189 research outputs found
Simultaneous Modeling of Disease Screening and Severity Prediction: A Multi-task and Sparse Regularization Approach
Disease prediction is one of the central problems in biostatistical research.
Some biomarkers are not only helpful in diagnosing and screening diseases but
also associated with the severity of the diseases. It should be helpful to
construct a prediction model that can estimate severity at the diagnosis or
screening stage from perspectives such as treatment prioritization. We focus on
solving the combined tasks of screening and severity prediction, considering a
combined response variable such as \{healthy, mild, intermediate, severe\}.
This type of response variable is ordinal, but since the two tasks do not
necessarily share the same statistical structure, the conventional cumulative
logit model (CLM) may not be suitable. To handle the composite ordinal
response, we propose the Multi-task Cumulative Logit Model (MtCLM) with
structural sparse regularization. This model is sufficiently flexible that can
fit the different structures of the two tasks and capture their shared
structure of them. In addition, MtCLM is valid as a stochastic model in the
entire predictor space, unlike another conventional and flexible model, the
non-parallel cumulative logit model (NPCLM). We conduct simulation experiments
and real data analysis to illustrate the prediction performance and
interpretability
Deep Learning for Echocardiography
Objectives: The aim of this study was to evaluate whether a deep convolutional neural network (DCNN) could detect regional wall motion abnormalities (RWMAs) and differentiate groups of coronary infarction territories from conventional 2-dimensional echocardiographic images compared with cardiologist/sonographer or resident readers.
Background: An effective intervention for reduction of misreading of RWMAs is needed. We hypothesized that a DCNN trained with echocardiographic images may provide improved detection of RWMAs in the clinical setting.
Methods: A total of 300 patients with history of myocardial infarction were enrolled. In this cohort, 100 each had infarctions of the left anterior descending branch (LAD), left circumflex branch (LCX), and right coronary artery (RCA). The age-matched 100 control patients with normal wall motion were selected from our database. Each case contained cardiac ultrasound images from short axis views at end-diastolic, mid-systolic and end-systolic phases. After 100 steps of training, diagnostic accuracies were calculated on the test set. We independently trained 10 versions of the same model, and performed ensemble predictions with them.
Results: For detection of the presence of wall motion abnormality, the area under the receiver-operating characteristic curve (AUC) by deep learning algorithm was similar to that by cardiologist/sonographer readers (0.99 vs. 0.98, p =0.15), and significantly higher than the AUC by resident readers (0.99 vs. 0.90, p =0.002). For detection of territories of wall motion abnormality, the AUC by the deep learning algorithm was similar to the AUC by cardiologist/sonographer readers (0.97 vs. 0.95, p =0.61) and significantly higher than the AUC by resident readers (0.97 vs. 0.83, p =0.003). In a validation group from an independent site (n=40), the AUC by the DL algorithm was 0.90.
Conclusions: Our results support the possibility of DCNN use for automated diagnosis of RWMAs in the field of echocardiography
Atomic-scale characterization of nitrogen-doped graphite: Effects of dopant nitrogen on the local electronic structure of the surrounding carbon atoms
We report the local atomic and electronic structure of a nitrogen-doped
graphite surface by scanning tunnelling microscopy, scanning tunnelling
spectroscopy, X-ray photoelectron spectroscopy, and first-principles
calculations. The nitrogen-doped graphite was prepared by nitrogen ion
bombardment followed by thermal annealing. Two types of nitrogen species were
identified at the atomic level: pyridinic-N (N bonded to two C nearest
neighbours) and graphitic-N (N bonded to three C nearest neighbours). Distinct
electronic states of localized {\pi} states were found to appear in the
occupied and unoccupied regions near the Fermi level at the carbon atoms around
pyridinic-N and graphitic-N species, respectively. The origin of these states
is discussed based on the experimental results and theoretical simulations.Comment: 6 Pages, with larger figure
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