Top Tweets for #sysrevblogs
1/2 #SysrevBlogs: "Extracting Data from Partially Structured Sources"
#PartiallyStructured documents, e.g. #JSON or #XML, are a great source of data but difficult to review. Put simply, the asymmetry of data b/w docs makes it harder to find relevant info.
https://t.co/phwov6sr3x
What is the point of extracting data if you don't do anything with it? This #SysrevBlogs analyzes the 3500 pieces of information produced by the Mangiferin #ManagedReview. Special consideration was given to complex (diverse) string labels.
https://t.co/uvknokvCEs #dataextraction
#SysrevBlogs: For those who prefer to read rather than listen/watch, our Mangiferin #ManagedReview is now available in #blog format. See how we used #Sysrev's #machinelearning to optimize #screening & #dataextraction
https://t.co/2iGXgaF4D3
#literaturereview #strategicreview
#SysrevBlogs: The diversity of string labels (191 unique #disease descriptions were extracted) make them the most complex to analyze.
#Textsimilarity can be used to cluster string labels for better analysis. E.g, the "diabetes milletus" cluster contained 13 unique descriptions.
#SysrevBlogs: "Generating Insights - analysis of data produced by the Mangiferin #ManagedReview." Sysrev facilitates three types of 'labels' - boolean, categorical, and string. Each label type requires different strategies for analysis.
https://t.co/b7lCM4gmES #dataanalysis
#SysrevBlogs: For those who prefer to read rather than listen/watch, our Mangiferin #ManagedReview is now available in #blog format. See how we used #Sysrev's #machinelearning to optimize #screening & #dataextraction
https://t.co/2iGXgaF4D3
#literaturereview #strategicreview
While more than capable of handling the rigor of a #systematicreview, #Sysrev excels when it’s #machinelearning capabilities can be used to their full potential - as shown here in this strategic, “Managed Review” on mangiferin.
https://t.co/X4thNagxeg #dataextraction
#SysrevBlogs: "Unsolved problems cannot be resolved with a simple google search. We need to bring back the careful research methods lauded in a time before the answer to every question was just a click away."
'Muting the Echo Chamber' by @tomlue.
https://t.co/0HfcYxzc9O #sysrev
I'm glad you appreciated the blog post! Check out our follow-up where we took the #NLP model and built https://t.co/SLTLLLQmgf
https://t.co/wbbekVuOMo #SysrevBlogs
How to extract genes from text with Sysrev and spaCy https://t.co/8VO0c6K1i1
This functionality was built with #machinelearning in mind. As this #SysrevBlogs post shows - we can create reviews that export "clean" #trainingdata. By supporting living reviews, we can even help ensure any training data is up to date. https://t.co/wbbekVuOMo
#SysrevBlogs: Sysrev Helps Create #ChemicalTransparency for Manufacturers by #SRG. https://t.co/CBxyQYZHVH
SRG assists its manufacturing clients manage #ChemicalsofConcern. SRG uses #Sysrev to manage #dataextraction tasks.
For #academics, #HEOR and #RWE consultants, or any other professional who still uses excel for their reviews. #SysrevBlogs: Why are you still using #excel for #dataextraction? Hint: you shouldn't be...
https://t.co/IFRrbgrAFI
#literaturereview #documentreview #systematicreview
#SysrevBlogs: Founder Tom Luechtefeld @tomlue discusses Sysrev's current and future #machinelearning capabilities. https://t.co/5Zt5TM5Uwl #systematicreview #livingreview
#SysrevBlogs: #Phamacovigilance means continuous streams of many varieties of data. Each source must be evaluated in as near as real-time as possible. #LivingReviews, an adaptation of #systematicreviews, are one solution to manage this influx of data. https://t.co/F78N5eGrf7
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