Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical models typically requires extraction of curated predictor variables from normalized EHR data, a labor-intensive process that discards the vast majority of information in each patient’s record. We propose a representation of patients’ entire raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format. We demonstrate that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization. We validated our approach using de-identified EHR data from two US academic medical centers with 216,221 adult patients hospitalized for at least 24 h. In the sequential format we propose, this volume of EHR data unrolled into a total of 46,864,534,945 data points, including clinical notes. Deep learning models achieved high accuracy for tasks such as predicting: in-hospital mortality (area under the receiver operator curve [AUROC] across sites 0.93–0.94), 30-day unplanned readmission (AUROC 0.75–0.76), prolonged length of stay (AUROC 0.85–0.86), and all of a patient’s final discharge diagnoses (frequency-weighted AUROC 0.90). These models outperformed traditional, clinically-used predictive models in all cases. We believe that this approach can be used to create accurate and scalable predictions for a variety of clinical scenarios. In a case study of a particular prediction, we demonstrate that neural networks can be used to identify relevant information from the patient’s chart.

Atopic dermatitis (AD) and psoriasis are the two most common chronic skin diseases. However patients with AD, but not psoriasis, suffer from frequent skin infections. To understand the molecular basis for this phenomenon, skin biopsies from AD and psoriasis patients were analyzed using GeneChip microarrays. The expression of innate immune response genes, human β defensin (HBD)-2, IL-8, and inducible NO synthetase (iNOS) was found to be decreased in AD, as compared with psoriasis, skin (HBD-2, p = 0.00021; IL-8, p = 0.044; iNOS, p = 0.016). Decreased expression of the novel antimicrobial peptide, HBD-3, was demonstrated at the mRNA level by real-time PCR (p = 0.0002) and at the protein level by immunohistochemistry (p = 0.0005). By real-time PCR, our data confirmed that AD, as compared with psoriasis, is associated with elevated skin production of Th2 cytokines and low levels of proinflammatory cytokines such as TNF-α, IFN-γ, and IL-1β. Because HBD-2, IL-8, and iNOS are known to be inhibited by Th2 cytokines, we examined the effects of IL-4 and IL-13 on HBD-3 expression in keratinocyte culture in vitro. We found that IL-13 and IL-4 inhibited TNF-α- and IFN-γ-induced HBD-3 production. These studies indicate that decreased expression of a constellation of antimicrobial genes occurs as the result of local up-regulation of Th2 cytokines and the lack of elevated amounts of TNF-α and IFN-γ under inflammatory conditions in AD skin. These observations could explain the increased susceptibility of AD skin to microorganisms, and suggest a new fundamental rule that may explain the mechanism for frequent infection in other Th2 cytokine-mediated diseases.

Atopic dermatitis is a chronic inflammatory skin disease associated with cutaneous hyperreactivity to environmental triggers and is often the first step in the atopic march that results in asthma and allergic rhinitis. The clinical phenotype that characterizes atopic dermatitis is the product of interactions between susceptibility genes, the environment, defective skin barrier function, and immunologic responses. This review summarizes recent progress in our understanding of the pathophysiology of atopic dermatitis and the implications for new management strategies. Historical perspectiveAtopic dermatitis (AD) is a chronic inflammatory skin disease associated with cutaneous hyperreactivity to environmental triggers that are innocuous to normal nonatopic individuals (1). Although written descriptions of AD date back to the early 1800s, an objective laboratory test does not exist for AD. The diagnosis of AD is based on the following constellation of clinical findings: pruritus, facial and extensor eczema in infants and children, flexural eczema in adults, and chronicity of the dermatitis.AD usually presents during early infancy and childhood, but it can persist into or start in adulthood (2). The lifetime prevalence of AD is 10-20% in children and 1-3% in adults. Its prevalence has increased two-to threefold during the past three decades in industrialized countries but remains much lower in countries with predominantly rural or agricultural areas. Wide variations in prevalence have been observed within countries inhabited by groups with similar genetic backgrounds, suggesting that environmental factors play a critical role in determining expression of AD.A precise understanding of the mechanisms underlying AD is critical for development of more effective management strategies (Table 1). Various studies indicate that AD has a complex etiology, with activation of multiple immunologic and inflammatory pathways (3). The clinical phenotype that characterizes AD is the product of complex interactions among susceptibility genes, the host's environment, defects in skin barrier function, and systemic and local immunologic responses. An understanding of the relative role of these factors in the pathogenesis of AD has been made possible by a variety of approaches, including the analysis of cellular and cytokine gene expression in AD skin lesions in humans as well as gene knockout and transgenic mouse models of candidate genes in AD. The current review will summarize progress in our understanding of the pathophysiology of AD and implications for therapy. Atopy as a systemic diseaseSeveral observations suggest that AD is the cutaneous manifestation of a systemic disorder that also gives rise to asthma, food allergy, and allergic rhinitis (1, 2). These conditions are all characterized by elevated serum IgE levels and peripheral eosinophilia. AD is often the initial step in the so-called "atopic march," which leads to asthma and allergic rhinitis in the majority of afflicted patients. In experimental models of AD, the induction...

Background-Atopic dermatitis (AD) is a chronic inflammatory skin disease that is characterized by a defective skin barrier function. Recent studies have reported mutations of the skin barrier gene encoding filaggrin in a subset of patients with AD.

Atopic dermatitis is a chronic inflammatory skin disease associated with cutaneous hyperreactivity to environmental triggers and is often the first step in the atopic march that results in asthma and allergic rhinitis. The clinical phenotype that characterizes atopic dermatitis is the product of interactions between susceptibility genes, the environment, defective skin barrier function, and immunologic responses. This review summarizes recent progress in our understanding of the pathophysiology of atopic dermatitis and the implications for new management strategies. Historical perspectiveAtopic dermatitis (AD) is a chronic inflammatory skin disease associated with cutaneous hyperreactivity to environmental triggers that are innocuous to normal nonatopic individuals (1). Although written descriptions of AD date back to the early 1800s, an objective laboratory test does not exist for AD. The diagnosis of AD is based on the following constellation of clinical findings: pruritus, facial and extensor eczema in infants and children, flexural eczema in adults, and chronicity of the dermatitis.AD usually presents during early infancy and childhood, but it can persist into or start in adulthood (2). The lifetime prevalence of AD is 10-20% in children and 1-3% in adults. Its prevalence has increased two-to threefold during the past three decades in industrialized countries but remains much lower in countries with predominantly rural or agricultural areas. Wide variations in prevalence have been observed within countries inhabited by groups with similar genetic backgrounds, suggesting that environmental factors play a critical role in determining expression of AD.A precise understanding of the mechanisms underlying AD is critical for development of more effective management strategies (Table 1). Various studies indicate that AD has a complex etiology, with activation of multiple immunologic and inflammatory pathways (3). The clinical phenotype that characterizes AD is the product of complex interactions among susceptibility genes, the host's environment, defects in skin barrier function, and systemic and local immunologic responses. An understanding of the relative role of these factors in the pathogenesis of AD has been made possible by a variety of approaches, including the analysis of cellular and cytokine gene expression in AD skin lesions in humans as well as gene knockout and transgenic mouse models of candidate genes in AD. The current review will summarize progress in our understanding of the pathophysiology of AD and implications for therapy. Atopy as a systemic diseaseSeveral observations suggest that AD is the cutaneous manifestation of a systemic disorder that also gives rise to asthma, food allergy, and allergic rhinitis (1, 2). These conditions are all characterized by elevated serum IgE levels and peripheral eosinophilia. AD is often the initial step in the so-called "atopic march," which leads to asthma and allergic rhinitis in the majority of afflicted patients. In experimental models of AD, the induction...

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Rationale: The 2016 definitions of sepsis included the quick Sepsisrelated Organ Failure Assessment (qSOFA) score to identify highrisk patients outside the intensive care unit (ICU).Objectives: We sought to compare qSOFA with other commonly used early warning scores. . Using the highest non-ICU score of patients, >2 SIRS had a sensitivity of 91% and specificity of 13% for the composite outcome compared with 54% and 67% for qSOFA >2, 59% and 70% for MEWS >5, and 67% and 66% for NEWS >8, respectively. Most patients met >2 SIRS criteria 17 hours before the combined outcome compared with 5 hours for >2 and 17 hours for >1 qSOFA criteria.Conclusions: Commonly used early warning scores are more accurate than the qSOFA score for predicting death and ICU transfer in non-ICU patients. These results suggest that the qSOFA score should not replace general early warning scores when risk-stratifying patients with suspected infection.

Machine learning is used increasingly in clinical care to improve diagnosis, treatment selection, and health system efficiency. Because machine-learning models learn from historically collected data, populations that have experienced human and structural biases in the past—called protected groups—are vulnerable to harm by incorrect predictions or withholding of resources. This article describes how model design, biases in data, and the interactions of model predictions with clinicians and patients may exacerbate health care disparities. Rather than simply guarding against these harms passively, machine-learning systems should be used proactively to advance health equity. For that goal to be achieved, principles of distributive justice must be incorporated into model design, deployment, and evaluation. The article describes several technical implementations of distributive justice—specifically those that ensure equality in patient outcomes, performance, and resource allocation—and guides clinicians as to when they should prioritize each principle. Machine learning is providing increasingly sophisticated decision support and population-level monitoring, and it should encode principles of justice to ensure that models benefit all patients.