Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease

Asthma and chronic obstructive pulmonary disease (COPD) are different disease entities. They are both clinical diagnoses, with diagnostic tools to discriminate between one another.There is a need to re-evaluate the concept of asthma and chronic obstructive pulmonary disease (COPD) as separate conditions, and to consider situations when they may coexist, or when one condition may evolve into the other. A prospective study included 70 patients with chronic airway diseases who were classified into three groups (COPD, asthma and ACO). They were selected from Department of Pulmonary Medicine, Kurnool Medical College outpatient clinic during the period from January 2019 to December 2019, where patients with COPD and ACO were diagnosed according to GOLD guidelines and patients with asthma were diagnosed according to GINA guidelines. Patients enrolled in the study were subjected to full history taking, clinical examination, full laboratory examination, plain chest radiography, spirometry before bronchodilator and after bronchodilator administration (reversibility test) and sputum analysis for counting eosinophils cells. This study was conducted on 70 patients with chronic airway diseases (COPD, asthma and asthma COPD overlap) were selected. It included 47(67.1%) males and 23(32.8%) females. In our study, 30 (42.8%) patients as having COPD, 19(27.1%) patients were diagnosed as having asthma and 21(30%) patients were diagnosed as having ACO. Regarding the age difference between groups, it was found that patients who were diagnosed as having ACO were older than asthmatic patients with mean age of 49.43±5.83 and 47.23±6.73years, respectively. The men age of patients with COPD was 57.32±6.74 which was older than both ACO and asthmatic patients. ACO represents a large percentage among patients with obstructive airway diseases. It shares some features of asthma such as atopy and positive sputum eosinophilia, and some features of COPD like old age of presentation and positive smoking history.

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Conventional and Nanotechnology Based Approaches to Combat Chronic Obstructive Pulmonary Disease: Implications for Chronic Airway Diseases

International Journal of Nanomedicine ◽

10.2147/ijn.s242516 ◽

2020 ◽

Vol Volume 15 ◽

pp. 3803-3826 ◽

Cited By ~ 1

Author(s):

Mehak Passi ◽

Sadia Shahid ◽

Sankarakuttalam Chockalingam ◽

Isaac Kirubakaran Sundar ◽

Gopinath Packirisamy

Keyword(s):

Chronic Obstructive Pulmonary Disease ◽

Pulmonary Disease ◽

Chronic Obstructive ◽

Obstructive Pulmonary Disease ◽

Airway Diseases ◽

Chronic Airway

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A machine learning approach to triaging patients with chronic obstructive pulmonary disease

PLoS ONE ◽

10.1371/journal.pone.0188532 ◽

2017 ◽

Vol 12 (11) ◽

pp. e0188532 ◽

Cited By ~ 20

Author(s):

Sumanth Swaminathan ◽

Klajdi Qirko ◽

Ted Smith ◽

Ethan Corcoran ◽

Nicholas G. Wysham ◽

...

Keyword(s):

Machine Learning ◽

Chronic Obstructive Pulmonary Disease ◽

Pulmonary Disease ◽

Chronic Obstructive ◽

Learning Approach ◽

Obstructive Pulmonary Disease ◽

Machine Learning Approach

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Symptoms Analysis Based Chronic Obstructive Pulmonary Disease Prediction In Bangladesh Using Machine Learning Approach

10.1109/icccnt51525.2021.9580078 ◽

2021 ◽

Author(s):

Mridul Das Joshe ◽

Nazmul Hassan Emon ◽

Mirajul Islam ◽

Nushrat Jahan Ria ◽

Abu Kaisar Mohammad Masum ◽

...

Keyword(s):

Machine Learning ◽

Chronic Obstructive Pulmonary Disease ◽

Pulmonary Disease ◽

Chronic Obstructive ◽

Learning Approach ◽

Disease Prediction ◽

Obstructive Pulmonary Disease ◽

Machine Learning Approach

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Prediction of chronic obstructive pulmonary disease stages using machine learning algorithms

International Journal of Decision Support System Technology ◽

10.4018/ijdsst.286693 ◽

2022 ◽

Vol 14 (1) ◽

pp. 0-0

Keyword(s):

Machine Learning ◽

Chronic Obstructive Pulmonary Disease ◽

Pulmonary Disease ◽

Predictive Value ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Chronic Obstructive ◽

Optimal Prediction ◽

Obstructive Pulmonary Disease ◽

Disease Stages

Identifying chronic obstructive pulmonary disease (COPD) severity stages is of great importance to control the related mortality rates and reduce the associated costs. This study aims to build prediction models for COPD stages and, to compare the relative performance of five machine learning algorithms to determine the optimal prediction algorithm. This research is based on data collected from a private hospital in Egypt for the two calendar years 2018 and 2019. Five machine learning algorithms were used for the comparison. The F1 score, specificity, sensitivity, accuracy, positive predictive value and negative predictive value were the performance measures used for algorithms comparison. Analysis included 211 patients’ records. Our results show that the best performing algorithm in most of the disease stages is the PNN with the optimal prediction accuracy and hence it can be considered as a powerful prediction tool used by decision makers in predicting severity stages of COPD.

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Diagnosing asthma and chronic obstructive pulmonary disease with machine learning

Health Informatics Journal ◽

10.1177/1460458217723169 ◽

2017 ◽

Vol 25 (3) ◽

pp. 811-827 ◽

Cited By ~ 15

Author(s):

Dimitris Spathis ◽

Panayiotis Vlamos

Keyword(s):

Machine Learning ◽

Chronic Obstructive Pulmonary Disease ◽

Random Forest ◽

Pulmonary Disease ◽

Clinical Decision Support Systems ◽

Clinical Decision ◽

Forced Expiratory Volume ◽

Random Forest Classifier ◽

Chronic Obstructive ◽

Obstructive Pulmonary Disease

This study examines the clinical decision support systems in healthcare, in particular about the prevention, diagnosis and treatment of respiratory diseases, such as Asthma and chronic obstructive pulmonary disease. The empirical pulmonology study of a representative sample (n = 132) attempts to identify the major factors that contribute to the diagnosis of these diseases. Machine learning results show that in chronic obstructive pulmonary disease’s case, Random Forest classifier outperforms other techniques with 97.7 per cent precision, while the most prominent attributes for diagnosis are smoking, forced expiratory volume 1, age and forced vital capacity. In asthma’s case, the best precision, 80.3 per cent, is achieved again with the Random Forest classifier, while the most prominent attribute is MEF2575.

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