approach to tuning for model stability, rather than only model accuracy, may be applicable to other biomarker discovery tasks
On dimension reduction in Gaussian filters
A priori dimension reduction is a widely adopted technique for reducing the
computational complexity of stationary inverse problems. In this setting, the
solution of an inverse problem is parameterized by a low-dimensional basis that
is often obtained from the truncated Karhunen-Loeve expansion of the prior
distribution. For high-dimensional inverse problems equipped with smoothing
priors, this technique can lead to drastic reductions in parameter dimension
and significant computational savings.
In this paper, we extend the concept of a priori dimension reduction to
non-stationary inverse problems, in which the goal is to sequentially infer the
state of a dynamical system. Our approach proceeds in an offline-online
fashion. We first identify a low-dimensional subspace in the state space before
solving the inverse problem (the offline phase), using either the method of
"snapshots" or regularized covariance estimation. Then this subspace is used to
reduce the computational complexity of various filtering algorithms - including
the Kalman filter, extended Kalman filter, and ensemble Kalman filter - within
a novel subspace-constrained Bayesian prediction-and-update procedure (the
online phase). We demonstrate the performance of our new dimension reduction
approach on various numerical examples. In some test cases, our approach
reduces the dimensionality of the original problem by orders of magnitude and
yields up to two orders of magnitude in computational savings
Personalized drug sensitivity screening for bladder cancer using conditionally reprogrammed patient-derived cells
Many patients with muscle-invasive bladder cancer (BC) are either ineligible for or do not benefit from cisplatin-based chemotherapy, and there is an unmet need to estimate individualsâ drug sensitivities. We investigated the suitability of conditionally reprogrammed (CR) cells for the characterization of BC properties and their feasibility for personalized drug sensitivity screening. The CR cultures were established from six BC tumors with varying histology and stage. Four cultures were successfully propagated for genomic, transcriptomic, and protein expression profiling and compared to the parental tumors. Two out of four CR cultures (urothelial carcinoma and small cell neuroendocrine carcinoma [SmCC]) corresponded well to their parental tumors and underwent drug sensitivity screening to identify novel drugs for the respective tumors. Both cultures were sensitive to standard BC chemotherapy agents (eg cisplatin and gemcitabine) and to conventional drugs such as taxanes and inhibitors of topoisomerase and proteasome. The SmCC cells were also sensitive to statins (eg, atorvastatin and pitavastatin). In summary, after confirming their representativeness and origin, we conclude that CR cells are a feasible platform for personalized drug sensitivity testing and might thus add to the approaches used to personalize BC treatment strategies
An approximate empirical Bayesian method for large-scale linear-Gaussian inverse problems
We study Bayesian inference methods for solving linear inverse problems,
focusing on hierarchical formulations where the prior or the likelihood
function depend on unspecified hyperparameters. In practice, these
hyperparameters are often determined via an empirical Bayesian method that
maximizes the marginal likelihood function, i.e., the probability density of
the data conditional on the hyperparameters. Evaluating the marginal
likelihood, however, is computationally challenging for large-scale problems.
In this work, we present a method to approximately evaluate marginal likelihood
functions, based on a low-rank approximation of the update from the prior
covariance to the posterior covariance. We show that this approximation is
optimal in a minimax sense. Moreover, we provide an efficient algorithm to
implement the proposed method, based on a combination of the randomized SVD and
a spectral approximation method to compute square roots of the prior covariance
matrix. Several numerical examples demonstrate good performance of the proposed
method
Fisher Information for Inverse Problems and Trace Class Operators
This paper provides a mathematical framework for Fisher information analysis
for inverse problems based on Gaussian noise on infinite-dimensional Hilbert
space. The covariance operator for the Gaussian noise is assumed to be trace
class, and the Jacobian of the forward operator Hilbert-Schmidt. We show that
the appropriate space for defining the Fisher information is given by the
Cameron-Martin space. This is mainly because the range space of the covariance
operator always is strictly smaller than the Hilbert space. For the Fisher
information to be well-defined, it is furthermore required that the range space
of the Jacobian is contained in the Cameron-Martin space. In order for this
condition to hold and for the Fisher information to be trace class, a
sufficient condition is formulated based on the singular values of the Jacobian
as well as of the eigenvalues of the covariance operator, together with some
regularity assumptions regarding their relative rate of convergence. An
explicit example is given regarding an electromagnetic inverse source problem
with "external" spherically isotropic noise, as well as "internal" additive
uncorrelated noise.Comment: Submitted to Journal of Mathematical Physic
Consistency of the posterior distribution in generalized linear inverse problems
For ill-posed inverse problems, a regularised solution can be interpreted as
a mode of the posterior distribution in a Bayesian framework. This framework
enriches the set the solutions, as other posterior estimates can be used as a
solution to the inverse problem, such as the posterior mean that can be easier
to compute in practice. In this paper we prove consistency of Bayesian
solutions of an ill-posed linear inverse problem in the Ky Fan metric for a
general class of likelihoods and prior distributions in a finite dimensional
setting. This result can be applied to study infinite dimensional problems by
letting the dimension of the unknown parameter grow to infinity which can be
viewed as discretisation on a grid or spectral approximation of an infinite
dimensional problem. Likelihood and the prior distribution are assumed to be in
an exponential form that includes distributions from the exponential family,
and to be differentiable. The observations can be dependent. No assumption of
finite moments of observations, such as expected value or the variance, is
necessary thus allowing for possibly non-regular likelihoods, and allowing for
non-conjugate and improper priors. If the variance exists, it may be
heteroscedastic, namely, it may depend on the unknown function. We observe
quite a surprising phenomenon when applying our result to the spectral
approximation framework where it is possible to achieve the parametric rate of
convergence, i.e the problem becomes self-regularised. We also consider a
particular case of the unknown parameter being on the boundary of the parameter
set, and show that the rate of convergence in this case is faster than for an
interior point parameter.Comment: arXiv admin note: substantial text overlap with arXiv:1110.301
A Subset of Secreted Proteins in Ascites Can Predict Platinum-Free Interval in Ovarian Cancer
Simple SummaryIdentifying proteins that correlate with better or worse outcomes may help to identify new treatment approaches for advanced high-grade serous ovarian cancer. Here, we utilize a machine learning technique to correlate the levels of 58 secreted proteins in tumor ascites with the time to disease recurrence after chemotherapy, which is known clinically as the platinum-free interval. We identify several candidate proteins correlated to shorter or longer platinum-free intervals and describe model analysis methods that may be useful for other studies aiming to identify factors impacting patient outcomes. Future validation of these factors in a prospective cohort would confirm their clinical utility, whereas a study of the mechanisms that they impact may identify new therapies. The time between the last cycle of chemotherapy and recurrence, the platinum-free interval (PFI), predicts overall survival in high-grade serous ovarian cancer (HGSOC). To identify secreted proteins associated with a shorter PFI, we utilized machine learning to predict the PFI from ascites composition. Ascites from stage III/IV HGSOC patients treated with neoadjuvant chemotherapy (NACT) or primary debulking surgery (PDS) were screened for secreted proteins and Lasso regression models were built to predict the PFI. Through regularization techniques, the number of analytes used in each model was reduced; to minimize overfitting, we utilized an analysis of model robustness. This resulted in models with 26 analytes and a root-mean-square error (RMSE) of 19 days for the NACT cohort and 16 analytes and an RMSE of 7 days for the PDS cohort. High concentrations of MMP-2 and EMMPRIN correlated with a shorter PFI in the NACT patients, whereas high concentrations of uPA Urokinase and MMP-3 correlated with a shorter PFI in PDS patients. Our results suggest that the analysis of ascites may be useful for outcome prediction and identified factors in the tumor microenvironment that may lead to worse outcomes. Our approach to tuning for model stability, rather than only model accuracy, may be applicable to other biomarker discovery tasks.</p
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