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

ABSTRACT:

Background

Increased data availability has prompted the creation of clinical decision support systems. These systems utilise clinical information to enhance health care provision, both to predict the likelihood of specific clinical outcomes or evaluate the risk of further complications. However, their adoption remains low due to concerns regarding the quality of recommendations, and a lack of clarity on how results are best obtained and presented.

Methods

We used autoencoders capable of reducing the dimensionality of complex datasets in order to produce a 2D representation denoted as latent space to support understanding of complex clinical data. In this output, meaningful representations of individual patient profiles are spatially mapped in an unsupervised manner according

SUBMITTER: Hernandez B 

PROVIDER: S-EPMC9992802 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature