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Your search keyword '"Rosenthal, Richard N."' showing total 11 results
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11 results on '"Rosenthal, Richard N."'

1. Enhancing Clinical Predictive Modeling through Model Complexity-Driven Class Proportion Tuning for Class Imbalanced Data: An Empirical Study on Opioid Overdose Prediction

Author

Liu, Yinan, Dong, Xinyu, Lyu, Weimin, Rosenthal, Richard N., Wong, Rachel, Ma, Tengfei, and Wang, Fusheng

Subjects
Computer Science - Machine Learning, Statistics - Applications
Abstract

Class imbalance problems widely exist in the medical field and heavily deteriorates performance of clinical predictive models. Most techniques to alleviate the problem rebalance class proportions and they predominantly assume the rebalanced proportions should be a function of the original data and oblivious to the model one uses. This work challenges this prevailing assumption and proposes that links the optimal class proportions to the model complexity, thereby tuning the class proportions per model. Our experiments on the opioid overdose prediction problem highlight the performance gain of tuning class proportions. Rigorous regression analysis also confirms the advantages of the theoretical framework proposed and the statistically significant correlation between the hyperparameters controlling the model complexity and the optimal class proportions.

Published
2023

4. Rapid Initiation of Injection Naltrexone for Opioid Use Disorder

Author

Shulman, Matisyahu, primary, Greiner, Miranda G., additional, Tafessu, Hiwot M., additional, Opara, Onumara, additional, Ohrtman, Kaitlyn, additional, Potter, Kenzie, additional, Hefner, Kathryn, additional, Jelstrom, Eve, additional, Rosenthal, Richard N., additional, Wenzel, Kevin, additional, Fishman, Marc, additional, Rotrosen, John, additional, Ghitza, Udi E., additional, Nunes, Edward V., additional, and Bisaga, Adam, additional

Published
2024
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11. Enhancing Clinical Predictive Modeling through Model Complexity-Driven Class Proportion Tuning for Class Imbalanced Data: An Empirical Study on Opioid Overdose Prediction.

Author

Liu Y, Dong X, Lyu W, Rosenthal RN, Wong R, Ma T, Kong J, and Wang F

Abstract

Class imbalance issues are prevalent in the medical field and significantly impact the performance of clinical predictive models. Traditional techniques to address this challenge aim to rebalance class proportions. They generally assume that the rebalanced proportions are derived from the original data, without considering the intricacies of the model utilized. This study challenges the prevailing assumption and introduces a new method that ties the optimal class proportions to model complexity. This approach allows for individualized tuning of class proportions for each model. Our experiments, centered on the opioid overdose prediction problem, highlight the performance gains achieved by this approach. Furthermore, rigorous regression analysis affirms the merits of the proposed theoretical framework, demonstrating a statistically significant correlation between hyperparameters controlling model complexity and the optimal class proportions., (©2024 AMIA - All rights reserved.)

Published
2024

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