70 research outputs found
Time series adversarial attacks: an investigation of smooth perturbations and defense approaches
Open Access funding enabled and organized by CAUL and its
Member Institutions. This work was funded by ArtIC project “Artificial
Intelligence for Care” (Grant ANR-20-THIA-0006-01) and co-funded
by Région Grand Est, Inria Nancy - Grand Est, IHU of Strasbourg,
University of Strasbourg and the University of Haute-AlsaceAdversarial attacks represent a threat to every deep neural network. They are particularly effective if they can perturb a given
model while remaining undetectable. They have been initially introduced for image classifiers, and are well studied for this
task. For time series, few attacks have yet been proposed. Most that have are adaptations of attacks previously proposed
for image classifiers. Although these attacks are effective, they generate perturbations containing clearly discernible patterns
such as sawtooth and spikes. Adversarial patterns are not perceptible on images, but the attacks proposed to date are readily
perceptible in the case of time series. In order to generate stealthier adversarial attacks for time series, we propose a new
attack that produces smoother perturbations. We introduced a function to measure the smoothness for time series. Using it,
we find that smooth perturbations are harder to detect both visually, by the naked eye and by deep learning models. We also
show two ways of protection against adversarial attacks: the first one by detecting the attacks using a deep model; the second
one by using adversarial training to improve the robustness of a model against a specific attack, thus making it less vulnerable.Open Access funding enabled and organized by CAUL and its Member InstitutionsArtIC project “Artificial Intelligence for Care” (Grant ANR-20-THIA-0006-01)Co-funded by Région Grand Est, Inria Nancy - Grand Est, IHU of Strasbourg, University of Strasbourg and the University of Haute-Alsac
Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS
Neural networks are important standard machine learning procedures for classification and regression. We describe the R package RSNNS that provides a convenient interface to the popular Stuttgart Neural Network Simulator SNNS. The main features are (a) encapsulation of the relevant SNNS parts in a C++ class, for sequential and parallel usage of different networks, (b) accessibility of all of the SNNS algorithmic functionality from R using a low-level interface, and (c) a high-level interface for convenient, R-style usage of many standard neural network procedures. The package also includes functions for visualization and analysis of the models and the training procedures, as well as functions for data input/output from/to the original SNNS file formats.This work was supported in part by the Spanish Ministry of Science and Innovation (MICINN) under Project TIN-2009-14575. C. Bergmeir holds a scholarship from the Spanish Ministry of Education (MEC) of the \Programa de Formación del Profesorado Universitario (FPU)"
Paying attention to cardiac surgical risk: An interpretable machine learning approach using an uncertainty-aware attentive neural network
Machine learning (ML) is increasingly applied to predict adverse postoperative outcomes in
cardiac surgery. Commonly used ML models fail to translate to clinical practice due to
absent model explainability, limited uncertainty quantification, and no flexibility to missing
data. We aimed to develop and benchmark a novel ML approach, the uncertainty-aware
attention network (UAN), to overcome these common limitations. Two Bayesian uncertainty
quantification methods were tested, generalized variational inference (GVI) or a posterior
network (PN). The UAN models were compared with an ensemble of XGBoost models and
a Bayesian logistic regression model (LR) with imputation. The derivation datasets consisted
of 153,932 surgery events from the Australian and New Zealand Society of Cardiac
and Thoracic Surgeons (ANZSCTS) Cardiac Surgery Database. An external validation consisted
of 7343 surgery events which were extracted from the Medical Information Mart for
Intensive Care (MIMIC) III critical care dataset. The highest performing model on the external
validation dataset was a UAN-GVI with an area under the receiver operating characteristic
curve (AUC) of 0.78 (0.01). Model performance improved on high confidence samples
with an AUC of 0.81 (0.01). Confidence calibration for aleatoric uncertainty was excellent for
all models. Calibration for epistemic uncertainty was more variable, with an ensemble of
XGBoost models performing the best with an AUC of 0.84 (0.08). Epistemic uncertainty was
improved using the PN approach, compared to GVI. UAN is able to use an interpretable and
flexible deep learning approach to provide estimates of model uncertainty alongside stateof-
the-art predictions. The model has been made freely available as an easy-to-use web
application demonstrating that by designing uncertainty-aware models with innately explainable
predictions deep learning may become more suitable for routine clinical use.The ANZSCTS Cardiac Surgery Database
Program is funded by the Department of Health
(Victoria), the Clinical Excellence Commission
(NSW)Queensland Health (QLD)ANZSCTS Database Research
activities are supported through a National Health
and Medical Research Council Principal Research
Fellowship (APP 1136372)Program Grant
(APP 1092642
Tree-based survival analysis improves mortality prediction in cardiac surgery
Objectives: Machine learning (ML) classification tools are known to accurately
predict many cardiac surgical outcomes. A novel approach, ML-based survival
analysis, remains unstudied for predicting mortality after cardiac surgery. We
aimed to benchmark performance, as measured by the concordance index
(C-index), of tree-based survival models against Cox proportional hazards (CPH)
modeling and explore risk factors using the best-performing model.
Methods: 144,536 patients with 147,301 surgery events from the Australian and New
Zealand Society of Cardiac and Thoracic Surgeons (ANZSCTS) national database were
used to train and validate models. Univariate analysis was performed using Student’s
T-test for continuous variables, Chi-squared test for categorical variables, and
stratified Kaplan-Meier estimation of the survival function. Three ML models were
tested, a decision tree (DT), random forest (RF), and gradient boosting machine
(GBM). Hyperparameter tuning was performed using a Bayesian search strategy.
Performance was assessed using 2-fold cross-validation repeated 5 times.
Results: The highest performing model was the GBM with a C-index of 0.803
(0.002), followed by RF with 0.791 (0.003), DT with 0.729 (0.014), and finally CPH
with 0.596 (0.042). The 5 most predictive features were age, type of procedure,
length of hospital stay, drain output in the first 4 h (ml), and inotrope use greater
than 4 h postoperatively.
Conclusion: Tree-based learning for survival analysis is a non-parametric and
performant alternative to CPH modeling. GBMs offer interpretable modeling of
non-linear relationships, promising to expose the most relevant risk factors and
uncover new questions to guide future research.The ANZSCTS National Cardiac Surgery Database Program is
funded by the Department of Health (Victoria)the Clinical
Excellence Commission (NSW)Queensland Health (QLD)Cardiac surgical units participating in
the registry. ANZSCTS Database Research activities are
supported through a National Health and Medical Research
Council Principal Research Fellowship (APP 1136372)Program Grant (APP 1092642
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