Medicine and Health Sciences Commons^™

Predicting Adverse Outcomes In End Stage Renal Disease: Machine Learning Applied To The United States Renal Data System, Zeid Khitan, Alexis D. Jacob, Courtney Balentine, Adam N. Jacob, Juan R. Sanabria, Joseph I. Shapiro

Marshall Journal of Medicine

We examined machine learning methods to predict death within six months using data derived from the United States Renal Data System (USRDS). We specifically evaluated a generalized linear model, a support vector machine, a decision tree and a random forest evaluated within the context of K-10 fold validation using the CARET package available within the open source architecture R program. We compared these models with the feed forward neural network strategy that we previously reported on with this data set.

Outcomes Of Transcutaneous Aortic Valve Replacement Among High Risk Wv Sample Population., George M. Yousef, Julia Poe, Cameron Killmer, Basel Edris, Jason Mader, Ellen A. Thompson, Daniel Snavely, Silvestre Cansino, Joseph I. Shapiro, Mark A. Studeny

Marshall Journal of Medicine

Introduction:Transcatheter aortic valve replacement (TAVR) is a relatively new strategy for replacing the aortic valve. We elected to review our early experience to see if we could identify clinical characteristics at baseline or immediately following the procedure that would predict death within one year.

Methods:Charts for all patients assigned to receive TAVR procedure at St Mary’s medical center, Huntington, West Virginia between April, 2013 till November, 2016 were identified and reviewed. A total of seventy-two (72) cases were included.

Results: All cause mortality rate at index hospitalization, 30 days, and 12 months was 5.6%(N=4), 6.9%(N=5), 19.4%(N=14) respectively. Stroke …

Predicting Adverse Outcomes In Chronic Kidney Disease Using Machine Learning Methods: Data From The Modification Of Diet In Renal Disease, Zeid Khitan, Anna P. Shapiro, Preeya T. Shah, Juan R. Sanabria, Prasanna Santhanam, Komal Sodhi, Nader G. Abraham, Joseph I. Shapiro

Marshall Journal of Medicine

Background: Understanding factors which predict progression of renal failure is of great interest to clinicians.

Objectives: We examined machine learning methods to predict the composite outcome of death, dialysis or doubling of serum creatinine using the modification of diet in renal disease (MDRD) data set.

Methods: We specifically evaluated a generalized linear model, a support vector machine, a decision tree, a feed-forward neural network and a random forest evaluated within the context of 10 fold validation using the CARET package available within the open source architecture R program.

Results: We found that using clinical parameters available at entry into the …