Erhan Bilal

Divergent-Convergent Reasoning: Scaling Test-Time Compute through Structured Solution Synthesis

Bo Wen, Yuhao Chen, Erhan Bilal, Carla Agurto Rios et autres

Test-time compute can substantially improve Large Language Model (LLM) reasoning performance, yet how and when additional compute helps remains poorly understood. We study Divergent-Convergent Reasoning (DCR), a simple two-phase primitive consisting of an exploration phase that generates multiple candidate solutions followed by …

A foundation model for sleep-based risk stratification and clinical outcomes

Erhan Bilal, Matheus Araújo, Kristen L. Beck, Catherine Heinzinger et autres

Clinical sleep studies capture multiple physiologic signals, yet interpretation is often reduced to single summary measures of limited prognostic value, such as the apnea-hypopnea index. We present a foundation model that learns rich representations of sleep physiology from more than 10,000 clinical …

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0960 External Validation of Patient Risk Groups Derived from a Polysomnography Foundation Model

Matheus Araujo, Erhan Bilal, Kristen Beck, Samer Ghosn et autres

Abstract Introduction Polysomnography (PSG) reports rely on a handful of summary indices, such as the apnea-hypopnea index (AHI), which incompletely capture the multidimensional burden of sleep-disordered breathing and its downstream health risks. We developed a risk stratification pipeline that uses RoBERTa-based foundational …

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1164 Correlation of Structural Brain Imaging and Spectral Sleep Fragmentation in a Large Polysomnography Cohort

Erhan Bilal, Matheus Araujo, Kristen Beck, Samer Ghosn et autres

Abstract Introduction Sleep physiology and brain health are deeply interconnected. Fragmented sleep has been implicated in cognitive decline, neuroinflammation, and hippocampal vulnerability. However, studies linking quantitative structural MRI to polysomnography (PSG) metrics remain limited. We leveraged a PSG foundation model and a …

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How Modality Shapes Perception and Reasoning: A Study of Error Propagation in ARC-AGI

Bo Wen, Chen Wang, Erhan Bilal

ARC-AGI and ARC-AGI-2 measure generalization-through-composition on small color-quantized grids, and their prize competitions make progress on these harder held-out tasks a meaningful proxy for systematic generalization. Recent instruction-first systems translate grids into concise natural-language or DSL rules executed in generate-execute-select loops, yet …

Clarifying the role of metformin and statistical interpretation in sleep quality research among patients with diabetes

Haseeb Safdar Ali, Erhan Bilal, Muddassir Khalid, Mohammed Hammad Jaber Amin

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Utility of synthetic musculoskeletal gaits for generalizable healthcare applications

Yasunori Yamada, Masatomo Kobayashi, Kaoru Shinkawa, Erhan Bilal et autres

Deep-neural-network-based artificial intelligence enables quantitative gait analysis with commodity sensors. However, current gait-analysis models are usually specialized for specific clinical populations and sensor settings due to the limited size and diversity of available datasets. We propose an approach that involves using synthetic …

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Audio and linguistic prediction of objective and subjective cognition in older adults: what is the role of different prompts?

Varsha D. Badal, Caitlyn Tran, Danielle Glorioso, Rebecca Daly et autres

Background: Psycho-linguistic and audio data derived from speech may be useful in screening and monitoring cognitive aging. However, there are gaps in understanding the predictive value of different prompts (e.g., open ended or structured) and the relationship of features to subjective versus …

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1143 Innovations in Time Series Data Integration in a Polysomnographic Foundational Model Informing Risk Groups

Catherine Heinzinger, Erhan Bilal, Kristen L. Beck, Samer Ghosn et autres

Abstract Introduction Despite the abundance of polysomnography (PSG) data, the limited summary metrics used in existing approaches may not provide the most informative insights for clinical decision-making. We hypothesized that a new data-driven clustering method using the entire multimodal raw PSG data …

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0410 Cardiovascular Disease Incidence Differs by Patient Cluster Based on Raw Polysomnography Data Foundational Model

Catherine Heinzinger, Erhan Bilal, Samer Ghosn, Kristen Beck et autres

Abstract Introduction Traditional sleep measures are not consistently associated with incident cardiovascular events in observational studies. We hypothesized deep learning approaches could uniquely identify distinct patient groups with varying cardiovascular disease (CVD) incidence, independent of traditional measures. Methods We used artificial intelligence …

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A Foundation Model for Sleep-Based Risk Stratification and Clinical Outcomes

Jeffrey Rogers, Erhan Bilal, Kristen Beck, Catherine Heinzinger et autres

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A novel approach for phenotypic characterization of sleep disorders

Erhan Bilal, Catherine Heinzinger, Samer Ghosn, Nancy Foldvary Schaefer et autres

Obstructive sleep apnea (OSA) is associated with increased morbidity and mortality, afflicting nearly one billion individuals worldwide. Polysomnography is the current gold standard for diagnosing OSA. It is an expensive, timeconsuming exam that relies on a single numerical measure of the frequency …

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