An initial investigation of Charlson comorbidity index Regression based on Clinical Notes
Author: Henrique D. P. dos Santos, Ana Helena D. P. S. Ulbrich, Vinicius Woloszyn, and Renata Vieira
Abstract: The Charlson comorbidity index (CCI) is widely used to predict mortality for patients who may have many comorbid conditions. Such index is also used as an indicator of the patients' complexity inside a hospital. In this paper, we evaluate a variety of feature extraction and regression methods to predict the CCI from clinical notes. We used a tertiary hospital dataset with 48 thousand hospitalizations featuring the CCI annotated by physicians. In our experiments, Dense Neural Networks with Word Embeddings proved to be the best regression method, with a mean absolute error of 0.51.
Complete Reference: Henrique D. P. dos Santos, Ana Helena D. P. S. Ulbrich, Vinicius Woloszyn, and Renata Vieira. 2018. An initial investigation of the Charlson comorbidity index regression based on clinical notes. 31st International Symposium on Computer-Based Medical Systems, CBMS 2018, 6 pages.
This project belongs to NLP Group at PUCRS, Brazil