Artificial Intelligence in COPD
Artificial Intelligence in COPD: Definition and Evolution
The term artificial intelligence (AI) refers to a set of algorithms (rules) that enable computers to learn, analyze data and make decisions based on that knowledge.
These algorithms can perform tasks that would typically require human intelligence, such as recognizing patterns, understanding natural language, problem-solving and decision-making.
This is not a new term (research into AI started in the fifties of last century) but due to exponentially growing computer power and cloud storage has now literally explode.2 Initial AI prototypes were based on so-called “machine learning”, where algorithms allow computers to learn from examples without being explicitly programmed.
A more recent type of AI is so-called “deep-learning”, which is a subset of machine learning that uses “artificial neural networks” as models and does not need feature engineering.
AI in COPD: Applications and Potential
Chronic obstructive pulmonary disease (COPD) is a major public health problem because its high prevalence (about 10% of adults in the general population suffer it, albeit most of them do not know that and are therefore not treated), raising incidence (in relation to the ageing of the population), associated morbi-morbidity (currently COPD is the third global cause of death) and associated personal, familiar and societal impact.
There are many potential aspects of COPD where AI can make a significant impact, including addressing the unacceptable rate of underdiagnosis, facilitating the interpretation of lung function tests, providing clear and easy therapeutic guide to practicing clinicians (see AvoMD in the current webpage of the Global Initiative for Obstructive Lung Disease (www.goldcopd.org)), and/or supporting and helping patients directly, among others.
Machine Learning for Patient Clustering
In this issue, Casal-Guisande et al. explores the potential role of machine learning to cluster patients hospitalized because of an exacerbation of COPD (n=524) in the Pulmonary Department of two third-level hospital in northwest Spain based on their social and clinical characteristics, in order to relate them to relevant clinical outcomes, such as early hospital readmissions and mortality.
Based on a very large number of demographic, clinical and socio-economic variables (see Table 1 in the original article14), this AI driven analysis identified four clusters of patients with different clinical and social characteristics associated with different relevant clinical outcomes, including length of stay, early hospital readmissions and mortality (Table 1).
Results
Not surprisingly Clusters A and C formed by younger individuals were associated to better outcomes than Clusters B and, particularly, D who were formed by older patients with cardiovascular (Cluster B) or other comorbidities and high dependency level (Cluster D).
Authors then used a supervised (hence, not unbiased) machine learning model (Random Forest) to develop an Intelligent Clinical Decision Support System (ICDSS) capable of assigning patients to these four clusters using only five key variables (age, body mass index, number of hospitalizations in the previous year, and number of basic and instrumental activities with dependency) from the very large list of variables analyzed originally.
The ICDSS developed showed a very high sensitivity and specificity (all areas under the receiving operating curves (AUC) were higher than 0.90).
Conclusions:
Collectively, these results show that AI (machine learning) can identify clusters of COPD patients hospitalized because of an exacerbation of their disease that relate to different resource utilization and prognosis, thus potentially helping to guide their therapeutic management.
This study, therefore, nicely illustrates the potential of AI in COPD, likely applicable too other chronic diseases, such heart failure or diabetes, with the final goal of providing better, personalized care to patients and to optimize the use of healthcare resources.
Authors
Alvar Agusti, Marc Vila
Read more details at
Fecha de publicación
Available online 20 January 2025
Categorías asociadas al artículo
- Artificial Intelligence in Respiratory Medicine-Artificial Intelligence in Respiratory Medicine, Comorbidity-Comorbilidad, COPD-EPOC, Ensayos-Trials, Estudios-Studies, Exacerbations-Exacerbaciones, GOLD, GOLD Report, Humanización-Gestión de pacientes, Investigación, Lung function-Función pulmonar, Spirometry-Espirometría, Tratamientos-Treatments
Noticias relacionadas
Novedades GOLD 2025 – 19 Noviembre 2024. 17:30-19h
NOVEDADES GOLD 2025, Reunión mixta el próximo 19 de Noviembre 2024, de 17:30 a 19h. Asistencia gratuita, pero precisa registro on-line. Link de registro aquí.
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