Non-adherence to disease-modifying antirheumatic drugs is associated with higher disease activity in early arthritis patients in the first year of the disease

Introduction: Non-adherence to disease-modifying antirheumatic drugs (DMARDs) hampers the targets of rheumatoid arthritis (RA) treatment, obtaining low disease activity and decreasing radiological progression. This study investigates if, and to what extent, non-adherence to treatment would lead to a higher 28-

Die Modellierungskonzepte von Wirtschaftssystemen

Es werden im Folgenden einige Modellierungskonzepte erläutert, die man gemeinsam als systemdynamische Modellbildung bezeichnet. Zu diesem Bereich gehören folgende Modellierungsansätze: System-Dynamics-Verfahren, FLOR-Modellierung, Regelkreisoptimierungsmodelle und Dynamisches Fortschreibungsmodell. Wann immer man versucht, ein Wirtschaftssystem zu analysieren und in Differenzial- bzw. Differenzengleichungen abzubilden, ist systemdynamische Modellierung ein gangbarer Weg. Sie basiert explizit auf der soft-modeling Simulationsmethode, die die wirtschaftlichen Reproduktionsprozesse im globalen Rahmen abzubilden und in ihrer dynamischen Interaktion zu interpretieren erfordert

Components of healthcare costs for early arthritis.

<p>Components of healthcare costs for early arthritis.</p

Demographic and disease characteristics and adherence percentages.

<p>Demographic and disease characteristics and adherence percentages.</p

Association between costs and adherence percentage.

<p>Association between costs and adherence percentage.</p

Multivariable linear regression analysis of possible predictors of costs per cost category.

<p>Multivariable linear regression analysis of possible predictors of costs per cost category.</p

Percentage distribution of costs categories for patients 80% or more adherent and patients less than 80% adherent.

<p>Percentage distribution of costs categories for patients 80% or more adherent and patients less than 80% adherent.</p

Flowchart of respondents.

<p>Flowchart of respondents.</p

The scalable precision medicine open knowledge engine (SPOKE): a massive knowledge graph of biomedical information

MotivationKnowledge graphs (KGs) are being adopted in industry, commerce and academia. Biomedical KG presents a challenge due to the complexity, size and heterogeneity of the underlying information.ResultsIn this work, we present the Scalable Precision Medicine Open Knowledge Engine (SPOKE), a biomedical KG connecting millions of concepts via semantically meaningful relationships. SPOKE contains 27 million nodes of 21 different types and 53 million edges of 55 types downloaded from 41 databases. The graph is built on the framework of 11 ontologies that maintain its structure, enable mappings and facilitate navigation. SPOKE is built weekly by python scripts which download each resource, check for integrity and completeness, and then create a 'parent table' of nodes and edges. Graph queries are translated by a REST API and users can submit searches directly via an API or a graphical user interface. Conclusions/Significance: SPOKE enables the integration of seemingly disparate information to support precision medicine efforts.Availability and implementationThe SPOKE neighborhood explorer is available at https://spoke.rbvi.ucsf.edu.Supplementary informationSupplementary data are available at Bioinformatics online