Current Fellows

Objective and Equitable Identification of Delirium: An Artificial Intelligence Approach Based on Big Clinical Data (EEG & Video)

Delirium is an acute neuropsychiatric disorder characterized by disturbances of attention and awareness that impacts more than 1 in 5 hospitalized older adults. Delirium causes substantial burden and distress for patients and their caregivers and is strongly associated with poor outcomes, including long-term cognitive decline, prolonged hospitalization, and significantly increased mortality. While delirium can be managed and even prevented in 40% of patients with appropriate assessments, it remains routinely underdiagnosed across diverse clinical conditions. Efforts to identify delirium more consistently have created standardized interview-based tools such as the Confusion Assessment Method (CAM). Yet such tools are still limited by their intrinsic subjectivity, inter-rater variability, intermittent application, and are time-consuming. Additionally, these tools are challenging to use in certain contexts, such as with non-English-speaking patients. We have found that Spanish-speaking patients are 5.2 times less likely to be fully screened for delirium compared to English-speaking patients. Similarly, patients of color are less likely to receive proper assessments, despite facing higher risks. This highlights an urgent need for equitable and accurate methods to identify delirium across diverse clinical populations. Therefore, our goal is to develop novel, highly sensitive and generalizable tools to assess delirium objectively and equitably. To achieve this, I will leverage our lab’s prior work to develop 1) quantitative electroencephalography (qEEG)-based and 2) video-based tools, combined with machine learning and artificial intelligence (AI) techniques, such as human pose estimation, to detect and track the development of delirium. By focusing on diverse patient groups—including individuals from varied racial, ethnic, and language backgrounds—and employing non-language-based methods such as neurophysiological and behavioral evaluations through EEG and video analysis, we can provide a unique resource to objectively assess delirium. This approach can ensure high generalizability across diverse populations and aims to improve clinical care while addressing healthcare disparities.

Developing Genetic Models of Childhood Hyperphagia and Obesity

Bardet-Biedl syndrome (BBS) is a rare, autosomal recessive disorder. BBS proteins are involved in the function of cilia, cellular organelles that are essential for cellular signaling. Obesity, polydactyly, retinitis pigmentosa, renal anomalies, and learning difficulties are among the main features of BBS, wherein obesity starts in early childhood. Obesity in BBS has been attributed to the melanocortin 4 receptor (MC4R) pathway in the hypothalamus, which plays a major role in appetite control and energy balance. BBS proteins in the proopiomelanocortin (POMC) neurons activate MC4R neurons via the leptin receptor to initiate satiety. However, the contribution of ciliary dysfunction to obesity remains poorly understood. The goal of this project is to understand how BBS proteins play a role in hyperphagia and obesity using three complementary approaches in zebrafish: (1) monitoring the eating behavior; (2) quantifying lipid accumulation; and (3) characterizing relevant transcriptional changes. By leveraging these tools, we aim to (1) Determine whether different BBS gene mutants display variable leptin signaling and (2) Use zebrafish BBS models to screen new drugs. This work will inform further the understanding of MC4R disruption caused by BBS genes and could be used as a therapeutic testing platform.