9 citations · 11 across the 8 of their papers we have counts for
8 papers
Scaling Recurrence-aware Foundation Models for Clinical Records via Next-Visit Prediction
Haresh Rengaraj Rajamohan, Xiang Gao, Weicheng Zhu +10
While large-scale pretraining has revolutionized language modeling, its potential remains underexplored in healthcare with structured electronic health records (EHRs). We present R…
Self-Supervised Learning for Knee Osteoarthritis: Diagnostic Limitations and Prognostic Value of Hospital Data
Haresh Rengaraj Rajamohan, Yuxuan Chen, Kyunghyun Cho +1
This study assesses whether self-supervised learning (SSL) improves knee osteoarthritis (OA) modeling for diagnosis and prognosis relative to ImageNet-pretrained initialization. We…
Foundation Models for Clinical Records at Health System Scale
Haresh Rengaraj Rajamohan, Xiang Gao, Weicheng Zhu +5
Large-scale pretraining has transformed modeling of language and other data types, but its potential remains underexplored in healthcare with structured electronic health records (…
A Progressive Risk Formulation for Enhanced Deep Learning based Total Knee Replacement Prediction in Knee Osteoarthritis
Haresh Rengaraj Rajamohan, Richard Kijowski, Kyunghyun Cho +1
We developed deep learning models for predicting Total Knee Replacement (TKR) need within various time horizons in knee osteoarthritis patients, with a novel capability: the models…
MR-Transformer: Vision Transformer for Total Knee Replacement Prediction Using Magnetic Resonance Imaging
Chaojie Zhang, Shengjia Chen, Ozkan Cigdem +4
A transformer-based deep learning model, MR-Transformer, was developed for total knee replacement (TKR) prediction using magnetic resonance imaging (MRI). The model incorporates th…
PrimSeq: a deep learning-based pipeline to quantitate rehabilitation training
Avinash Parnandi, Aakash Kaku, Anita Venkatesan +7
Stroke rehabilitation seeks to increase neuroplasticity through the repeated practice of functional motions, but may have minimal impact on recovery because of insufficient repetit…
Sequence-to-Sequence Modeling for Action Identification at High Temporal Resolution
Aakash Kaku, Kangning Liu, Avinash Parnandi +7
Automatic action identification from video and kinematic data is an important machine learning problem with applications ranging from robotics to smart health. Most existing works…
Emotion Recognition from Speech
Kannan Venkataramanan, Haresh Rengaraj Rajamohan
In this work, we conduct an extensive comparison of various approaches to speech based emotion recognition systems. The analyses were carried out on audio recordings from Ryerson A…