22,731 research outputs found

    A case study in open source innovation: developing the Tidepool Platform for interoperability in type 1 diabetes management.

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    OBJECTIVE:Develop a device-agnostic cloud platform to host diabetes device data and catalyze an ecosystem of software innovation for type 1 diabetes (T1D) management. MATERIALS AND METHODS:An interdisciplinary team decided to establish a nonprofit company, Tidepool, and build open-source software. RESULTS:Through a user-centered design process, the authors created a software platform, the Tidepool Platform, to upload and host T1D device data in an integrated, device-agnostic fashion, as well as an application ("app"), Blip, to visualize the data. Tidepool's software utilizes the principles of modular components, modern web design including REST APIs and JavaScript, cloud computing, agile development methodology, and robust privacy and security. DISCUSSION:By consolidating the currently scattered and siloed T1D device data ecosystem into one open platform, Tidepool can improve access to the data and enable new possibilities and efficiencies in T1D clinical care and research. The Tidepool Platform decouples diabetes apps from diabetes devices, allowing software developers to build innovative apps without requiring them to design a unique back-end (e.g., database and security) or unique ways of ingesting device data. It allows people with T1D to choose to use any preferred app regardless of which device(s) they use. CONCLUSION:The authors believe that the Tidepool Platform can solve two current problems in the T1D device landscape: 1) limited access to T1D device data and 2) poor interoperability of data from different devices. If proven effective, Tidepool's open source, cloud model for health data interoperability is applicable to other healthcare use cases

    Monitoring Self & World: A Novel Network Model of Hallucinations in Schizophrenia

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    Schizophrenia (Sz) is a psychotic disorder characterized by multifaceted symptoms including hallucinations (e.g. vivid perceptions that occur in the absence of external stimuli). Auditory hallucinations are the most common type of hallucination in Sz; roughly 70 percent of Sz patients report hearing voices specifically (e.g. auditory verbal hallucinations). Prior functional magnetic resonance imaging (fMRI) studies have provided initial insights into the neural mechanisms underlying hallucinations, implicating an anatomically-distributed network of cortical (sensory, insular, and inferior frontal cortex) and subcortical (hippocampal, striatal) regions. Yet, it remains unclear how this distributed network gives rise to hallucinations impacting different sensory modalities. The insular cortex is a central hub of a larger functional network called the salience network. By regulating default-mode network activity (associated with internally-directed thought), and fronto-parietal network activity (associated with externally-directed attention), the salience network is able to orient our attention to the most pressing matters (e.g. bodily pain, environmental threats, etc.). Abnormal salience monitoring is thought to underlie Sz symptoms; improper monitoring of salient internal events (e.g. auditory-verbal imagery, visual images) plausibly generates hallucinations, but no prior study has directly tested this hypothesis by exploring how sensory networks interact with the salience network in the context of hallucinations in Sz. This dissertation project combined exploratory and hypothesis-driven approaches to delineate functional neural markers of Sz symptoms. The first analysis explored the relationship between Sz symptom expression and altered functional communication between salience and default-mode networks. The second analysis explored fMRI signal fluctuations associated with modality-dependent (e.g. auditory, visual) hallucinations. The final analysis tested the hypothesis that abnormal functional communication between salience and sensory (e.g. auditory, visual) networks underlies hallucinations in Sz. The results suggest that there are three key players in the generation of auditory hallucinations in Sz: auditory cortex, hippocampus, and salience network. A novel functional network model of auditory hallucinations is proposed to account for these findings
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