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on the development of artificial intelligence/machine learning algorithms to integrate multiple sources of patient-derived data, such as optical coherence tomography (OCT), retinal fundus photography (RFP), electronic
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developed and tested fall prevention interventions, examined the feasibility of digital health solutions such as apps, ingestible sensors, and wearables for medication adherence and vaccine uptake, and
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, perform data cleaning. Develop machine learning algorithms, natural language processing tools, and end user tools. Develop and interpret MCMC models; develop meta-models. Analyze epidemiologic and
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and wellbeing of older adults who seek emergency care. We have developed and tested fall prevention interventions, examined the feasibility of digital health solutions such as apps, ingestible sensors
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structured and unstructured biomedical data, including EHR data, wearable sensor time-series data, or clinical annotations. Demonstrated ability to build interactive or web-based systems using frameworks with
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solutions to maximize recruitment and retention Serve as a resource and participate in study initiation and close out duties All ranks: Mark retinal images to develop AI algorithms for image biomarkers
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prevention, and digital health. The Goldberg lab uses sensor-based technology including Apple Watches and iPhones to conduct fitness and cognitive tests in older adults. Funding support comes from multi-year