Work

Compositional and Hierarchical Semantic Learning Model for Hospital Readmission Prediction

Year: 2024

Type: conference-paper

Abstract: Clinical notes provide a wealth of patient information that is valuable for predicting clinical outcomes. In particular, predicting hospital 30-day readmission is important to improve healthcare outcomes and reduce cost. Previous works on outcome prediction using clinical notes overlook complex semantic compositions and syntactic structure when learning the note level embedding, which may fail to capture the note semantics and make accurate predictions.

Source: ACM International Conference on Information and Knowledge Management (CIKM)

DOI: 10.1145/3627673.3679814

Authors: Weiting Gao, Xiangyu Gao, Yi Chen

Institution: New Jersey Institute of Technology

Language: English

Topic: Machine Learning in Healthcare

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

OpenAlex ID: W4403582569

FWCI: 0.294

Cites: 30

Cited by: 1

Related to: 10