ArticleValue in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research2022
A Framework for Using Real-World Data and Health Outcomes Modeling to Evaluate Machine Learning-Based Risk Prediction Models.
Article in Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 9 citations in OpenAlex.
- Governing AI for Pharmacovigilance in Low-Income Countries: Systems Perspective.Journal of medical Internet research · 2026Article
- Review
- Transforming lung transplantation with artificial intelligence: a narrative review from organ allocation to post-transplant management.Journal of thoracic disease · 2026Review
- Steps toward developing an algorithm to facilitate recognition of translational science.Journal of clinical and translational science · 2026Article
- In Silico Evaluation of Algorithm-Based Clinical Decision Support Systems: Protocol for a Scoping Review.JMIR research protocols · 2025Article
- Predicting surgical outcomes of acute diffuse peritonitis: Updated risk models based on real-world clinical data.Annals of gastroenterological surgery · 2024Article
- Longitudinal plasmode algorithms to evaluate statistical methods in realistic scenarios: an illustration applied to occupational epidemiology.BMC medical research methodology · 2023Article
- The lung allocation score and other available models lack predictive accuracy for post-lung transplant survival.The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation · 2022Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
objectivesWe propose a framework of health outcomes modeling with dynamic decision making and real-world data (RWD) to evaluate the potential utility of novel risk prediction models in clinical practice. Lung transplant (LTx) referral decisions in cystic fibrosis offer a complex case study.
methodsWe used longitudinal RWD for a cohort of adults (n = 4247) from the Cystic Fibrosis Foundation Patient Registry to compare outcomes of an LTx referral policy based on machine learning (ML) mortality risk predictions to referral based on (1) forced expiratory volume in 1 second (FEV
resultsOnly 12% of patients (95% confidence interval 11%-13%) were referred for LTx over 5 years under UC, compared with 19% (18%-20%) under FEV
conclusionsHealth outcomes modeling with RWD may help to identify novel ML risk prediction models with high potential real-world clinical utility and rule out further investment in models that are unlikely to offer meaningful real-world benefits.
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Registered trials
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