5 citations · 11 across the 10 of their papers we have counts for
10 papers
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…
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…
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…
RealAC: A Domain-Agnostic Framework for Realistic and Actionable Counterfactual Explanations
Asiful Arefeen, Shovito Barua Soumma, Hassan Ghasemzadeh
Counterfactual explanations provide human-understandable reasoning for AI-made decisions by describing minimal changes to input features that would alter a model's prediction. To b…
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…
Enhancing Metabolic Syndrome Prediction with Hybrid Data Balancing and Counterfactuals
Sanyam Paresh Shah, Abdullah Mamun, Shovito Barua Soumma +1
Metabolic Syndrome (MetS) is a cluster of interrelated risk factors that significantly increases the risk of cardiovascular diseases and type 2 diabetes. Despite its global prevale…
Freezing of Gait Detection Using Gramian Angular Fields and Federated Learning from Wearable Sensors
Shovito Barua Soumma, S M Raihanul Alam, Rudmila Rahman +4
Freezing of gait (FOG) is a debilitating symptom of Parkinson's disease that impairs mobility and safety by increasing the risk of falls. An effective FOG detection system must be…
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…
Wearable-Based Real-time Freezing of Gait Detection in Parkinson's Disease Using Self-Supervised Learning
Shovito Barua Soumma, Kartik Mangipudi, Daniel Peterson +2
LIFT-PD is an innovative self-supervised learning framework developed for real-time detection of Freezing of Gait (FoG) in Parkinson's Disease (PD) patients, using a single triaxia…
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…