UCTV Seminars

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Mobile health (mHealth) technologies allow for the generation of intensive care unit medical information, literally, in the palm of your hand. A smart phone can be transformed into a mobile heart monitor to diagnose atrial fibrillation, and continuous glucose monitoring has revolutionized the way diabetics manage their blood sugar levels. The digitization of human health through noninvasive devices and sensors can provide meaningful measures of individual wellness outside of a clinical environment. This information can then be used to guide health decisions - or personalize medicine. However, mHealth data presents a computational challenge as it can be both wide and long (i.e. big data). Furthermore, this challenge can be broken into two components that are both vital to the production of actionable health care: data storage and processing; and its interpretation. It is this latter component that is notoriously omitted in the conversation on big data. This talk will focus on analytical methods designed to interpret information from big data sources. Particularly, this talk will address approaches to analyze data on numerous variables from multiple sources which are serially correlated over time. This investigation is motivated by a recent mHealth study conducted at the Scripps Translational Science Institute designed to assess the relationship between neurocognitive and cardiovascular/pulmonary measurements, and the overall health benefits of meditation.