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ObjectivesWe validate a machine learning-based sepsis-prediction algorithm (InSight) for the detection and prediction of three sepsis-related gold standards, using only six vital signs. We evaluate robustness to missing data, customisation to site-specific data using transfer learning and generalisability to new settings.DesignA machine-learning algorithm with gradient tree boosting. Features for prediction were created from combinations of six vital sign measurements and their changes over time.SettingA mixed-ward retrospective dataset from the University of California, San Francisco (UCSF) Medical Center (San Francisco, California, USA) as the primary source, an intensive care unit dataset from the Beth Israel Deaconess Medical Center (Boston, Massachusetts, USA) as a transfer-learning source and four additional institutions’ datasets to evaluate generalisability.Participants684 443 total encounters, with 90 353 encounters from June 2011 to March 2016 at UCSF.InterventionsNone.Primary and secondary outcome measuresArea under the receiver operating characteristic (AUROC) curve for detection and prediction of sepsis, severe sepsis and septic shock.ResultsFor detection of sepsis and severe sepsis, InSight achieves an AUROC curve of 0.92 (95% CI 0.90 to 0.93) and 0.87 (95% CI 0.86 to 0.88), respectively. Four hours before onset, InSight predicts septic shock with an AUROC of 0.96 (95% CI 0.94 to 0.98) and severe sepsis with an AUROC of 0.85 (95% CI 0.79 to 0.91).ConclusionsInSight outperforms existing sepsis scoring systems in identifying and predicting sepsis, severe sepsis and septic shock. This is the first sepsis screening system to exceed an AUROC of 0.90 using only vital sign inputs. InSight is robust to missing data, can be customised to novel hospital data using a small fraction of site data and retains strong discrimination across all institutions.

We present vide, the Void IDentification and Examination toolkit, an open-source Python/C++ code for finding cosmic voids in galaxy redshift surveys and N -body simulations, characterizing their properties, and providing a platform for more detailed analysis. At its core, vide uses a substantially enhanced version of zobov (Neyinck 2008) to calculate a Voronoi tessellation for estimating the density field and a performing a watershed transform to construct voids. Additionally, vide provides significant functionality for both pre-and post-processing: for example, vide can work with volume-or magnitude-limited galaxy samples with arbitrary survey geometries, or dark matter particles or halo catalogs in a variety of common formats. It can also randomly subsample inputs and includes a Halo Occupation Distribution model for constructing mock galaxy populations. vide uses the watershed levels to place voids in a hierarchical tree, outputs a summary of void properties in plain ASCII, and provides a Python API to perform many analysis tasks, such as loading and manipulating void catalogs and particle members, filtering, plotting, computing clustering statistics, stacking, comparing catalogs, and fitting density profiles. While centered around ZOBOV, the toolkit is designed to be as modular as possible and accommodate other void finders. vide has been in development for several years and has already been used to produce a wealth of results, which we summarize in this work to highlight the capabilities of the toolkit. vide is publicly available at http://bitbucket.org/cosmicvoids/vide public and http://www.cosmicvoids.net.

We have searched for the signature of cosmic voids in the CMB, in both the Planck temperature and lensing-convergence maps; voids should give decrements in both. We use ZOBOV voids from the DR12 SDSS CMASS galaxy sample. We base our analysis on N -body simulations, to avoid a posteriori bias. For the first time, we detect the signature of voids in CMB lensing: the significance is 3.2σ, close to ΛCDM in both amplitude and projected densityprofile shape. A temperature dip is also seen, at modest significance (2.3σ), with amplitude about 6 times the prediction. This temperature signal is induced mostly by voids with radius between 100 and 150 h −1 Mpc, while the lensing signal is mostly contributed by smaller voids -as expected; lensing relates directly to density, while ISW depends on gravitational potential. The void abundance in observations and simulations agree, as well. We also repeated the analysis excluding lower-significance voids: no lensing signal is detected, with an upper limit of about twice the ΛCDM prediction. But the mean temperature decrement now becomes non-zero at the 3.7σ level (similar to that found by Granett et al.), with amplitude about 20 times the prediction. However, the observed dependence of temperature on void size is in poor agreement with simulations, whereas the lensing results are consistent with ΛCDM theory. Thus, the overall tension between theory and observations does not favour non-standard theories of gravity, despite the hints of an enhanced amplitude for the ISW effect from voids.

We apply the Alcock-Paczyński (AP) test to the stacked voids identified using the large-scale structure galaxy catalog from the Baryon Oscillation Spectroscopic Survey (BOSS). This galaxy catalog is part of the Sloan Digital Sky Survey Data Release 12 and is the final catalog of SDSS-III. We also use 1000 mock galaxy catalogs that match the geometry, density, and clustering properties of the BOSS sample in order to characterize the statistical uncertainties of our measurements and take into account systematic errors such as redshift space distortions. For both BOSS data and mock catalogs, we use the ZOBOV algorithm to identify voids, we stack together all voids with effective radii of 30 − 100h −1 Mpc in the redshift range 0.43 − 0.7, and we accurately measure the shape of the stacked voids. Our tests with the mock catalogs show that we measure the stacked void ellipticity with a statistical precision of 2.6%. The stacked voids in redshift space are slightly squashed along the line of sight, which is consistent with previous studies. We repeat this measurement of stacked void shape in the BOSS data assuming several values of Ω m within the flat ΛCDM model, and we compare to the mock catalogs in redshift space in order to perform the AP test. We obtain a constraint of Ω m = 0.38 +0.18 −0.15 at the 68% confidence level from the AP test. We discuss the various sources of statistical and systematic noise that affect the constraining power of this method. In particular, we find that the measured ellipticity of stacked voids scales more weakly with cosmology than the standard AP prediction, leading to significantly weaker constraints. We discuss how AP constraints will improve in future surveys with larger volumes and densities.

We present a cosmic void catalog using the large-scale structure galaxy catalog from the Baryon Oscillation Spectroscopic Survey (BOSS). This galaxy catalog is part of the Sloan Digital Sky Survey (SDSS) Data Release 12 and is the final catalog of SDSS-III. We take into account the survey boundaries, masks, and angular and radial selection functions, and apply the ZOBOV void finding algorithm to the Galaxy catalog. We identify a total of 10,643 voids. After making quality cuts to ensure that the voids represent real underdense regions, we obtain 1,228 voids with effective radii spanning the range 20-100 -h Mpc 1 and with central densities that are, on average, 30% of the mean sample density. We release versions of the catalogs both with and without quality cuts. We discuss the basic statistics of voids, such as their size and redshift distributions, and measure the radial density profile of the voids via a stacking technique. In addition, we construct mock void catalogs from 1000 mock galaxy catalogs, and find that the properties of BOSS voids are in good agreement with those in the mock catalogs. We compare the stellar mass distribution of galaxies living inside and outside of the voids, and find no large difference. These BOSS and mock void catalogs are useful for a number of cosmological and galaxy environment studies.

Any multivariate distribution can be uniquely decomposed into marginal (1-point) distributions, and a function called the copula, which contains all of the information on correlations between the distributions. The copula provides an important new methodology for analyzing the density field in large-scale structure. We derive the empirical 2-point copula for the evolved dark matter density field. We find that this empirical copula is well-approximated by a Gaussian copula. We consider the possibility that the full n-point copula is also Gaussian and describe some of the consequences of this hypothesis. Future directions for investigation are discussed.

BackgroundClinical decision support systems are used to help predict patient stability and mortality in the Intensive Care Unit (ICU). Accurate patient information can assist clinicians with patient management and in allocating finite resources. However, systems currently in common use have limited predictive value in the clinical setting. The increasing availability of Electronic Health Records (EHR) provides an opportunity to use medical information for more accurate patient stability and mortality prediction in the ICU.ObjectiveDevelop and evaluate an algorithm which more accurately predicts patient mortality in the ICU, using the correlations between widely available clinical variables from the EHR.MethodsWe have developed an algorithm, AutoTriage, which uses eight common clinical variables from the EHR to assign patient mortality risk scores. Each clinical variable produces a subscore, and combinations of two or three discretized clinical variables also produce subscores. A combination of weighted subscores produces the overall score. We validated the performance of this algorithm in a retrospective study on the MIMIC III medical ICU dataset.ResultsAutoTriage 12 h mortality prediction yields an Area Under Receiver Operating Characteristic value of 0.88 (95% confidence interval 0.86 to 0.88). At a sensitivity of 80%, AutoTriage maintains a specificity of 81% with a diagnostic odds ratio of 16.26.ConclusionsThrough the multidimensional analysis of the correlations between eight common clinical variables, AutoTriage provides an improvement in the specificity and sensitivity of patient mortality prediction over existing prediction methods.