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AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning
Ahmed M. Alaa · Mihaela van der Schaar

Thu Jul 12 02:40 AM -- 02:50 AM (PDT) @ K1
in Other Applications 2 »

Clinical prognostic models derived from largescalehealthcare data can inform critical diagnosticand therapeutic decisions. To enable off-theshelfusage of machine learning (ML) in prognosticresearch, we developed AUTOPROGNOSIS:a system for automating the design of predictivemodeling pipelines tailored for clinical prognosis.AUTOPROGNOSIS optimizes ensembles ofpipeline configurations efficiently using a novelbatched Bayesian optimization (BO) algorithmthat learns a low-dimensional decomposition ofthe pipelines’ high-dimensional hyperparameterspace in concurrence with the BO procedure.This is achieved by modeling the pipelines’ performancesas a black-box function with a Gaussianprocess prior, and modeling the “similarities”between the pipelines’ baseline algorithmsvia a sparse additive kernel with a Dirichlet prior.Meta-learning is used to warmstart BO with externaldata from “similar” patient cohorts by calibratingthe priors using an algorithm that mimicsthe empirical Bayes method. The system automaticallyexplains its predictions by presentingthe clinicians with logical association rules thatlink patients’ features to predicted risk strata. Wedemonstrate the utility of AUTOPROGNOSIS using10 major patient cohorts representing various aspectsof cardiovascular patient care.

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Author Information

Ahmed M. Alaa (UCLA)
Mihaela van der Schaar (UCLA)
Mihaela van der Schaar

Professor van der Schaar is John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge, a Turing Faculty Fellow at The Alan Turing Institute in London, and Chancellor's Professor at UCLA. She was elected IEEE Fellow in 2009. She has received numerous awards, including the Oon Prize on Preventative Medicine from the University of Cambridge (2018), an NSF Career Award (2004), 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award. She holds 35 granted USA patents. In 2019, she was identified by National Endowment for Science, Technology and the Arts as the female researcher based in the UK with the most publications in the field of AI. She was also elected as a 2019 "Star in Computer Networking and Communications". Her current research focus is on machine learning, AI and operations research for healthcare and medicine. For more details, see her website: http://www.vanderschaar-lab.com/

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