1,077 research outputs found

    Could Data Broker Information Threaten Physician Prescribing and Professional Behavior?

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    Privacy is threatened by the extent of data collected and sold by consumer data brokers. Physicians, as individual consumers, leave a ‘data trail’ in the offline (e.g. through traditional shopping) and online worlds (e.g. through online purchases and use of social media). Such data could easily and legally be used without a physician’s knowledge or consent to influence prescribing practices or other physician professional behavior. We sought to determine the extent to which such consumer data was available on a sample of more than 3,000 physicians, healthcare faculty and healthcare system staff at one university’s health units. Using just work email addresses for these employees we cheaply and quickly obtained external data on nearly two thirds of employees on demographic characteristics (e.g. income, top 10% national wealth, children at home, married), purchases (e.g. baby products, cooking, sports), behavior (e.g. charitable donor, discount shopper) and interests (e.g. automotive, health and wellness). Consumer data brokers have valuable, cost-effective and detailed information on many healthcare professionals, including data that could be used to segment, target, detail and generally market to physicians in ways that seem under‐appreciated. We call for greater attention to this potential aspect of physician-industry relationships

    Bidirectional Representation Learning from Transformers using Multimodal Electronic Health Record Data to Predict Depression

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    Advancements in machine learning algorithms have had a beneficial impact on representation learning, classification, and prediction models built using electronic health record (EHR) data. Effort has been put both on increasing models' overall performance as well as improving their interpretability, particularly regarding the decision-making process. In this study, we present a temporal deep learning model to perform bidirectional representation learning on EHR sequences with a transformer architecture to predict future diagnosis of depression. This model is able to aggregate five heterogenous and high-dimensional data sources from the EHR and process them in a temporal manner for chronic disease prediction at various prediction windows. We applied the current trend of pretraining and fine-tuning on EHR data to outperform the current state-of-the-art in chronic disease prediction, and to demonstrate the underlying relation between EHR codes in the sequence. The model generated the highest increases of precision-recall area under the curve (PRAUC) from 0.70 to 0.76 in depression prediction compared to the best baseline model. Furthermore, the self-attention weights in each sequence quantitatively demonstrated the inner relationship between various codes, which improved the model's interpretability. These results demonstrate the model's ability to utilize heterogeneous EHR data to predict depression while achieving high accuracy and interpretability, which may facilitate constructing clinical decision support systems in the future for chronic disease screening and early detection.Comment: in IEEE Journal of Biomedical and Health Informatics (2021

    The Guinea Pig as a Model for Sporadic Alzheimer\u27s Disease (AD): The Impact of Cholesterol Intake on Expression of AD-Related Genes

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    We investigated the guinea pig, Cavia porcellus, as a model for Alzheimer’s disease (AD), both in terms of the conservation of genes involved in AD and the regulatory responses of these to a known AD risk factor - high cholesterol intake. Unlike rats and mice, guinea pigs possess an Ab peptide sequence identical to human Ab. Consistent with the commonality between cardiovascular and AD risk factors in humans, we saw that a high cholesterol diet leads to up-regulation of BACE1 (b-secretase) transcription and down-regulation of ADAM10 (a-secretase) transcription which should increase release of Ab from APP. Significantly, guinea pigs possess isoforms of AD-related genes found in humans but not present in mice or rats. For example, we discovered that the truncated PS2V isoform of human PSEN2, that is found at raised levels in AD brains and that increases c-secretase activity and Ab synthesis, is not uniquely human or aberrant as previously believed. We show that PS2V formation is up-regulated by hypoxia and a high-cholesterol diet while, consistent with observations in humans, Ab concentrations are raised in some brain regions but not others. Also like humans, but unlike mice, the guinea pig gene encoding tau, MAPT, encodes isoforms with both three and four microtubule binding domains, and cholesterol alters the ratio of these isoforms. We conclude that AD-related genes are highly conserved and more similar to human than the rat or mouse. Guinea pigs represent a superior rodent model for analysis of the impact of dietary factors such as cholesterol on the regulation of AD-related genes

    Universal Gelation of Metal Oxide Nanocrystals via Depletion Attractions

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    Nanocrystal gelation provides a powerful framework to translate nanoscale properties into bulk materials and to engineer emergent properties through the assembled microstructure. However, many established gelation strategies rely on chemical reactions and specific interactions, e.g., stabilizing ligands or ions on the surface of the nanocrystals, and are therefore not easily transferrable. Here, we report a general gelation strategy via non-specific and purely entropic depletion attractions applied to three types of metal oxide nanocrystals. The gelation thresholds of two compositionally distinct spherical nanocrystals agree quantitatively, demonstrating the adaptability of the approach for different chemistries. Consistent with theoretical phase behavior predictions, nanocrystal cubes form gels at a lower polymer concentration than nanocrystal spheres, allowing shape to serve as a handle to control gelation. These results suggest that the fundamental underpinnings of depletion-driven assembly, traditionally associated with larger colloidal particles, are also applicable at the nanoscale
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