Finding the best trade-off between performance and interpretability in predicting hospital length of stay using structured and unstructured data.

ABSTRACT:

Objective

This study aims to develop high-performing Machine Learning and Deep Learning models in predicting hospital length of stay (LOS) while enhancing interpretability. We compare performance and interpretability of models trained only on structured tabular data with models trained only on unstructured clinical text data, and on mixed data.

Methods

The structured data was used to train fourteen classical Machine Learning models including advanced ensemble trees, neural networks and k-nearest neighbors. The unstructured data was used to fine-tune a pre-trained Bio Clinical BERT Transformer Deep Learning model. The structured and unstructured data were then merged into a tabular dataset after vectorization of the clinical text and a dimensional reduction through Latent Dir

SUBMITTER: Jaotombo F 

PROVIDER: S-EPMC10688642 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature