@article{mehrotra_maity_2021, title={Simultaneous variable selection, clustering, and smoothing in function-on-scalar regression}, volume={11}, ISSN={["1708-945X"]}, DOI={10.1002/cjs.11668}, abstractNote={We address the problem of multicollinearity in a function‐on‐scalar regression model by using a prior that simultaneously selects, clusters, and smooths functional effects. Our methodology groups the effects of highly correlated predictors, performing dimension reduction without dropping relevant predictors from the model. We validate our approach via a simulation study, showing superior performance relative to existing dimension‐reduction approaches described in the function‐on‐scalar literature. We also demonstrate the use of our model on a data set of age‐specific fertility rates from the United Nations Gender Information database.}, journal={CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE}, author={Mehrotra, Suchit and Maity, Arnab}, year={2021}, month={Nov} }
 @article{loring_mehrotra_piccini_camm_carlson_fonarow_fox_peterson_pieper_kakkar_2020, title={Machine learning does not improve upon traditional regression in predicting outcomes in atrial fibrillation: an analysis of the ORBIT-AF and GARFIELD-AF registries}, volume={22}, ISSN={["1532-2092"]}, DOI={10.1093/europace/euaa172}, abstractNote={Abstract
               
                  Aims
                  Prediction models for outcomes in atrial fibrillation (AF) are used to guide treatment. While regression models have been the analytic standard for prediction modelling, machine learning (ML) has been promoted as a potentially superior methodology. We compared the performance of ML and regression models in predicting outcomes in AF patients.
               
               
                  Methods and results
                  The Outcomes Registry for Better Informed Treatment of Atrial Fibrillation (ORBIT-AF) and Global Anticoagulant Registry in the FIELD (GARFIELD-AF) are population-based registries that include 74 792 AF patients. Models were generated from potential predictors using stepwise logistic regression (STEP), random forests (RF), gradient boosting (GB), and two neural networks (NNs). Discriminatory power was highest for death [STEP area under the curve (AUC) = 0.80 in ORBIT-AF, 0.75 in GARFIELD-AF] and lowest for stroke in all models (STEP AUC = 0.67 in ORBIT-AF, 0.66 in GARFIELD-AF). The discriminatory power of the ML models was similar or lower than the STEP models for most outcomes. The GB model had a higher AUC than STEP for death in GARFIELD-AF (0.76 vs. 0.75), but only nominally, and both performed similarly in ORBIT-AF. The multilayer NN had the lowest discriminatory power for all outcomes. The calibration of the STEP modelswere more aligned with the observed events for all outcomes. In the cross-registry models, the discriminatory power of the ML models was similar or lower than the STEP for most cases.
               
               
                  Conclusion
                  When developed from two large, community-based AF registries, ML techniques did not improve prediction modelling of death, major bleeding, or stroke.
               }, number={11}, journal={EUROPACE}, author={Loring, Zak and Mehrotra, Suchit and Piccini, Jonathan P. and Camm, John and Carlson, David and Fonarow, Gregg C. and Fox, Keith A. A. and Peterson, Eric D. and Pieper, Karen and Kakkar, Ajay K.}, year={2020}, month={Nov}, pages={1635–1644} }
 @article{loring_mehrotra_piccini_2019, title={Machine learning in 'big data': handle with care}, volume={21}, ISSN={["1532-2092"]}, DOI={10.1093/europace/euz130}, abstractNote={A 70-year-old man presented to the emergency department with syncope. He was normotensive and tachycardic (180 b.p.m.). The 12-lead electrocardiogram showed a bidirectional ventricular tachycardia (BDVT): a narrower QRS with left posterior hemi-block (right axis deviation) and apical exit (negative V1-6) alternating on a beat-to-beat basis with a wider QRS with left anterior hemi-block (left axis deviation) and right bundle branch block. Arterial-blood gas showed severe metabolic alkalosis (pH 7.58) and hypokalaemia (1.48 mmol/L). He was defibrillated twice (ventricular fibrillation) and BDVT resumed after aggressive potassium replacement. Diagnostic work-up revealed adrenocorticotropic hormone (ACTH)- producing pheophomocytoma (}, number={9}, journal={EUROPACE}, author={Loring, Zak and Mehrotra, Suchit and Piccini, Jonathan P.}, year={2019}, month={Sep}, pages={1284–1285} }