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Cited by 81Artificial Intelligence in HealthcareTransplantation: Methods and OutcomesMachine Learning in Healthcare

Predictive Abilities of Machine Learning Techniques May Be Limited by Dataset Characteristics: Insights from the UNOS Database

Miller, P. Elliott, Pawar, Sumeet, Vaccaro, Benjamin, McCullough, Megan, Rao, Pooja, Ghosh, Rohit, Warier, Prashant, Desai, Nihar R. +1 more·2019·Journal of Cardiac Failure
DOI 10.1016/j.cardfail.2019.01.018
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Abstract

Background Traditional statistical approaches to prediction of outcomes have drawbacks when applied to large clinical databases. It is hypothesized that machine learning methodologies might overcome these limitations by considering higher-dimensional and nonlinear relationships among patient variables.Methods and results The Unified Network for Organ Sharing (UNOS) database was queried from 1987 to 2014 for adult patients undergoing cardiac transplantation. The dataset was divided into 3 time periods corresponding to major allocation adjustments and based on geographic regions. For our outcome of 1-year survival, we used the standard statistical methods logistic regression, ridge regression, and regressions with LASSO (least absolute shrinkage and selection operator) and compared them with the machine learning methodologies neural networks, naïve-Bayes, tree-augmented naïve-Bayes, support vector machines, random forest, and stochastic gradient boosting. Receiver operating characteristic curves and C-statistics were calculated for each model. C-Statistics were used for comparison of discriminatory capacity across models in the validation sample. After identifying 56,477 patients, the major univariate predictors of 1-year survival after heart transplantation were consistent with earlier reports and included age, renal function, body mass index, liver function tests, and hemodynamics. Advanced analytic models demonstrated similarly modest discrimination capabilities compared with traditional models (C-statistic ≤0.66, all). The neural network model demonstrated the highest C-statistic (0.66) but this was only slightly superior to the simple logistic regression, ridge regression, and regression with LASSO models (C-statistic = 0.65, all). Discrimination did not vary significantly across the 3 historically important time periods.Conclusions The use of advanced analytic algorithms did not improve prediction of 1-year survival from heart transplant compared with more traditional prediction models. The prognostic abilities of machine learning techniques may be limited by quality of the clinical dataset.

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Authors
Miller, P. E., Pawar, S., Vaccaro, B., McCullough, M., Rao, P., Ghosh, R., Warier, P., Desai, N. R., & Ahmad, T.
Affiliations
  • Yale University (US)
  • Qure.ai Technologies Private Limited (India) (IN)
Corresponding author
Ahmad, Tariq
ORCID
Venue
Journal of Cardiac Failure
Publisher
Elsevier BV
Published
2019-06
Volume
25
Issue
6
Pages
479-483
ISSN
1071-9164 (ISSN-L)
DOI
10.1016/j.cardfail.2019.01.018
OpenAlex
W2911626728
Cited by
81
References
17
Citations by year
2026: 5 · 2025: 11 · 2024: 6 · 2023: 11 · 2022: 11 · 2021: 22 · 2020: 13 · 2019: 2
FWCI
13.07
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations
  • Journal of Cardiac Failure (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Health Sciences › Medicine › Surgery › Transplantation: Methods and Outcomes
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Keywords
heart transplantation, machine learning, clinical outcome prediction, dataset quality, model discrimination, artificial neural networks, random forest, support vector machine, logistic regression, LASSO, ridge regression, naive Bayes, organ allocation, UNOS registry
MeSH terms
Machine Learning, Forecasting, Humans, Tissue and Organ Procurement, United States, Survival Rate, Heart Transplantation, Databases, Factual, Neural Networks, Computer

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