Difference between revisions of "Template:Article of the week"

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'''"[[Journal:Promoting data sharing among Indonesian scientists: A proposal of a generic university-level research data management plan (RDMP)|Promoting data sharing among Indonesian scientists: A proposal of a generic university-level research data management plan (RDMP)]]"'''
'''"[[Journal:Data to diagnosis in global health: A 3P approach|Data to diagnosis in global health: A 3P approach]]"'''


Every researcher needs data in their working ecosystem, but despite the resources (funding, time, and energy) they have spent to get the data, only a few are putting more real attention into [[Information management|data management]]. This paper mainly describes our recommendation of a research data management plan (RDMP) at the university level. This paper is an extension of our initiative, to be developed at the university or national level, while also in-line with current developments in scientific practices mandating data sharing and data re-use. Researchers can use this article as an assessment form to describe the setting of their research and data management. Researchers can also develop a more detailed RDMP to cater to a specific project's environment. ('''[[Journal:Promoting data sharing among Indonesian scientists: A proposal of a generic university-level research data management plan (RDMP)|Full article...]]''')<br />
With connected medical devices fast becoming ubiquitous in healthcare monitoring, there is a deluge of data coming from multiple body-attached sensors. Transforming this flood of data into effective and efficient diagnosis is a major challenge. To address this challenge, we present a "3P" approach: personalized patient monitoring, precision diagnostics, and preventive criticality alerts. In a collaborative work with doctors, we present the design, development, and testing of a healthcare data analytics and communication framework that we call RASPRO (Rapid Active Summarization for effective PROgnosis). The heart of RASPRO is "physician assist filters" (PAF) that 1. transform unwieldy multi-sensor time series data into summarized patient/disease-specific trends in steps of progressive precision as demanded by the doctor for a patient’s personalized condition, and 2. help in identifying and subsequently predictively alerting the onset of critical conditions. ('''[[Journal:Data to diagnosis in global health: A 3P approach|Full article...]]''')<br />
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Revision as of 15:25, 18 March 2019

Fig9 Pathinarupothi BMCMedInfoDecMak2018 18.png

"Data to diagnosis in global health: A 3P approach"

With connected medical devices fast becoming ubiquitous in healthcare monitoring, there is a deluge of data coming from multiple body-attached sensors. Transforming this flood of data into effective and efficient diagnosis is a major challenge. To address this challenge, we present a "3P" approach: personalized patient monitoring, precision diagnostics, and preventive criticality alerts. In a collaborative work with doctors, we present the design, development, and testing of a healthcare data analytics and communication framework that we call RASPRO (Rapid Active Summarization for effective PROgnosis). The heart of RASPRO is "physician assist filters" (PAF) that 1. transform unwieldy multi-sensor time series data into summarized patient/disease-specific trends in steps of progressive precision as demanded by the doctor for a patient’s personalized condition, and 2. help in identifying and subsequently predictively alerting the onset of critical conditions. (Full article...)

Recently featured:

▪ Building a newborn screening information management system from theory to practice
▪ Adapting data management education to support clinical research projects in an academic medical center
▪ Development of an electronic information system for the management of laboratory data of tuberculosis and atypical mycobacteria at the Pasteur Institute in Côte d’Ivoire
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