Learning meaningful latent space representations for patient risk ...

This study demonstrates that when adequately configured, autoencoders can produce two-dimensional representations of a complex dataset that ...

Each point represents a patient and the shaded areas represent the density distribution; that is, the concentration of patients for which the ...

Learning meaningful latent space representations for patient risk ...

Each point represents a patient and the shaded areas represent the density distribution; that is, the concentration of patients for which the ...

Learning meaningful latent space representations for patient risk ...

Methods We used autoencoders capable of reducing the dimensionality of complex datasets in order to produce a 2D representation denoted as ...

Learning meaningful latent space representations for patient risk ...

... risk stratification: Model development and validation for dengue and other acute febrile illness. Learning meaningful latent space representations for patient ...

Learning meaningful latent space representations for patient risk ...

Learning meaningful latent space representations for patient risk stratification: model development and validation for dengue and other acute febrile illnes.

Learning meaningful latent space representations for patient risk ...

Methods: We used autoencoders capable of reducing the dimensionality of complex datasets in order to produce a 2D representation denoted as latent space to ...

Learning meaningful latent space representations for patient risk ...

Learning meaningful latent space representations for patient risk stratification: Model development and validation for dengue and other acute febrile illness.

Learning Latent Space Representations to Predict Patient Outcomes

(2) We provide a comprehensive evaluation of risk factors identified by our neural network models. Our results show that the risk factors ...

Learning meaningful latent space representations for patient risk ...

Learning meaningful latent space representations for patient risk stratification: Model development and validation for dengue and other acute febrile ...

Learning Latent Space Representations to Predict Patient Outcomes

Learning Latent Space Representations to Predict Patient Outcomes: Model Development and Validation · Subendhu Rongali, A. Rose, +4 authors. Hong Yu · Published ...

(PDF) Learning Latent Space Representations to Predict Patient ...

Our CLOUT models use a correlational neural network model to identify a latent space representation between different types of discrete clinical features during ...

Learning and visualizing chronic latent representations using ...

We explore in this work how the combination of LRs with a visualization method can be used to map the patient data in a two-dimensional space, ...

Predicting chemical ecotoxicity by learning latent space chemical ...

Instead of aiming to preserve distances or local structures, it learns latent space embeddings. These embeddings contain representations of data that are more ...

Learning Latent Space Representations to Predict Patient Outcomes

Our CLOUT models use a correlational neural network model to identify a latent space representation between different types of discrete clinical ...

Deep Representation Learning of Patient Data from Electronic ...

4 into meaningful features. The patient representations built from many EHR data modalities (including clinical narratives, lab tests, treatments, etc.) should ...

Learning Latent Space Representations to Predict Patient Outcomes

Using physicians' input as the gold standard, we compared the risk factors identified by both CLOUT and logistic regression models. Results: ...

Attri-VAE: Attribute-based interpretable representations of medical ...

The resulting latent space allowed the generation of realistic synthetic data in the trajectory between two distinct input samples or along a specific attribute ...

Deep Patient: An Unsupervised Representation to Predict the Future ...

Here we show that unsupervised deep feature learning applied to pre-process patient-level aggregated EHR data results in representations that ...

Explaining Latent Representations with a Corpus of Examples

... space. Our purpose is to gain ... 3.3 Use case: clinical risk model across countries ... understand latent representations involved in unsupervised learning.