Interventional Data Science Projects


Our goal is to use cutting-edge data analytics to directly improve the care of patients treated within the healthcare system.  To that end, we use advanced data processing and machine-learning techniques to predict which patients are at highest risk of certain outcomes, and who will benefit most from certain interventions.  Our key philosophy it to not only develop new methods and evaluate existing methods, but to apply those methods in real world scenarios to improve patient outcomes.  We seek to advance and rigorously evaluate these systems in a clinical context to determine if patient care can truly benefit from these novel tools.

Specific Studies:

Acute Kidney Alerts

Acute kidney injury (AKI) affects up to 20% of hospitalized patients and increases the risk of dying in the hospital by a factor of 10.  However, AKI is asymptomatic and can go unrecognized by even well-trained medical providers.  Automated detection of AKI coupled with provider alerting has the potential to improve outcomes.  We are conducting three randomized trials of AKI alerts to evaluate the ability of alerts to slow the progression of AKI, avoid dialysis, and save lives.

Electronic Alerts for Acute Kidney Injury Amelioration, ELAIA-1: Active.  This trial will randomize roughly 6,000 patients with AKI to AKI alerts or usual care with a goal of determining whether alerts reduce the rate of worsening of acute kidney injury, dialysis, or death.

ELAIA-2: Anticipated Start of January 2019.  This trial will randomize patients with AKI who are receiving a kidney-toxic medication to alerts (highlighting the particular medication) versus usual care.

ELAIA-3: Anticipated Start of January 2020.  This trial will use an advanced machine-learning technique known as uplift modeling to target AKI alerts to a subset of of patients who are most likely to be benefited, by reducing alert fatigue and improving overall effectiveness.

Predicting Imminent AKI

While AKI carries substantial risk, there remains no therapeutic intervention that can alter the course of AKI once it develops beyond optimizing usual care.  Our AKI Tomorrow studies use a real-time predictive model to identify hospitalized patients who are at risk of developing AKI within the next 24 hours.

AKI Tomorrow Pilot Phase: Currently Active.  This study will enroll 30 patients we predict may develop AKI in the next 24 hours.  After informed consent, we obtain blood, urine, and access to their electronic medical record to determine the accuracy of our algorithm and measure biomarkers to understand the underlying pathophysiology in order to guide the additional steps required to improve their care.

AKI Tomorrow Full Study: Anticipated Start of January 2019. By exporting data to an "artificial intelligence" server, we can create models that predict AKI with much greater fidelity and feed those predictions back into the clinical system.

Future Directions.  We will integrate this predictive engine with clinical laboratory medicine to facilitate reflex testing of residual blood samples to augment the information already available in the electronic health record.  Additionally, we will evaluate the efficacy of our AKI Tomorrow model.  Does it meaningfully improve care for hospitalized patients by randomizing to usual care vs. prediction-based intervention?

Bio Profile

Francis Perry Wilson, MD, MS

Principal Investigator

Assistant Professor of Medicine (Nephrology)

Interim Director, Program of Applied Translational Research

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Bio Profile

Chirag R Parikh, MD, PhD, FACP

Professor Adjunct

Director, Program of Applied Translational Research

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Bio Profile

Haiqun Lin, MD, PhD

Associate Professor of Biostatistics

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Aditya Biswas

Research Assistant, Yale University

Harold Feldman, MD, MSCE

George S. Pepper Professor of Public Health and Preventative Medicine, Yale University

Amit Garg, MD, PhD

Professor of Medicine (Nephrology), Western University, London, Ontario, Canada

Stephen Latham, JD, PhD

Director, Interdisciplinary Center for Bioethics, Yale University 

Melissa Martin

Research Associate, Yale University

Paul M. Palevsky, MD

Professor of Medicine, Renal-Electrolyte Division, University of Pittsburgh

ELAIA Publications

Contact Information

For more information, or if you are interested in collaborating on this study, please contact F. Perry Wilson

Project Funding

Funding for this project comes, in part, from the following grants:

K23 DK097201 

"Mediators & Prognostic Value of Muscle Mass & Function in Chronic Kidney Disease" 

R01 DK113191

"Optimizing Electronic Alerts for Acute Kidney Injury" 

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