Injury, such as falls, motor vehicle crashes, and drug overdose, is a major source of morbidity and mortality. The interaction between injury and disease is complex and mutually causative. For instance, patients with Alzheimer’s Disease or Parkinson’s Disease are known to be at heightened risk of hip fracture from falls and in turn injurious falls among these patients can drastically alter the trajectory of the disease. So far, research on injury-disease interaction has been scant and fragmented. The proposed project is aimed at uncovering the gestalt of the relations between different injuries and different diseases through a data science approach.

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Our goal is use a large pool of homecare data (including structured data, free text clinical notes, and recorded patient-provider phone conversations) to build predictive models that help identify patients at risk for poor outcomes (like hospital admission or falls).

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Despite the promise of predictive analytics in healthcare, the lack of continuous internal sensing devices has impeded its realization. With the exception of CGMs, no current commercially available wearable devices yield information intimate to the body. To overcome this deficiency, our research group has developed a minimally invasive wearable device capable of continuous monitoring of glucose and electrolytes in the superficial layer of the skin in an extremely minimally invasive manner.

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Alzheimer’s disease and related dementia (AD/dementia) represent a looming public health crisis, affecting roughly 5 million people in the U.S. and 11% of older adults. As with other chronic conditions, racial/ethnic and socio-economic disparities exist in the prevalence and burden of illness. However, less is known about how disparities in access to care influence the care trajectories – i.e., the scope, frequency and sequence of services used across healthcare settings – of those with AD/dementia.

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Predicting preterm birth in nulliparous women is challenging and our efforts to develop predictors for that condition from environmental variables produce insufficient classifier accuracy. Recent studies highlight the involvement of common genetic variants in length of pregnancy. This project involves the development of a risk score for preterm birth based on both genetic and environmental attributes.

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Using machine learning to conduct brain state classification at real-time on EEG/fNIRS/fMRI data.

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DNA sequence reads from a community of microbial genomes are currently processed without considering sequence variants. The project involves building a processing pipeline of such billions of short reads, identifying closest strains they might belong to, assembling them into specific clones, calling their variants, and analyzing the dynamic nature of these bacterial strains along sampling points.

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