AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning

By Ahmed M. Alaa, Mihaela van der Schaar. 2018

103 citations recorded by the source corpus.

Clinical prognostic models derived from largescale healthcare data can inform critical diagnostic and therapeutic decisions. To enable off-theshelf usage of machine learning (ML) in prognostic research, we developed AUTOPROGNOSIS: a system for automating the design of predictive modeling pipelines tailored for clinical prognosis. AUTOPROGNOSIS optimizes ensembles of pipeline configurations efficiently using a novel batched Bayesian optimization (BO) algorithm that learns a low-dimensional decomposition of the pipelines high-dimensional hyperparameter space in concurrence with the BO procedure. This is achieved by modeling the pipelines performances as a black-box function with a Gaussian process prior, and modeling the similarities between the pipelines baseline algorithms via a sparse additive kernel with a Dirichlet prior. Meta-learning is used to warmstart BO with external data from similar patient cohorts by calibrating the priors using an algorithm that mimics the empirical Bayes method. The system automatically explains its predictions by presenting the clinicians with logical association rules that link patients features to predicted risk strata. We demonstrate the utility of AUTOPROGNOSIS using 10 major patient cohorts representing various aspects of cardiovascular patient care.

Subfields: Artificial Intelligence, Health Information Management

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Citations in the corpus

Auto-sklearn: Efficient and Robust Automated Machine Learning (2019, methodology): “Since the publication of the original NIPS paper [14] Auto-sklearn has become a standard baseline for new approaches to automated machine learning such as FLASH [39], RECIPE [32], Hyperband [25], Auto-Prognosis [2], ML-PLAN [28], Auto-Stacker [10] and AlphaD3M [11].”
Natural Language Processing in Electronic Health Records in relation to healthcare decision-making: A systematic review (2023, unknown): “AutoPrognosis [121] χ √ Local Tabular Biomedical”
How artificial intelligence and machine learning can help healthcare systems respond to COVID-19 (2020, methodology): “…competing risks at the individual level– estimating the probabilities of a patient experiencing different adverse events and how these probabilities are changing over time—have shown improved prediction accuracy compared to conventional epidemiological methods ( Alaa and van der Schaar 2018).”
Mapping the Landscape of Artificial Intelligence Applications against COVID-19 (2020, methodology): “Other approaches go further, using patient characteristics such as age, gender and co-morbidities as features in a Bayesian optimization algorithm that is able to predict future resource usage at both an individual and a hospital level (Alaa & van der Schaar, 2018).”