Improving End-of-Life Care for COPD Patients: Design and Development of an Intelligent Clinical Decision Support System to Predict One-Year Mortality After Acute Exacerbations

Abstract

Chronic obstructive pulmonary disease (COPD) is a complex and heterogeneous disease, which presents a significant challenge in identifying patients at high risk of short- and medium-term mortality. Such complexity poses challenges to clinical decision-making and the effective planning of end-of-life care in these patients. This study proposes the development of a novel intelligent clinical decision support system, designed to predict 1-year mortality in COPD patients following an acute exacerbation. The system is constructed upon a database of over 500 patients, comprising demographic, clinical, and social variables. First, a feature selection process is conducted to identify the variables that possess the greatest predictive power. Based on these, the data for each patient are encapsulated in a pseudosymbol construct that represents and consolidates them. The construction of the pseudosymbol comprises two distinct steps: (1) transforming the variables into a sound composition and (2) generating the corresponding spectrogram, which constitutes a visual representation (i.e., an image). The system employs a convolutional neural network, SqueezeNet, as the inference engine to calculate the 1-year mortality risk based on the images. Ten percent of the data was reserved for testing the system, achieving an area under the ROC curve (AUC) close to 0.85, indicating a high predictive power. Despite this promising initial result, further clinical validations in real-world settings will be necessary to confirm the system’s applicability and usefulness.