355 research outputs found
Avoiding Biased Clinical Machine Learning Model Performance Estimates in the Presence of Label Selection
When evaluating the performance of clinical machine learning models, one must
consider the deployment population. When the population of patients with
observed labels is only a subset of the deployment population (label
selection), standard model performance estimates on the observed population may
be misleading. In this study we describe three classes of label selection and
simulate five causally distinct scenarios to assess how particular selection
mechanisms bias a suite of commonly reported binary machine learning model
performance metrics. Simulations reveal that when selection is affected by
observed features, naive estimates of model discrimination may be misleading.
When selection is affected by labels, naive estimates of calibration fail to
reflect reality. We borrow traditional weighting estimators from causal
inference literature and find that when selection probabilities are properly
specified, they recover full population estimates. We then tackle the
real-world task of monitoring the performance of deployed machine learning
models whose interactions with clinicians feed-back and affect the selection
mechanism of the labels. We train three machine learning models to flag
low-yield laboratory diagnostics, and simulate their intended consequence of
reducing wasteful laboratory utilization. We find that naive estimates of AUROC
on the observed population undershoot actual performance by up to 20%. Such a
disparity could be large enough to lead to the wrongful termination of a
successful clinical decision support tool. We propose an altered deployment
procedure, one that combines injected randomization with traditional weighted
estimates, and find it recovers true model performance
From Lower Town to St. Cloud State: Geophysical Survey of an Evolving Urban Landscape 1869-2019
In 2019 a team of graduate students from the Cultural Resource Management Masters program, led by Rob Mann, PhD., Professor of Anthropology, under took a ground penetrating radar survey of critical sites on the St. Cloud State University campus. These were sites that had played a role in shaping the development of the University. The project was funded by a graduate student research grant from St. Cloud State University
Memory Reflected in Our Decisions: Working Memory and Risky Choice Framing
The current study looks at the role working memory plays in risky-choice framing. Eighty-six participants took the Automatic OSPAN, a measurement of working memory; this was followed by a risky-choice framing task. Results show that participants with high working memory capacities demonstrate well-pronounced framing effects, whereas those with low working memory capacities do not. This pattern suggests that, in a typical risky-choice decision task, individuals with high working memory capacity are especially likely to be influenced by contextual factors, such as the frame, and consequently demonstrate stronger framing effects
K-space data processing for Magnetic Resonance Elastography (MRE)
International audienceObject: Magnetic Resonance Elastography (MRE) requires substantial data processing based on phase image reconstruction, wave enhancement and inverse problem solving. The objective of this study is to propose a new, fast MRE method based on MR raw data processing, particularly adapted to applications requiring fast MRE measurement or high elastogram update rate.Material and Methods: The proposed method allows measuring tissue elasticity directly from raw data without prior phase image reconstruction and without phase unwrapping. Experimental feasibility is assessed both in a gelatin phantom and in the liver of a porcine model in vivo. Elastograms are reconstructed with the raw MRE method and compared to those obtained using conventional MRE. In a third experiment, changes in elasticity are monitored in real-time in a gelatin phantom during its solidification by using both conventional MRE and raw MRE.Results: The raw MRE method shows promising results by providing similar elasticity values to the ones obtained with conventional MRE methods while decreasing the number of processing steps and circumventing the delicate step of phase unwrapping. Limitations of the proposed method are the influence of the magnitude on the elastogram and the requirement for a minimum number of phase offsets.Conclusion: This study demonstrates the feasibility of directly reconstructing elastograms from raw data
Report for closeout of DE-FC02-96SF21260 - United Steelworkers Former Worker Program - Screening Exams 1996-2006
Medical Screening and surveillance of former DOE worker
Quantum dynamics of a fully-blockaded Rydberg atom ensemble
Classical simulation of quantum systems plays an important role in the study
of many-body phenomena and in the benchmarking and verification of quantum
technologies. Exact simulation is often limited to small systems because the
dimension of the Hilbert space increases exponentially with the size of the
system. For systems that possess a high degree of symmetry, however, classical
simulation can reach much larger sizes. Here, we consider an ensemble of
strongly interacting atoms with permutation symmetry, enabling the simulation
of dynamics of hundreds of atoms at arbitrarily long evolution times. The
system is realized by an ensemble of three-level atoms, where one of the levels
corresponds to a highly excited Rydberg state. In the limit of all-to-all
Rydberg blockade, the Hamiltonian is invariant under permutation of the atoms.
Using techniques from representation theory, we construct a block-diagonal form
of the Hamiltonian, where the size of the largest block increases only linearly
with the system size. We apply this formalism to derive efficient pulse
sequences to prepare arbitrary permutation-invariant quantum states. Moreover,
we study the quantum dynamics following a quench, uncovering a parameter regime
in which the system thermalizes slowly and exhibits pronounced revivals. Our
results create new opportunities for the experimental and theoretical study of
large interacting and nonintegrable quantum systems
Interventional MR Elastography for MRI-Guided Percutaneous Procedures
International audiencePURPOSE : MRI-guided thermal ablations require reliable monitoring methods to ensure complete destruction of the diseased tissue while avoiding damage to the surrounding healthy tissue. Based on the fact that thermal ablations result in substantial changes in biomechanical properties, interventional MR elastography (MRE) dedicated to the monitoring of MR-guided thermal therapies is proposed here. METHODS : Interventional MRE consists of a needle MRE driver, a fast and interactive gradient echo pulse sequence with motion encoding, and an inverse problem solver in real-time. This complete protocol was tested in vivo on swine and the ability to monitor elasticity changes in real-time was assessed in phantom. RESULTS : Thanks to a short repetition time, a reduction of the number of phase-offsets and the use of a sliding window, one refreshed elastogram was provided every 2.56 s for an excitation frequency of 100 Hz. In vivo elastograms of swine liver were successfully provided in real-time during one breath-hold. Changes of elasticity were successfully monitored in a phantom during its gelation with the same elastogram frame rate. CONCLUSION : This study demonstrates the ability of detecting elasticity changes in real-time and providing elastograms in vivo with interventional MRE that could be used for the monitoring of thermal ablations
Cyclic AMP-specific phosphodiesterase, PDE8A1, is activated by protein kinase A-mediated phosphorylation
The cyclic AMP-specific phosphodiesterase PDE8 has been shown to play a pivotal role in important processes such as steroidogenesis, T cell adhesion, regulation of heart beat and chemotaxis. However, no information exists on how the activity of this enzyme is regulated. We show that under elevated cAMP conditions, PKA acts to phosphorylate PDE8A on serine 359 and this action serves to enhance the activity of the enzyme. This is the first indication that PDE8 activity can be modulated by a kinase, and we propose that this mechanism forms a feedback loop that results in the restoration of basal cAMP levels. (C) 2012 Federation of European Biochemical Societies. Published by Elsevier B. V. All rights reserve
Risk of stomach cancer in Aotearoa/New Zealand: A Māori population based case-control study.
Māori, the indigenous people of New Zealand, experience disproportionate rates of stomach cancer, compared to non-Māori. The overall aim of the study was to better understand the reasons for the considerable excess of stomach cancer in Māori and to identify priorities for prevention. Māori stomach cancer cases from the New Zealand Cancer Registry between 1 February 2009 and 31 October 2013 and Māori controls, randomly selected from the New Zealand electoral roll were matched by 5-year age bands to cases. Logistic regression was used to estimate odd ratios (OR) and 95% confidence intervals (CI) between exposures and stomach cancer risk. Post-stratification weighting of controls was used to account for differential non-response by deprivation category. The study comprised 165 cases and 480 controls. Nearly half (47.9%) of cases were of the diffuse subtype. There were differences in the distribution of risk factors between cases and controls. Of interest were the strong relationships seen with increased stomach risk and having >2 people sharing a bedroom in childhood (OR 3.30, 95%CI 1.95-5.59), testing for H pylori (OR 12.17, 95%CI 6.15-24.08), being an ex-smoker (OR 2.26, 95%CI 1.44-3.54) and exposure to environmental tobacco smoke in adulthood (OR 3.29, 95%CI 1.94-5.59). Some results were attenuated following post-stratification weighting. This is the first national study of stomach cancer in any indigenous population and the first Māori-only population-based study of stomach cancer undertaken in New Zealand. We emphasize caution in interpreting the findings given the possibility of selection bias. Population-level strategies to reduce the incidence of stomach cancer in Māori include expanding measures to screen and treat those infected with H pylori and a continued policy focus on reducing tobacco consumption and uptake
DEPLOYR: A technical framework for deploying custom real-time machine learning models into the electronic medical record
Machine learning (ML) applications in healthcare are extensively researched,
but successful translations to the bedside are scant. Healthcare institutions
are establishing frameworks to govern and promote the implementation of
accurate, actionable and reliable models that integrate with clinical workflow.
Such governance frameworks require an accompanying technical framework to
deploy models in a resource efficient manner. Here we present DEPLOYR, a
technical framework for enabling real-time deployment and monitoring of
researcher created clinical ML models into a widely used electronic medical
record (EMR) system. We discuss core functionality and design decisions,
including mechanisms to trigger inference based on actions within EMR software,
modules that collect real-time data to make inferences, mechanisms that
close-the-loop by displaying inferences back to end-users within their
workflow, monitoring modules that track performance of deployed models over
time, silent deployment capabilities, and mechanisms to prospectively evaluate
a deployed model's impact. We demonstrate the use of DEPLOYR by silently
deploying and prospectively evaluating twelve ML models triggered by clinician
button-clicks in Stanford Health Care's production instance of Epic. Our study
highlights the need and feasibility for such silent deployment, because
prospectively measured performance varies from retrospective estimates. By
describing DEPLOYR, we aim to inform ML deployment best practices and help
bridge the model implementation gap
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