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Found 15 papers, 7 papers with code
Approaching adverse event detection utilizing transformers on clinical time-series
Patients being admitted to a hospital will most often be associated with a certain clinical development during their stay.
A clinically motivated self-supervised approach for content-based image retrieval of CT liver images
We address these limitations by (1) proposing a self-supervised learning framework that incorporates domain-knowledge into the training procedure and (2) providing the first representation learning explainability analysis in the context of CBIR of CT liver images.
The Kernelized Taylor Diagram
Our proposed kernelized Taylor diagram is capable of visualizing similarities between populations with minimal assumptions of the data distributions.
Mixing Up Contrastive Learning: Self-Supervised Representation Learning for Time Series
The lack of labeled data is a key challenge for learning useful representation from time series data.
RELAX: Representation Learning Explainability
Our approach can also model the uncertainty in its explanations, which is essential to produce trustworthy explanations.
On the Use of Time Series Kernel and Dimensionality Reduction to Identify the Acquisition of Antimicrobial Multidrug Resistance in the Intensive Care Unit
The acquisition of Antimicrobial Multidrug Resistance (AMR) in patients admitted to the Intensive Care Units (ICU) is a major global concern.
Uncertainty-Aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time Series
A measure of uncertainty in the relevance scores is computed by taking the standard deviation across the relevance scores produced by each model in the ensemble, which in turn is used to make the explanations more reliable.
A Kernel to Exploit Informative Missingness in Multivariate Time Series from EHRs
A large fraction of the electronic health records (EHRs) consists of clinical measurements collected over time, such as lab tests and vital signs, which provide important information about a patient's health status.
Time series cluster kernels to exploit informative missingness and incomplete label information
To overcome this limitation, we present a kernel capable of exploiting the potentially rich information in the missing values and patterns, as well as the information from the observed data.
Noisy multi-label semi-supervised dimensionality reduction
With the proposed Noisy multi-label semi-supervised dimensionality reduction (NMLSDR) method, the noisy multi-labels are denoised and unlabeled data are labeled simultaneously via a specially designed label propagation algorithm.
Learning representations for multivariate time series with missing data using Temporal Kernelized Autoencoders
In this work, we propose a novel autoencoder architecture based on recurrent neural networks to generate compressed representations of MTS.
An Unsupervised Multivariate Time Series Kernel Approach for Identifying Patients with Surgical Site Infection from Blood Samples
A large fraction of the electronic health records consists of clinical measurements collected over time, such as blood tests, which provide important information about the health status of a patient.
Classification of postoperative surgical site infections from blood measurements with missing data using recurrent neural networks
Clinical measurements that can be represented as time series constitute an important fraction of the electronic health records and are often both uncertain and incomplete.
Learning compressed representations of blood samples time series with missing data
Clinical measurements collected over time are naturally represented as multivariate time series (MTS), which often contain missing data.
Time Series Cluster Kernel for Learning Similarities between Multivariate Time Series with Missing Data
An ensemble learning approach is exploited to ensure robustness to parameters by combining the clustering results of many GMM to form the final kernel.
