Matzenhamilton2842

Recruitment of health research participants through social media is becoming more common. In the United States, 80% of adults use at least one social media platform. Social media platforms may allow researchers to reach potential participants efficiently. However, online research methods may be associated with unique threats to sample validity and data integrity. Limited research has described issues of data quality and authenticity associated with the recruitment of health research participants through social media, and sources of low-quality and fraudulent data in this context are poorly understood.

The goal of the research was to describe and explain threats to sample validity and data integrity following recruitment of health research participants through social media and summarize recommended strategies to mitigate these threats. Our experience designing and implementing a research study using social media recruitment and online data collection serves as a case study.

Using published strategies to ity.

With the increasing incidences and mortality of digestive system tumor diseases in China, ways to use clinical experience data in Chinese electronic medical records (CEMRs) to determine potentially effective relationships between diagnosis and treatment have become a priority. As an important part of artificial intelligence, a knowledge graph is a powerful tool for information processing and knowledge organization that provides an ideal means to solve this problem.

This study aimed to construct a semantic-driven digestive system tumor knowledge graph (DSTKG) to represent the knowledge in CEMRs with fine granularity and semantics.

This paper focuses on the knowledge graph schema and semantic relationships that were the main challenges for constructing a Chinese tumor knowledge graph. The DSTKG was developed through a multistep procedure. As an initial step, a complete DSTKG construction framework based on CEMRs was proposed. Then, this research built a knowledge graph schema containing 7 classes and 16 kon of a tumor knowledge graph and provide a preliminary step for the intelligent application of knowledge graphs based on CEMRs. Additional data sources and stronger research on assertion classification are needed to gain insight into the DSTKG's potential.

We constructed a granular semantic DSTKG. It could provide guidance for the construction of a tumor knowledge graph and provide a preliminary step for the intelligent application of knowledge graphs based on CEMRs. Additional data sources and stronger research on assertion classification are needed to gain insight into the DSTKG's potential.

Community-based agriculture has been found to decrease food insecurity and alleviate health inequities. CC-90011 supplier Furthermore, it provides a sense of ownership, resources to help integrate new communities, and a space to nurture existing cultural identities for intersectionally diverse gardeners. This sense of belonging in connection with access to growing plots has been linked to psychological well-being and resilience. However, little is known about how the psychosocial benefits of plot ownership affect resilience and which aspects of this resilience are salient.

This community-based participatory research (CBPR) project will examine the role of community gardens in decreasing food insecurity and facilitating various forms of resilience in food-insecure groups in Rochester, Minnesota. Since participation in community gardens nurtures various forms of resilience along individual, group, and community dimensions, our research seeks to understand how dimensions of resilience vary along intersectional lines. In additmmunity garden; this will assist others in planning and implementing similar projects.

PRR1-10.2196/21218.

PRR1-10.2196/21218.

Home hospitalization is widely accepted as a cost-effective alternative to conventional hospitalization for selected patients. A recent analysis of the home hospitalization and early discharge (HH/ED) program at Hospital Clínic de Barcelona over a 10-year period demonstrated high levels of acceptance by patients and professionals, as well as health value-based generation at the provider and health-system levels. However, health risk assessment was identified as an unmet need with the potential to enhance clinical decision making.

The objective of this study is to generate and assess predictive models of mortality and in-hospital admission at entry and at HH/ED discharge.

Predictive modeling of mortality and in-hospital admission was done in 2 different scenarios at entry into the HH/ED program and at discharge, from January 2009 to December 2015. Multisource predictive variables, including standard clinical data, patients' functional features, and population health risk assessment, were considered.

Wet HH discharge.

The introduction of next-generation sequencing (NGS) into molecular cancer diagnostics has led to an increase in the data available for the identification and evaluation of driver mutations and for defining personalized cancer treatment regimens. The meaningful combination of omics data, ie, pathogenic gene variants and alterations with other patient data, to understand the full picture of malignancy has been challenging.

This study describes the implementation of a system capable of processing, analyzing, and subsequently combining NGS data with other clinical patient data for analysis within and across institutions.

On the basis of the already existing NGS analysis workflows for the identification of malignant gene variants at the Institute of Pathology of the University Hospital Erlangen, we defined basic requirements on an NGS processing and analysis pipeline and implemented a pipeline based on the GEMINI (GEnome MINIng) open source genetic variation database. For the purpose of validation, this pipy, we demonstrate the feasibility of the pipeline integration into hospital workflows by providing an exemplary integration into the data integration center infrastructure, which is currently being established across Germany.

The German Network on Primary Immunodeficiency Diseases (PID-NET) utilizes the European Society for Immunodeficiencies (ESID) registry as a platform for collecting data. In the context of PID-NET data, we show how registries based on custom software can be made interoperable for better collaborative access to precollected data. The Open Source Registry System for Rare Diseases (Open-Source-Registersystem für Seltene Erkrankungen [OSSE], in German) provides patient organizations, physicians, scientists, and other parties with open source software for the creation of patient registries. In addition, the necessary interoperability between different registries based on the OSSE, as well as existing registries, is supported, which allows those registries to be confederated at both the national and international levels.

Data from the PID-NET registry should be made available in an interoperable manner without losing data sovereignty by extending the existing custom software of the registry using the OSSE registry framework.