Prediction of COPD Using Machine Learning, Clinical Summary Notes, and Vital Signs

TL;DR

This paper develops machine learning models utilizing clinical notes and vital signs to predict COPD exacerbations, aiming to enable timely interventions and improve patient outcomes.

Contribution

It introduces two novel AI-based models that incorporate NLP and physiological data for accurate COPD exacerbation prediction.

Findings

Achieved ROC AUC of 0.82 for predicting COPD exacerbations

Utilized large ICU datasets with physiologic signals and clinical notes

Demonstrated potential for real-time monitoring and early intervention

Abstract

Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease that causes obstructed airflow from the lungs. In the United States, more than 15.7 million Americans have been diagnosed with COPD, with 96% of individuals living with at least one other chronic health condition. It is the 4th leading cause of death in the country. Over 2.2 million patients are admitted to hospitals annually due to COPD exacerbations. Monitoring and predicting patient exacerbations on-time could save their life. This paper presents two different predictive models to predict COPD exacerbation using AI and natural language processing (NLP) approaches. These models use respiration summary notes, symptoms, and vital signs. To train and test these models, data records containing physiologic signals and vital signs time series were used. These records were captured from patient monitors and comprehensive clinical data obtained from hospital medical information systems for tens of thousands of Intensive Care Unit (ICU) patients. We achieved an area under the Receiver operating characteristic (ROC) curve of 0.82 in detection and prediction of COPD exacerbation.