9 papers
Technical note on: Zero-Training Feature-Space Alignment via Information Geometry
Behraj Khan, Tahir Qasim Syed, Syed Ahmad Chan Bukhari
Deep vision models often degrade under distribution shift. Test-time adaptation can improve robustness but typically requires iterative optimization, hyperparameter tuning, and mul…
CalTwin: Towards Calibrated, Shift-Robust Medical World Models via Fisher-Information Regularisation
Behraj Khan, Shabir Ahmad, Syed Ahmad Chan Bukhari +1
Medical world models aim to learn a latent state of patient or organ physiology and a transition function that forecasts how that state evolves under interventions, supporting down…
PIcsC: Partitioning-Induced Covariate Shift Correction
Behraj Khan, Behroz Mirza, Syed Ahmad Chan Bukhari +1
Covariate shift across training-data partitions biases model selection and parameter estimation in cross-validation, lifelong learning, and federated learning. We propose \textit{P…
Graph-Constrained Policy Learning for Extreme Clinical Code Prediction
Amritpal Singh, Sebastian Torres, Khawar Shakeel +1
Clinical code prediction maps unstructured discharge summaries to ICD-10-CM leaf codes in a large, sparse, and deeply hierarchical label space. Most systems treat the task as flat…
Early Prediction of Multi-Label Care Escalation Triggers in the Intensive Care Unit Using Electronic Health Records
Syed Ahmad Chan Bukhari, Amritpal Singh, Shifath Hossain +1
Intensive Care Unit (ICU) patients often present with complex, overlapping signs of physiological deterioration that require timely escalation of care. Traditional early warning sy…
A Machine Learning Framework for Pathway-Driven Therapeutic Target Discovery in Metabolic Disorders
Iram Wajahat, Amritpal Singh, Fazel Keshtkar +1
Metabolic disorders, particularly type 2 diabetes mellitus (T2DM), represent a significant global health burden, disproportionately impacting genetically predisposed populations su…
Confidence-Calibrating Regularization for Robust Brain MRI Segmentation Under Domain Shift
Behraj Khan, Tahir Qasim Syed, Syed Ahmad Chan Bukhari
The Segment Anything Model (SAM) exhibits strong zero-shot performance on natural images but suffers from domain shift and overconfidence when applied to medical volumes. We propos…
A Narrative-Driven Computational Framework for Clinician Burnout Surveillance
Syed Ahmad Chan Bukhari, Fazel Keshtkar, Alyssa Meczkowska
Clinician burnout poses a substantial threat to patient safety, particularly in high-acuity intensive care units (ICUs). Existing research predominantly relies on retrospective sur…
Accelerating Medical Knowledge Discovery through Automated Knowledge Graph Generation and Enrichment
Mutahira Khalid, Raihana Rahman, Asim Abbas +3
Knowledge graphs (KGs) serve as powerful tools for organizing and representing structured knowledge. While their utility is widely recognized, challenges persist in their automatio…