Biomedical AI Projects
At the intersection of AI and healthcare, our biomedical research projects focus on developing intelligent, multimodal, and explainable systems to tackle real-world clinical challenges. From predicting the progression of Alzheimer’s disease to forecasting ICU patient outcomes, these works prioritize transparency, robustness, and practical deployment. Leveraging deep learning, time-series analysis, and XAI, we aim to support clinicians with trustworthy tools that improve diagnosis, prognosis, and critical care decision-making across diverse patient populations.
Alzheimer’s disease (AD) is the most common form of dementia, affecting millions globally, yet early and accurate detection remains a critical challenge. InfoLab leads a research program combining multimodal neuroimaging data (MRI, PET), clinical assessments, and genetic biomarkers with state-of-the-art deep learning to detect and track AD progression. Our models incorporate explainable AI techniques that highlight which brain regions and biomarkers drive predictions, enabling clinicians to interpret and trust model outputs. We further harden these systems against adversarial perturbations to ensure diagnostic robustness in real-world clinical settings.
Clinical decision-making demands the integration of heterogeneous data sources—lab results, imaging, clinical notes, vital signs, and genomic data—that no single model handles optimally. InfoLab develops dynamic ensemble frameworks that adaptively combine specialized sub-models at late fusion stages, weighting their contributions based on data availability and uncertainty. Each ensemble decision is paired with an explainability layer that surfaces the evidence supporting the recommendation, enabling clinicians to validate, override, or defer to the system with confidence. Applications span depression severity assessment, sepsis risk stratification, and post-surgical outcome prediction.
Predicting patient outcomes in the Intensive Care Unit (ICU)—mortality risk, length of stay, and deterioration events—can save lives and optimize resource allocation, yet the complexity of clinical time-series data makes this a formidable modeling challenge. InfoLab applies dynamic ensemble methods that fuse longitudinal vital signs, lab measurements, medication histories, and diagnostic codes from large-scale ICU databases such as MIMIC and eICU. Our explainable models generate patient-level risk scores alongside interpretable feature attributions, giving intensivists actionable insight into which clinical indicators are driving the prediction at each point in time.