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In our previous study, the text similarity-based information retrieval search engine eTBLAST [1] was tuned with the MEDLINE abstract dataset [2] to create Déjà vu, a publicly available database of over 70,000 highly similar biomedical citations [3].
Given the similarity to chart review data for the majority of ICD-9 and ICD-10 coded comorbidities, we applied the coding algorithms to hospital discharge abstract dataset.
This is considered encouraging, given that the quality of systems has consistently improved in successive STs (comparing results on the GE abstract dataset in 2009 and 2011), but also because the output quality can be fairly stably maintained when variations occur in text type, bio-medical subdomain and event types.
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This eagerness to place our faith in highly abstract datasets over messy reality has encouraged an elite cadre of brilliant thinkers to drive our economy right into the ditch.
Finally, The Worldesignersobotic Bartendescribeere.
Routinely collected administrative databases, hospital discharge abstract datasets in particular, will be used to calculate patient outcomes.
In addition to conducting a patient survey, hospital discharge abstract datasets will be used to calculate additional patient outcomes like in-hospital mortality and failure-to-rescue.
In this study, the minimum number of abstracts required to train a model with satisfactory performance was determined by training models using 2 to approximately 100 abstracts from dataset A. The remaining abstracts in dataset A were used as the testing set.
The reference indicated for each study was either a published manuscript, a protocol, an abstract or unpublished dataset as indicated.
Abstract: Large biological datasets are currently available, and their analysis has applications to basic science and medicine.
LOS and total cost for each hospitalization were abstracted from the dataset as directly supplied by the hospital.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com