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20242026
most citedTrustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability

2 citations · 3 across the 3 of their papers we have counts for

collaborators

10 papers

cs.AI20262 cited

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability

Abdullah Mamun, Shovito Barua Soumma, Hassan Ghasemzadeh

Ensuring trust in AI systems is essential for the safe and ethical integration of machine learning systems into high-stakes domains such as digital health. Key dimensions, includin…

cs.LG2026

Counterfactual Modeling with Fine-Tuned LLMs for Health Intervention Design and Sensor Data Augmentation

Shovito Barua Soumma, Asiful Arefeen, Stephanie M. Carpenter +2

Counterfactual explanations (CFEs) provide human-centric interpretability by identifying the minimal, actionable changes required to alter a machine learning model's prediction. Th…

cs.LG20261 cited

Hybrid Attention Model Using Feature Decomposition and Knowledge Distillation for Glucose Forecasting

Ebrahim Farahmand, Shovito Barua Soumma, Nooshin Taheri Chatrudi +1

The availability of continuous glucose monitors as over-the-counter commodities have created a unique opportunity to monitor a person's blood glucose levels, forecast blood glucose…

cs.LG2026

GlyRAG: Context-Aware Retrieval-Augmented Framework for Blood Glucose Forecasting

Shovito Barua Soumma, Hassan Ghasemzadeh

Accurate blood glucose forecasting using continuous glucose monitoring (CGM) data can support the early prediction of dysglycemic risk. However, current neural-network-based foreca…

cs.LG2025

Self-Supervised Learning and Opportunistic Inference for Continuous Monitoring of Freezing of Gait in Parkinson's Disease

Shovito Barua Soumma, Daniel Peterson, Shyamal Mehta +1

Parkinson's disease (PD) is a progressive neurological disorder that impacts the quality of life significantly, making in-home monitoring of motor symptoms such as Freezing of Gait…

cs.AI2025

SenseCF: LLM-Prompted Counterfactuals for Intervention and Sensor Data Augmentation

Shovito Barua Soumma, Asiful Arefeen, Stephanie M. Carpenter +2

Counterfactual explanations (CFs) offer human-centric insights into machine learning predictions by highlighting minimal changes required to alter an outcome. Therefore, CFs can be…