The Dr. Keyhan and Dr. Jafar Mobasseri Endowed Chair for Innovations in Cancer Research
Joint Appointment, Urology, Glickman Urological & Kidney Institute
Location: Cleveland Clinic Main Campus
My general research interest lies in medical decision making. More specifically, my research is focused on the development, validation, and use of prediction models. Most of these models are available online, and designed for physician use, at http://riskcalc.org/. I am also interested in quality of life assessment to support medical decision making (such as utility assessment), decision analysis, cost-effectiveness analysis, and comparative effectiveness.
Michael W. Kattan, MBA, PhD, is the Dr. Keyhan and Dr. Jafar Mobasseri Endowed Chair for Innovations in Cancer Research. He is Chairman, Department of Quantitative Health Sciences, at Cleveland Clinic's main campus; Professor of Medicine, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland; Professor, Division of General Medical Sciences - Oncology, Cancer Center, School of Medicine, Case Western Reserve University; and Professor, Department of Epidemiology and Biostatistics, School of Medicine, Case Western Reserve University.
More details can be found here.
Medical Education - University of Houston
Management of Information Systems
Houston, TX USA
Graduate School - University of Arkansas, Fayetteville
Computer Information Systems/Quantitative Analysis
Fayetteville, AR USA
Undergraduate - University of Arkansas, Fayetteville
Fayetteville, AR USA
Ever since my dissertation, “A Comparison of Machine Learning with Traditional Statistical Techniques,” I’ve had a long-standing interest in machine learning (ML) and artificial intelligence (AI). At first, I compared AI with human experts  to better understand when one should outperform the other . I then compared ML with traditional statistical techniques, similarly, trying to understand when one would prove superior. I first developed a theoretical framework to describe the factors that should drive the performance in favor of or against ML in any given situation . With the framework in place, I simulated data to illustrate the validity of this framework . I later published a condensed illustration of the framework . In wanting to apply ML in more varied applications, I noticed they were not well suited to handle time-until-event data and built these extensions [6,7]. With that in place, I was able to compare a variety of ML techniques with the standard statistical approach for time-until-event data, Cox proportional hazards regression . What matters most is how well these ML and AI techniques fare in real-world data; to this end, I’ve studied their performances when predicting prostate cancer recurrence , clinical deterioration in the ward , and pelvic floor disorders after delivery . In a recent comparative effectiveness study of bariatric surgery, we found that random forests were best for 2 of the models, but regression was superior for the remaining 6 models . This disappointment for random forests led us to pursue enhancement, specifically, adding multi-objective particle swarm optimization (MOPSO). We found the combination to outperform random forests alone , though random forests did well for us predicting progression of diabetic kidney disease . More recently, when predicting good postoperative depth of focus after cataract surgery, extreme gradient boost outperformed logistic regression . All of these complex issues regarding machine learning vs. statistical methods are discussed in our book .
Most of my statistical prediction modeling preferences are outlined in a recent book. Get it here.
Inspired by personal frustrations with medical uncertainty, I am particularly interested in statistical prediction models and medical decision making. The most common mistakes made, with ways to fix them, are discussed here.
A. Prediction Model Development
B. Prediction Model Assessment
C. Prediction Communication and Interpretation
D. Predictions Doctors Make
E. Decision Analysis and Utility Assessment
F. Novel Uses of Prediction Models
G. General Biostatistical Issues
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A University of Washington team focused on aggressive skin cancer partnered with Dr. Michael Kattan, pulling from his expertise in designing oncologic predictive models.
Dr. Misra-Hebert and colleagues assessed a Cleveland Clinic model’s ability to predict the likelihood that a patient would need to be readmitted to the hospital within 30 days of discharge.
This latest prediction model from Drs. Kattan and Jehi helps identify which COVID-19-positive patients are at greatest risk for severe COVID-19, which may help physicians prioritize who should receive COVID-19 vaccines first.
The first U.S. cost-effectiveness analysis in decades supports more surgical evaluations, suggesting the up-front cost of evaluation is significantly smaller than the price paid by patients, society and healthcare systems when medications alone are used.
The first-in-class individual prediction model, developed by Drs. Jehi and Kattan, reveals new characteristics that affect a person's risk for testing positive, including taking certain medications and vaccination history.
Staff in the Department of Quantitative Health Sciences have created a dashboard to help stay current on COVID-19 case and mortality U.S. data.