812 research outputs found
Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks
Predicting the future health information of patients from the historical
Electronic Health Records (EHR) is a core research task in the development of
personalized healthcare. Patient EHR data consist of sequences of visits over
time, where each visit contains multiple medical codes, including diagnosis,
medication, and procedure codes. The most important challenges for this task
are to model the temporality and high dimensionality of sequential EHR data and
to interpret the prediction results. Existing work solves this problem by
employing recurrent neural networks (RNNs) to model EHR data and utilizing
simple attention mechanism to interpret the results. However, RNN-based
approaches suffer from the problem that the performance of RNNs drops when the
length of sequences is large, and the relationships between subsequent visits
are ignored by current RNN-based approaches. To address these issues, we
propose {\sf Dipole}, an end-to-end, simple and robust model for predicting
patients' future health information. Dipole employs bidirectional recurrent
neural networks to remember all the information of both the past visits and the
future visits, and it introduces three attention mechanisms to measure the
relationships of different visits for the prediction. With the attention
mechanisms, Dipole can interpret the prediction results effectively. Dipole
also allows us to interpret the learned medical code representations which are
confirmed positively by medical experts. Experimental results on two real world
EHR datasets show that the proposed Dipole can significantly improve the
prediction accuracy compared with the state-of-the-art diagnosis prediction
approaches and provide clinically meaningful interpretation
MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III
Robust machine learning relies on access to data that can be used with
standardized frameworks in important tasks and the ability to develop models
whose performance can be reasonably reproduced. In machine learning for
healthcare, the community faces reproducibility challenges due to a lack of
publicly accessible data and a lack of standardized data processing frameworks.
We present MIMIC-Extract, an open-source pipeline for transforming raw
electronic health record (EHR) data for critical care patients contained in the
publicly-available MIMIC-III database into dataframes that are directly usable
in common machine learning pipelines. MIMIC-Extract addresses three primary
challenges in making complex health records data accessible to the broader
machine learning community. First, it provides standardized data processing
functions, including unit conversion, outlier detection, and aggregating
semantically equivalent features, thus accounting for duplication and reducing
missingness. Second, it preserves the time series nature of clinical data and
can be easily integrated into clinically actionable prediction tasks in machine
learning for health. Finally, it is highly extensible so that other researchers
with related questions can easily use the same pipeline. We demonstrate the
utility of this pipeline by showcasing several benchmark tasks and baseline
results
Refinement algebra for probabilistic programs
We identify a refinement algebra for reasoning about probabilistic program transformations in a total-correctness setting. The algebra is equipped with operators that determine whether a program is enabled or terminates respectively. As well as developing the basic theory of the algebra we demonstrate how it may be used to explain key differences and similarities between standard (i.e. non-probabilistic) and probabilistic programs and verify important transformation theorems for probabilistic action systems.29 page(s
Border Terriers under primary veterinary care in England: demography and disorders
The Border Terrier is a working terrier type that is generally considered to be a relatively healthy and hardy breed. This study aimed to characterise the demography and common disorders of Border Terriers receiving veterinary care in England using de-identified electronic patient record data within the VetCompassâą Programme
Occupational correlates of smoking among urban transit operators: A prospective study
<p>Abstract</p> <p>Background</p> <p>Workers in blue-collar and service occupations smoke at higher rates than workers in white-collar and professional occupations. Occupational stress may explain some of the occupational class differences in smoking and quitting behavior. The purpose of this study is to investigate the contribution of occupational factors to smoking behavior over a ten year period among a multiethnic cohort of urban transit operators, while accounting for demographic factors and alcohol.</p> <p>Methods</p> <p>The sample consists of 654 San Francisco Municipal Railway (MUNI) transit operators who participated in two occupational health studies and biennial medical examinations during 1983â85 and 1993â95. Workers who had initiated, increased, or maintained their smoking over the ten year period were compared to workers who remained non-smokers. Occupational factors included self-rated frequency of job problems (e.g., difficulties with equipment, passengers, traffic), job burnout (i.e., the emotional exhaustion subscale of the Maslach Burnout Inventory), time needed to unwind after work, and years employed as a transit operator. A series of logistic regression models were developed to estimate the contribution of occupational factors to smoking behavior over time.</p> <p>Results</p> <p>Approximately 35% of the workers increased, initiated, or maintained their smoking over the ten-year period. Frequency of job problems was significantly associated with likelihood of smoking increase, initiation, or maintenance (OR = 1.30; 95% CI 1.09, 1.55). Black operators were significantly more likely to have smoked over the ten-year period compared to operators in other racial/ethnic groups.</p> <p>Conclusion</p> <p>Understanding the role of work-related stress vis-Ă -vis smoking behavior is of critical importance for crafting workplace smoking prevention and cessation interventions that are applicable to blue-collar work settings, and for developing policies that mitigate occupational stress.</p
An assessment of false discovery rates and statistical significance in label-free quantitative proteomics with combined filters
Essential versus accessory aspects of cell death: recommendations of the NCCD 2015
Cells exposed to extreme physicochemical or mechanical stimuli die in an uncontrollable manner, as a result of their immediate structural breakdown. Such an unavoidable variant of cellular demise is generally referred to as âaccidental cell deathâ (ACD). In most settings, however, cell death is initiated by a genetically encoded apparatus, correlating with the fact that its course can be altered by pharmacologic or genetic interventions. âRegulated cell deathâ (RCD) can occur as part of physiologic programs or can be activated once adaptive responses to perturbations of the extracellular or intracellular microenvironment fail. The biochemical phenomena that accompany RCD may be harnessed to classify it into a few subtypes, which often (but not always) exhibit stereotyped morphologic features. Nonetheless, efficiently inhibiting the processes that are commonly thought to cause RCD, such as the activation of executioner caspases in the course of apoptosis, does not exert true cytoprotective effects in the mammalian system, but simply alters the kinetics of cellular demise as it shifts its morphologic and biochemical correlates. Conversely, bona fide cytoprotection can be achieved by inhibiting the transduction of lethal signals in the early phases of the process, when adaptive responses are still operational. Thus, the mechanisms that truly execute RCD may be less understood, less inhibitable and perhaps more homogeneous than previously thought. Here, the Nomenclature Committee on Cell Death formulates a set of recommendations to help scientists and researchers to discriminate between essential and accessory aspects of cell death
Measurement of the Forward-Backward Asymmetry in the B -> K(*) mu+ mu- Decay and First Observation of the Bs -> phi mu+ mu- Decay
We reconstruct the rare decays , , and in a data sample
corresponding to collected in collisions at
by the CDF II detector at the Fermilab Tevatron
Collider. Using and decays we report the branching ratios. In addition, we report
the measurement of the differential branching ratio and the muon
forward-backward asymmetry in the and decay modes, and the
longitudinal polarization in the decay mode with respect to the squared
dimuon mass. These are consistent with the theoretical prediction from the
standard model, and most recent determinations from other experiments and of
comparable accuracy. We also report the first observation of the {\mathcal{B}}(B^0_s \to
\phi\mu^+\mu^-) = [1.44 \pm 0.33 \pm 0.46] \times 10^{-6}27 \pm 6B^0_s$ decay observed.Comment: 7 pages, 2 figures, 3 tables. Submitted to Phys. Rev. Let
Measurements of the properties of Lambda_c(2595), Lambda_c(2625), Sigma_c(2455), and Sigma_c(2520) baryons
We report measurements of the resonance properties of Lambda_c(2595)+ and
Lambda_c(2625)+ baryons in their decays to Lambda_c+ pi+ pi- as well as
Sigma_c(2455)++,0 and Sigma_c(2520)++,0 baryons in their decays to Lambda_c+
pi+/- final states. These measurements are performed using data corresponding
to 5.2/fb of integrated luminosity from ppbar collisions at sqrt(s) = 1.96 TeV,
collected with the CDF II detector at the Fermilab Tevatron. Exploiting the
largest available charmed baryon sample, we measure masses and decay widths
with uncertainties comparable to the world averages for Sigma_c states, and
significantly smaller uncertainties than the world averages for excited
Lambda_c+ states.Comment: added one reference and one table, changed order of figures, 17
pages, 15 figure
Search for a New Heavy Gauge Boson Wprime with Electron + missing ET Event Signature in ppbar collisions at sqrt(s)=1.96 TeV
We present a search for a new heavy charged vector boson decaying
to an electron-neutrino pair in collisions at a center-of-mass
energy of 1.96\unit{TeV}. The data were collected with the CDF II detector
and correspond to an integrated luminosity of 5.3\unit{fb}^{-1}. No
significant excess above the standard model expectation is observed and we set
upper limits on . Assuming standard
model couplings to fermions and the neutrino from the boson decay to
be light, we exclude a boson with mass less than
1.12\unit{TeV/}c^2 at the 95\unit{%} confidence level.Comment: 7 pages, 2 figures Submitted to PR
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