On‐label and off‐label prescribing patterns of erythropoiesis‐stimulating agents in inpatient hospital settings in the US during the period of major regulatory changes
Sign Up to like & getrecommendations! Published in 2017 at "Research in Social and Administrative Pharmacy"
DOI: 10.1016/j.sapharm.2016.07.005
Abstract: Background: A number of policy and labeling interventions aimed at reducing inappropriate prescribing of erythropoiesis‐stimulating agents (ESAs) were implemented in the U.S. between 2006 and 2010. These interventions included the addition of an FDA Black… read more here.
Keywords: label; use; period major; esa use ... See more keywords
An Empirical Study on Bugs in Python Interpreters
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Reliability"
DOI: 10.1109/tr.2022.3159812
Abstract: Python is an interpreted programming language that has been widely used in many fields. The successful execution of a Python program depends on both the correctness of Python program and the correctness of Python interpreter.… read more here.
Keywords: python interpreters; item label; item; bugs python ... See more keywords
DTranNER: biomedical named entity recognition with deep learning-based label-label transition model
Sign Up to like & getrecommendations! Published in 2020 at "BMC Bioinformatics"
DOI: 10.1186/s12859-020-3393-1
Abstract: Background Biomedical named-entity recognition (BioNER) is widely modeled with conditional random fields (CRF) by regarding it as a sequence labeling problem. The CRF-based methods yield structured outputs of labels by imposing connectivity between the labels.… read more here.
Keywords: transition model; label; deep learning; label label ... See more keywords