Prognostication and risk factors for cystic fibrosis via automated machine learning
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Abstract Accurate prediction of survival for cystic fibrosis (CF) patients is instrumental in establishing the optimal timing for referring patients with terminal respiratory failure for lung transplantation (LT). Current practice considers referring patients for LT evaluation once the forced expiratory volume (FEV 1 ) drops below 30% of its predicted nominal value. While FEV 1 is indeed a strong predictor of CF-related mortality, we hypothesized that the survival behavior of CF patients exhibits a lot more heterogeneity. To this end, we developed an algorithmic framework, which we call AutoPrognosis, that leverages the power of machine learning to automate the process of constructing clinical prognostic models, and used it to build a prognostic model for CF using data from a contemporary cohort that involved 99% of the CF population in the UK. AutoPrognosis uses Bayesian optimization techniques to automate the process of configuring ensembles of machine learning pipelines, which involve imputation, feature processing, classification and calibration algorithms. Because it is automated, it can be used by clinical researchers to build prognostic models without the need for in-depth knowledge of machine learning. Our experiments revealed that the accuracy of the model learned by AutoPrognosis is superior to that of existing guidelines and other competing models.
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An unvalidated proof of concept using CF registry data in the UK has been developed to assess optimal time for lung transplant referral.
“An unvalidated proof of concept of this has already been developed using CF registry data in the UK to assess optimal time for lung transplant referral (32).”
From Virtual monitoring in CF – the importance of continuous monitoring in a multi-organ chronic condition (2023) · cites this paper as [32]
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- Authors
- Alaa, A. M., & van der Schaar, M.
- Affiliations
- University of California, Los Angeles (US)
- Corresponding author
- Alaa, Ahmed M.
- Venue
- Scientific Reports
- Published
- 2018-07-26
- Volume
- 8
- Issue
- 1
- Pages
- 11242-11242
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC6062529
- OpenAlex
- W2883161521
- Cited by
- 80
- References
- 66
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 9 · 2023: 14 · 2022: 4 · 2021: 14 · 2020: 23 · 2019: 5 · 2018: 1
- FWCI
- 8.10
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Alan Turing Institute
- Cystic Fibrosis Trust
- Topics
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Cystic Fibrosis Research Advances
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- cystic fibrosis, survival prediction, prognostic model, lung transplantation, FEV1, Bayesian optimization, automated machine learning, risk factors, clinical decision support
- MeSH terms
- Machine Learning, Automation, Cystic Fibrosis, United Kingdom, Humans, Prognosis, Respiratory Function Tests, Respiratory Insufficiency, Risk Factors, Biomarkers, Survival Analysis
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