A Comparison of Patient History- and EKG-based Cardiac Risk Scores.

Patient-specific risk scores are used to identify individuals at elevated risk for cardiovascular disease. Typically, risk scores are based on patient habits and medical history - age, sex, race, smoking behavior, and prior vital signs and diagnoses. We explore an alternative source of information, a patient's raw electrocardiogram recording, and develop a score of patient risk for various outcomes. We compare models that predict adverse cardiac outcomes following an emergency department visit, and show that a learned representation (e.g. deep neural network) of raw EKG waveforms can improve prediction over traditional risk factors. Further, we show that a simple model based on segmented heart beats performs as well or better than a complex convolutional network recently shown to reliably automate arrhythmia detection in EKGs. We analyze a large cohort of emergency department patients and show evidence that EKG-derived scores can be more robust to patient heterogeneity

A short note on the presence of spurious states in finite basis approximations

The genesis of spurious solutions in finite basis approximations to operators which possess a continuum and a point spectrum is discussed and a simple solution for identifying these solutions is suggested

Media Sports Stars: Masculinities and Moralities (Book Review)

Book review by Andrew C. Miller. Whannel, Garry. Media Sports Stars: Masculinities and Moralities. London; New York: Routledge, 2002. ISBN 9780415170376; 9780415170383 (pbk.

Stratal Architecture in a Prograding Shoreface Deposit, Eastern Shore, VA: Relationship to Grain Size, Permeability, and Facies Distribution

A fundamental concern of the stratigrapher is to develop predictive models of stratigraphic organization. In sedimentology one of the most significant problems that has yet to be resolved is the fact that there is a lack of quantitative information regarding the relationship between geometry of beds, thickness of beds, grain size and sedimentary structures in sandy environments, especially shallow marine deposits. Scientists have also realized the need to correlate quantitative permeability to sedimentary structures and scales of stratigraphic organization. The purpose of the study is to investigate the scales of stratigraphic organization that control the variation of grain size and permeability in shallow marine deposits. A model of stratal architecture is constructed in order to relate scales of stratigraphic organization to these properties. The hypothesis tested is that models of stratal architecture are more efficient predictors of grain size and permeability than are facies models in shallow marine sands. Several methods are used to test the hypothesis, including mapping of stratal geometry, measuring stratal characteristics, and the construction of facies distribution through measured sections. These techniques are used to erect the stratal architecture of strand plain deposits at Oyster, Virginia. ANOVA, Tukey-Kramer Means Comparisons tests and variograms are performed to test the statistical significance of mean grain size and permeability variability over multiple scales of stratigraphic organization. Results from this study demonstrate that multiple levels of stratigraphic organization are statistically significant with respect to the spatial variability of grain size and permeability, and that one-dimensional facies models are clearly unable to resolve these important stratigraphic scales. The study also revealed that a parabolic relationship exists between mean grain size and set thickness, and is thought to be the evolutionary consequence of the progressive sorting process

Reducing Reparameterization Gradient Variance

Optimization with noisy gradients has become ubiquitous in statistics and machine learning. Reparameterization gradients, or gradient estimates computed via the "reparameterization trick," represent a class of noisy gradients often used in Monte Carlo variational inference (MCVI). However, when these gradient estimators are too noisy, the optimization procedure can be slow or fail to converge. One way to reduce noise is to use more samples for the gradient estimate, but this can be computationally expensive. Instead, we view the noisy gradient as a random variable, and form an inexpensive approximation of the generating procedure for the gradient sample. This approximation has high correlation with the noisy gradient by construction, making it a useful control variate for variance reduction. We demonstrate our approach on non-conjugate multi-level hierarchical models and a Bayesian neural net where we observed gradient variance reductions of multiple orders of magnitude (20-2,000x)

A comparison of broad iron emission lines in archival data of neutron star low-mass X-ray binaries

Relativistic X-ray disk-lines have been found in multiple neutron star low-mass X-ray binaries, in close analogy with black holes across the mass-scale. These lines have tremendous diagnostic power and have been used to constrain stellar radii and magnetic fields, often finding values that are consistent with independent timing techniques. Here, we compare CCD-based data from Suzaku with Fe K line profiles from archival data taken with gas-based spectrometers. In general, we find good consistency between the gas-based line profiles from EXOSAT, BeppoSAX and RXTE and the CCD data from Suzaku, demonstrating that the broad profiles seen are intrinsic to the line and not broad due to instrumental issues. However, we do find that when fitting with a Gaussian line profile, the width of the Gaussian can depend on the continuum model in instruments with low spectral resolution, though when the different models fit equally well the line widths generally agree. We also demonstrate that three BeppoSAX observations show evidence for asymmetric lines, with a relativistic disk-line model providing a significantly better fit than a Gaussian. We test this by using the posterior predictive p-value method, and bootstrapping of the spectra to show that such deviations from a Gaussian are unlikely to be observed by chance.Comment: 13 pages, 9 figures, accepted to Ap