Recent literature in self-supervised has demonstrated significant progress in closing the gap between supervised and unsupervised methods in the image and text domains.
Topic Modeling refers to the problem of discovering the main topics that have occurred in corpora of textual data, with solutions finding crucial applications in numerous fields.
We demonstrate the results of our algorithm on publicly-available datasets that contain all different hemorrhage types in various sizes and intensities, and our results are compared to earlier unsupervised and supervised algorithms.
We provide a multi-faceted analysis of critical properties exhibited by these conversations on social media regarding the novel coronavirus pandemic.
Based on our experiments we show knowledge transfer using our model outperforms common methods in a real time transfer scenario.
While activity recognition from inertial sensors holds potential for mobile health, differences in sensing platforms and user movement patterns cause performance degradation.
We introduce HTAD, a novel model for diagnosis prediction using Electronic Health Records (EHR) represented as Heterogeneous Information Networks.
GMLP is based on the idea of learning expressive feature combinations (groups) and exploiting them to reduce the network complexity by defining local group-wise operations.
The chosen features should increase the prediction accuracy for a low cost, but determining which features will do that is challenging.
In this work, we present a two-step unsupervised representation learning scheme to summarize the multi-modal clinical time series consisting of signals and medical codes into a patient status vector.
In many real-world scenarios where data is high dimensional, test time acquisition of features is a non-trivial task due to costs associated with feature acquisition and evaluating feature value.
The data contained in these records are irregular and contain multiple modalities such as notes, and medical codes.
In order to make imputations, we train a simple and effective generator network to generate imputations that a discriminator network is tasked to distinguish.
Furthermore, based on the suggested dataset, we provide a comparison of recent and state-of-the-art approaches to cost-sensitive feature acquisition and learning.
The emergence of continuous health monitoring and the availability of an enormous amount of time series data has provided a great opportunity for the advancement of personal health tracking.
The suggested method acquires features incrementally based on a context-aware feature-value function.
In real-world scenarios, different features have different acquisition costs at test-time which necessitates cost-aware methods to optimize the cost and performance trade-off.
To the best of our knowledge, this is the first study to use Heterogeneous Information Network for modeling clinical data and disease diagnosis.
Electrocardiogram (ECG) can be reliably used as a measure to monitor the functionality of the cardiovascular system.