Natural Language Processing Methods to Identify Oncology Patients at High Risk for Acute Care with Clinical Notes.
Year: 2023
Type: article
Abstract: -penalised logistic regression with SHD achieved a C-statistic of 0.748 (95%-CI: 0.735, 0.762), while the same model with language features achieved 0.730 (95%-CI: 0.717, 0.745) and a transformer-based model achieved 0.702 (95%-CI: 0.688, 0.717). This paper shows how language models can be used in clinical applications and underlines how risk bias is different for diverse patient groups, even using only free-text data.
Source: PubMed
Institutions: ETH Zurich, Stanford University, Leiden University Medical Center
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4381716579