JMIR Publications

ABSTRACT

Background:

Chronic obstructive pulmonary disease (COPD) is a major cause of death and places a heavy burden on healthcare. To optimize the allocation of precious preventive care management resources and improve the outcomes for high-risk patients with COPD, we recently built the most accurate model to date to predict severe COPD exacerbations, which need inpatient stays or emergency department visits, in the following 12 months. Our model is a machine learning model. As is the case with most machine learning models, our model does not explain its predictions, forming a barrier for clinical use. Previously, we designed a method to automatically give rule-type explanations for machine learning predictions and suggest tailored interventions with no loss of model performance. This method has been tested on asthma outcome prediction, but not on COPD outcome prediction before.

Objective:

To assess the generalizability of our automatic explanation method for predicting severe COPD exacerbations.

Methods:

The patient cohort included all patients with COPD who ever visited the University of Washington Medicine facilities during 2011-2019. In a secondary analysis on 43,576 data instances, we used our formerly developed automatic explanation method to automatically explain our model’s predictions and suggest tailored interventions.

Results:

Our method explained the predictions for 97.1% (100/103) of the patients with COPD whom our model correctly predicted to have severe COPD exacerbations in the following 12 months, and the predictions on 73.6% (134/182) of the patients with COPD who had ≥1 severe COPD exacerbation in the following 12 months.

Conclusions:

Our automatic explanation method worked well for predicting severe COPD exacerbations. After further improving our method, we hope we can use it to facilitate future clinical use of our model.