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Cited by 118Artificial Intelligence in HealthcareMachine Learning in HealthcareStatistical Methods in Epidemiology

Prediction of premature all-cause mortality: A prospective general population cohort study comparing machine-learning and standard epidemiological approaches

Open access
Weng, Stephen F., Vaz, Luis, Qureshi, Nadeem, Kai, Joe·2019·PLOS ONE
DOI 10.1371/journal.pone.0214365PMC PMC6436798
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Abstract

BACKGROUND: Prognostic modelling using standard methods is well-established, particularly for predicting risk of single diseases. Machine-learning may offer potential to explore outcomes of even greater complexity, such as premature death. This study aimed to develop novel prediction algorithms using machine-learning, in addition to standard survival modelling, to predict premature all-cause mortality. METHODS: A prospective population cohort of 502,628 participants aged 40-69 years were recruited to the UK Biobank from 2006-2010 and followed-up until 2016. Participants were assessed on a range of demographic, biometric, clinical and lifestyle factors. Mortality data by ICD-10 were obtained from linkage to Office of National Statistics. Models were developed using deep learning, random forest and Cox regression. Calibration was assessed by comparing observed to predicted risks; and discrimination by area under the 'receiver operating curve' (AUC). FINDINGS: 14,418 deaths (2.9%) occurred over a total follow-up time of 3,508,454 person-years. A simple age and gender Cox model was the least predictive (AUC 0.689, 95% CI 0.681-0.699). A multivariate Cox regression model significantly improved discrimination by 6.2% (AUC 0.751, 95% CI 0.748-0.767). The application of machine-learning algorithms further improved discrimination by 3.2% using random forest (AUC 0.783, 95% CI 0.776-0.791) and 3.9% using deep learning (AUC 0.790, 95% CI 0.783-0.797). These ML algorithms improved discrimination by 9.4% and 10.1% respectively from a simple age and gender Cox regression model. Random forest and deep learning achieved similar levels of discrimination with no significant difference. Machine-learning algorithms were well-calibrated, while Cox regression models consistently over-predicted risk. CONCLUSIONS: Machine-learning significantly improved accuracy of prediction of premature all-cause mortality in this middle-aged population, compared to standard methods. This study illustrates the value of machine-learning for risk prediction within a traditional epidemiological study design, and how this approach might be reported to assist scientific verification.

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  • AI is expected to augment healthcare workflow through automated triage, improve the productivity of individual physicians, reduce human errors, discover better patterns of patient care, defray medical costs, perform minimally invasive surgery, and reduce mortality rates.

    “AI is expected to augment healthcare workflow through automated triage, improve the productivity of individual physicians, reduce human errors, discover better patterns of patient care, defray medical costs, perform minimally invasive surgery, and reduce mortality rates.8–14”

    From Educating Future Physicians in Artificial Intelligence (AI): An Integrative Review and Proposed Changes (2021) · cites this paper as [13]

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Research works printed but not linked: 1

  1. [23]Voets M, Møllersen K, LA B. Replication study: Development and validation of deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. arXiv. 2018;abs/1803.04337. PMC6553744 31170223

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Authors
Weng, S. F., Vaz, L., Qureshi, N., & Kai, J.
Affiliations
University of Nottingham (GB)
Corresponding author
Weng, Stephen F.
ORCID
Venue
PLOS ONE
Publisher
Public Library of Science (PLoS)
Published
2019-03-27
Volume
14
Issue
3
Pages
e0214365
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0214365
PMC
PMC6436798
OpenAlex
W2923291095
Cited by
118
References
35
Citations by year
2026: 9 · 2025: 14 · 2024: 17 · 2023: 11 · 2022: 16 · 2021: 27 · 2020: 20 · 2019: 4
FWCI
18.26
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Repository@Nottingham (University of Nottingham) (submittedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Figshare (submittedVersion, cc-by)
Funders
  • National Institute for Health and Care Research
  • NIHR School for Primary Care Research
Topics
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Keywords
mortality prediction, machine learning, random forest, deep learning, Cox proportional hazards model, risk prediction model, model discrimination, model calibration, middle-aged adults, UK Biobank
MeSH terms
Deep Learning, Adult, Aged, Female, United Kingdom, Humans, Male, Middle Aged, Prognosis, Prospective Studies, ROC Curve, Logistic Models, Biological Specimen Banks, Mortality, Premature

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