202 citations · 539 across the 30 of their papers we have counts for
8 papers · 1 filter
A Causal Machine Learning Framework for Treatment Personalization in Clinical Trials: Application to Ulcerative Colitis
Cristian Minoccheri, Sophia Tesic, Kayvan Najarian +1
Randomized controlled trials estimate average treatment effects, but treatment response heterogeneity motivates personalized approaches. A critical question is whether statisticall…
LISM: Long-range Integrative State space Models via Input-Latent State Interactions
Cong Ma, Kayvan Najarian, Hovhannes Baghdasaryan +1
State space models (SSMs) are an emerging paradigm that achieves linear-time scaling, however, they intrinsically suffer from "curse of memory". The memory of SSMs, including Mamba…
Supervised Coupled Matrix-Tensor Factorization (SCMTF) for Computational Phenotyping of Patient Reported Outcomes in Ulcerative Colitis
Cristian Minoccheri, Sophia Tesic, Kayvan Najarian +1
Phenotyping is the process of distinguishing groups of patients to identify different types of disease progression. A recent trend employs low-rank matrix and tensor factorization…
Tensor Denoising via Amplification and Stable Rank Methods
Jonathan Gryak, Kayvan Najarian, Harm Derksen
Tensors in the form of multilinear arrays are ubiquitous in data science applications. Captured real-world data, including video, hyperspectral images, and discretized physical sys…
Prediction of Oral Food Challenge Outcomes via Ensemble Learning
Justin Zhang, Deborah Lee, Kylie Jungles +5
Oral Food Challenges (OFCs) are essential to accurately diagnosing food allergy due to the limitations of existing clinical testing. However, some patients are hesitant to undergo…
A Novel Tropical Geometry-based Interpretable Machine Learning Method: Application in Prognosis of Advanced Heart Failure
Heming Yao, Harm Derksen, Jessica R. Golbus +4
A model's interpretability is essential to many practical applications such as clinical decision support systems. In this paper, a novel interpretable machine learning method is pr…
Radial basis function kernel optimization for Support Vector Machine classifiers
Karl Thurnhofer-Hemsi, Ezequiel López-Rubio, Miguel A. Molina-Cabello +1
Support Vector Machines (SVMs) are still one of the most popular and precise classifiers. The Radial Basis Function (RBF) kernel has been used in SVMs to separate among classes wit…
An Unsupervised Feature Learning Approach to Reduce False Alarm Rate in ICUs
Behzad Ghazanfari, Fatemeh Afghah, Kayvan Najarian +3
The high rate of false alarms in intensive care units (ICUs) is one of the top challenges of using medical technology in hospitals. These false alarms are often caused by patients'…