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Cited by 116Chronic Obstructive Pulmonary Disease (COPD) ResearchMachine Learning in HealthcareArtificial Intelligence in Healthcare

Machine learning and deep learning predictive models for long-term prognosis in patients with chronic obstructive pulmonary disease: a systematic review and meta-analysis

Open access
Smith, Luke A, Oakden-Rayner, Lauren, Bird, Alix, Zeng, Minyan, To, Minh-Son, Mukherjee, Sutapa, Palmer, Lyle J·2023·The Lancet Digital Health
DOI 10.1016/s2589-7500(23)00177-2
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Abstract

Background Machine learning and deep learning models have been increasingly used to predict long-term disease progression in patients with chronic obstructive pulmonary disease (COPD). We aimed to summarise the performance of such prognostic models for COPD, compare their relative performances, and identify key research gaps. Methods We conducted a systematic review and meta-analysis to compare the performance of machine learning and deep learning prognostic models and identify pathways for future research. We searched PubMed, Embase, the Cochrane Library, ProQuest, Scopus, and Web of Science from database inception to April 6, 2023, for studies in English using machine learning or deep learning to predict patient outcomes at least 6 months after initial clinical presentation in those with COPD. We included studies comprising human adults aged 18–90 years and allowed for any input modalities. We reported area under the receiver operator characteristic curve (AUC) with 95% CI for predictions of mortality, exacerbation, and decline in forced expiratory volume in 1 s (FEV 1 ). We reported the degree of interstudy heterogeneity using Cochran's Q test (significant heterogeneity was defined as p≤0·10 or I 2 >50%). Reporting quality was assessed using the TRIPOD checklist and a risk-of-bias assessment was done using the PROBAST checklist. This study was registered with PROSPERO (CRD42022323052). Findings We identified 3620 studies in the initial search. 18 studies were eligible, and, of these, 12 used conventional machine learning and six used deep learning models. Seven models analysed exacerbation risk, with only six reporting AUC and 95% CI on internal validation datasets (pooled AUC 0·77 [95% CI 0·69–0·85]) and there was significant heterogeneity ( I 2 97%, p<0·0001). 11 models analysed mortality risk, with only six reporting AUC and 95% CI on internal validation datasets (pooled AUC 0·77 [95% CI 0·74–0·80]) with significant degrees of heterogeneity ( I 2 60%, p=0·027). Two studies assessed decline in lung function and were unable to be pooled. Machine learning and deep learning models did not show significant improvement over pre-existing disease severity scores in predicting exacerbations (p=0·24). Three studies directly compared machine learning models against pre-existing severity scores for predicting mortality and pooled performance did not differ (p=0·57). Of the five studies that performed external validation, performance was worse than or equal to regression models. Incorrect handling of missing data, not reporting model uncertainty, and use of datasets that were too small relative to the number of predictive features included provided the largest risks of bias. Interpretation There is limited evidence that conventional machine learning and deep learning prognostic models demonstrate superior performance to pre-existing disease severity scores. More rigorous adherence to reporting guidelines would reduce the risk of bias in future studies and aid study reproducibility. Funding None.

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  • DL models consistently outperform both regression-based and other ML models.

    “Our results indicated that DL models consistently outperform both regression-based and other ML models, aligning with findings from previous studies (71, 72).”

    From Machine learning-based risk predictive models for diabetic kidney disease in type 2 diabetes mellitus patients: a systematic review and meta-analysis (2025) · cites this paper as [71]

    The Model performance for deep learning section reports no significant difference in pooled mortality AUC between deep learning and other machine-learning models (p=0.44). The Discussion says evidence that these models outperform regression models is limited: externally validated performance was worse than or equal to regression models. These findings contradict the claim of consistent outperformance. (LLM, conf=0.90)

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Research works printed but not linked: 4

  1. Boueiz A, Xu Z, Chang Y, et al. Machine learning prediction of progression in forced expiratory volume in 1 second in the copdgene study. Chronic Obstr Pulm Dis 2022; 9: 349-65.
  2. Le, TT. Use of machine learning to predict COPD treatments and exacerbations in medicare older adults: a comparison of multiple approaches. PhD thesis, University of Maryland, 2021.
  3. Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift. 32nd International Conference on Machine Learning; July 6-11, 2015.
  4. Belkin M, Hsu D, Mitra P. Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate. 32nd Conference on Neural Information Processing Systems; Dec 2-8, 2018.

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Authors
Smith, L. A., Oakden-Rayner, L., Bird, A., Zeng, M., To, M.-S., Mukherjee, S., & Palmer, L. J.
Affiliations
  • The University of Adelaide (AU)
  • Flinders University (AU)
Corresponding author
Smith, Luke A
ORCID
Venue
The Lancet Digital Health
Publisher
Elsevier BV
Published
2023-12
Volume
5
Issue
12
Pages
e872-e881
ISSN
2589-7500 (ISSN-L)
DOI
10.1016/s2589-7500(23)00177-2
OpenAlex
W4388922522
Cited by
116
References
52
Citations by year
2026: 42 · 2025: 50 · 2024: 24
FWCI
5.40
Type
journal article
Language
en
Open access
Licence
Locations
  • The Lancet Digital Health (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
Australian Government
Topics
  • Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Chronic Obstructive Pulmonary Disease (COPD) Research
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
chronic obstructive pulmonary disease, machine learning, deep learning, prognostic model, long-term prognosis, COPD exacerbation risk, mortality prediction, FEV1 decline, lung function decline, disease severity index, model performance comparison, internal validation, risk of bias, generalizability, research gaps, health economics, health equity
MeSH terms
Deep Learning, Adult, Humans, Prognosis, Quality of Life, Reproducibility of Results, Pulmonary Disease, Chronic Obstructive

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