628 research outputs found
Addressing Problems in Evaluating Health-Relevant Programs through Systematic Planning and Evaluation
The authors argue that inconsistent terminology is often a hindrance in assessing health program implementation, effectiveness and efficiency. Attending closely to this, they propose a model scheme for conducting such evaluations
ManyDG: Many-domain Generalization for Healthcare Applications
The vast amount of health data has been continuously collected for each
patient, providing opportunities to support diverse healthcare predictive tasks
such as seizure detection and hospitalization prediction. Existing models are
mostly trained on other patients data and evaluated on new patients. Many of
them might suffer from poor generalizability. One key reason can be overfitting
due to the unique information related to patient identities and their data
collection environments, referred to as patient covariates in the paper. These
patient covariates usually do not contribute to predicting the targets but are
often difficult to remove. As a result, they can bias the model training
process and impede generalization. In healthcare applications, most existing
domain generalization methods assume a small number of domains. In this paper,
considering the diversity of patient covariates, we propose a new setting by
treating each patient as a separate domain (leading to many domains). We
develop a new domain generalization method ManyDG, that can scale to such
many-domain problems. Our method identifies the patient domain covariates by
mutual reconstruction and removes them via an orthogonal projection step.
Extensive experiments show that ManyDG can boost the generalization performance
on multiple real-world healthcare tasks (e.g., 3.7% Jaccard improvements on
MIMIC drug recommendation) and support realistic but challenging settings such
as insufficient data and continuous learning.Comment: The paper has been accepted by ICLR 2023, refer to
https://openreview.net/forum?id=lcSfirnflpW. We will release the data and
source codes here https://github.com/ycq091044/ManyD
Should a Sentinel Node Biopsy Be Performed in Patients with High-Risk Breast Cancer?
A negative sentinel lymph node (SLN) biopsy spares many breast cancer patients the complications associated with lymph node irradiation or additional surgery. However, patients at high risk for nodal involvement based on clinical characteristics may remain at unacceptably high risk of axillary disease even after a negative SLN biopsy result. A Bayesian nomogram was designed to combine the probability of axillary disease prior to nodal biopsy with customized test characteristics for an SLN biopsy and provides the probability of axillary disease despite a negative SLN biopsy. Users may individualize the sensitivity of an SLN biopsy based on factors known to modify the sensitivity of the procedure. This tool may be useful in identifying patients who should have expanded upfront exploration of the axilla or comprehensive axillary irradiation
Failure Investigation of an Intra-Manifold Explosion in a Horizontally-Mounted 870 lbf Reaction Control Thruster
In June 2010, an 870 lbf Space Shuttle Orbiter Reaction Control System Primary Thruster experienced an unintended shutdown during a test being performed at the NASA White Sands Test Facility. Subsequent removal and inspection of the thruster revealed permanent deformation and misalignment of the thruster valve mounting plate. Destructive evaluation determined that after three nominal firing sequences, the thruster had experienced an energetic event within the fuel (monomethylhydrazine) manifold at the start of the fourth firing sequence. The current understanding of the phenomenon of intra-manifold explosions in hypergolic bipropellant thrusters is documented in literature where it is colloquially referred to as a ZOT. The typical ZOT scenario involves operation of a thruster in a gravitational field with environmental pressures above the triple point pressure of the propellants. Post-firing, when the thruster valves are commanded closed, there remains a residual quantity of propellant in both the fuel and oxidizer (nitrogen tetroxide) injector manifolds known as the "dribble volume". In an ambient ground test configuration, these propellant volumes will drain from the injector manifolds but are impeded by the local atmospheric pressure. The evacuation of propellants from the thruster injector manifolds relies on the fluids vapor pressure to expel the liquid. The higher vapor pressure oxidizer will evacuate from the manifold before the lower vapor pressure fuel. The localized cooling resulting from the oxidizer boiling during manifold draining can result in fuel vapor migration and condensation in the oxidizer passage. The liquid fuel will then react with the oxidizer that enters the manifold during the next firing and may produce a localized high pressure reaction or explosion within the confines of the oxidizer injector manifold. The typical ZOT scenario was considered during this failure investigation, but was ultimately ruled out as a cause of the explosion. Converse to the typical ZOT failure mechanism, the failure of this particular thruster was determined to be the result of liquid oxidizer being present within the fuel manifold
Fast-ignition design transport studies: realistic electron source, integrated PIC-hydrodynamics, imposed magnetic fields
Transport modeling of idealized, cone-guided fast ignition targets indicates
the severe challenge posed by fast-electron source divergence. The hybrid
particle-in-cell [PIC] code Zuma is run in tandem with the
radiation-hydrodynamics code Hydra to model fast-electron propagation, fuel
heating, and thermonuclear burn. The fast electron source is based on a 3D
explicit-PIC laser-plasma simulation with the PSC code. This shows a quasi
two-temperature energy spectrum, and a divergent angle spectrum (average
velocity-space polar angle of 52 degrees). Transport simulations with the
PIC-based divergence do not ignite for > 1 MJ of fast-electron energy, for a
modest 70 micron standoff distance from fast-electron injection to the dense
fuel. However, artificially collimating the source gives an ignition energy of
132 kJ. To mitigate the divergence, we consider imposed axial magnetic fields.
Uniform fields ~50 MG are sufficient to recover the artificially collimated
ignition energy. Experiments at the Omega laser facility have generated fields
of this magnitude by imploding a capsule in seed fields of 50-100 kG. Such
imploded fields are however more compressed in the transport region than in the
laser absorption region. When fast electrons encounter increasing field
strength, magnetic mirroring can reflect a substantial fraction of them and
reduce coupling to the fuel. A hollow magnetic pipe, which peaks at a finite
radius, is presented as one field configuration which circumvents mirroring.Comment: 16 pages, 17 figures, submitted to Phys. Plasma
SCRIB: Set-classifier with Class-specific Risk Bounds for Blackbox Models
Despite deep learning (DL) success in classification problems, DL classifiers
do not provide a sound mechanism to decide when to refrain from predicting.
Recent works tried to control the overall prediction risk with classification
with rejection options. However, existing works overlook the different
significance of different classes. We introduce Set-classifier with
Class-specific RIsk Bounds (SCRIB) to tackle this problem, assigning multiple
labels to each example. Given the output of a black-box model on the validation
set, SCRIB constructs a set-classifier that controls the class-specific
prediction risks with a theoretical guarantee. The key idea is to reject when
the set classifier returns more than one label. We validated SCRIB on several
medical applications, including sleep staging on electroencephalogram (EEG)
data, X-ray COVID image classification, and atrial fibrillation detection based
on electrocardiogram (ECG) data. SCRIB obtained desirable class-specific risks,
which are 35\%-88\% closer to the target risks than baseline methods
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