Natural Language Processing Methods to Identify Oncology Patients at High Risk for Acute Care with Clinical Notes

arXiv (Cornell University)

Abstract

Clinical notes are an essential component of a health record. This paper evaluates how natural language processing (NLP) can be used to identify the risk of acute care use (ACU) in oncology patients, once chemotherapy starts. Risk prediction using structured health data (SHD) is now standard, but predictions using free-text formats are complex. This paper explores the use of free-text notes for the prediction of ACU instead of SHD. Deep Learning models were compared to manually engineered language features. Results show that SHD models minimally outperform NLP models; an l1-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.

Authors 3

1. Claudio Fanconi

   ETH Zurich · Stanford University

   Affiliation as printed

   ETH Zürich , Zürich , Switzerland

   Stanford University , Stanford , California , United States

2. Marieke Meija van Buchem Aachen

   Stanford University · Leiden University Medical Center

   Affiliation as printed

   Leiden University Medical Center , Leiden , The Netherlands

   Stanford University , Stanford , California , United States

3. Tina Hernandez‐Boussard

   Stanford University

   Affiliation as printed

   Stanford University , Stanford , California , United States