Benchmarking clinical risk prediction algorithms with ensemble machine learning for the noninvasive diagnosis of liver fibrosis in NAFLD

Charu, Vivek; Liang, Jane W.; Mannalithara, Ajitha; Kwong, Allison; Tian, Lu; Kim, W. Ray

doi:10.1097/hep.0000000000000908

Cited by 35Liver Disease Diagnosis and TreatmentArtificial Intelligence in HealthcareLiver Diseases and Immunity

Benchmarking clinical risk prediction algorithms with ensemble machine learning for the noninvasive diagnosis of liver fibrosis in NAFLD

Charu, Vivek, Liang, Jane W., Mannalithara, Ajitha, Kwong, Allison, Tian, Lu, Kim, W. Ray·2024·Hepatology

DOI 10.1097/hep.0000000000000908

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Abstract

Background and Aims: Ensemble machine-learning methods, like the superlearner, combine multiple models into a single one to enhance predictive accuracy. Here we explore the potential of the superlearner as a benchmarking tool for clinical risk prediction, illustrating the approach to identifying significant liver fibrosis among patients with NAFLD. Approach and Results: We used 23 demographic/clinical variables to train superlearner(s) on data from the NASH-clinical research network observational study (n = 648) and validated models with data from the FLINT trial (n = 270) and National Health and Nutrition Examination Survey (NHANES) participants with NAFLD (n = 1244). Comparing the superlearner’s performance to existing models (Fibrosis-4 [FIB-4], NAFLD fibrosis score, Forns, AST to Platelet Ratio Index [APRI], BARD, and Steatosis-Associated Fibrosis Estimator [SAFE]), it exhibited strong discriminative ability in the FLINT and NHANES validation sets, with AUCs of 0.79 (95% CI: 0.73–0.84) and 0.74 (95% CI: 0.68–0.79) respectively. Conclusions: Notably, the SAFE score performed similarly to the superlearner, both of which outperformed FIB-4, APRI, Forns, and BARD scores in the validation data sets. Surprisingly, the superlearner derived from 12 base models matched the performance of one with 90 base models. Overall, the superlearner, being the “best-in-class” machine-learning predictor, excelled in detecting fibrotic NASH, and this approach can be used to benchmark the performance of conventional clinical risk prediction models.

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Benchmarking clinical risk prediction algorithms with ensemble machine learning: An illustration of the superlearner algorithm for the non-invasive diagnosis of liver fibrosis in non-alcoholic fatty liver diseasePreprint
Charu, V., Liang, J. W., Mannalithara, A., Kwong, A. J., Tian, L., & Kim, W. R. (2023) · medRxiv · doi:10.1101/2023.08.02.23293569 · Cited by 1 · see citing works
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Authors: Charu, V., Liang, J. W., Mannalithara, A., Kwong, A., Tian, L., & Kim, W. R.
Affiliations: Stanford Medicine (US)
Department of Biomedical Data Sciences, Stanford University School of Medicine, Stanford, California, USA
Corresponding author: Charu, Vivek
ORCID: Charu, Vivek
Liang, Jane W.
Mannalithara, Ajitha
Kwong, Allison
Tian, Lu
Kim, W. Ray
Venue: Hepatology
Publisher: Ovid Technologies (Wolters Kluwer Health)
Published: 2024-04-30
Volume: 80
Issue: 5
Pages: 1184-1195
ISSN: 0270-9139 (ISSN-L)
DOI: 10.1097/hep.0000000000000908
OpenAlex: W4396515817
Cited by: 35
References: 22
Citations by year: 2026: 13 · 2025: 21 · 2024: 1
FWCI: 10.62
Type: journal article
Language: en
Open access: closed
Locations: Hepatology (publishedVersion)
PubMed (publishedVersion)
Funders: National Institutes of Health (KL2TR003143)
National Institute on Alcohol Abuse and Alcoholism (K23AA029197)
National Institute of Diabetes and Digestive and Kidney Diseases (R01DK127224, P30DK116074)
Topics: Health Sciences › Medicine › Epidemiology › Liver Disease Diagnosis and Treatment
Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Health Sciences › Medicine › Hepatology › Liver Diseases and Immunity
Keywords: liver fibrosis, non-alcoholic fatty liver disease, nonalcoholic steatohepatitis, super learner, ensemble learning, clinical risk prediction, benchmarking, noninvasive fibrosis assessment, FIB-4, APRI, NHANES
MeSH terms: Machine Learning, Adult, Algorithms, Female, Humans, Liver Cirrhosis, Male, Middle Aged, Risk Assessment, Benchmarking, Non-alcoholic Fatty Liver Disease

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Benchmarking clinical risk prediction algorithms with ensemble machine learning for the noninvasive diagnosis of liver fibrosis in NAFLD

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