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Viewpoint Paper: Identifying Patient Smoking Status from Medical Discharge Records
TLDR
A Natural Language Processing (NLP) challenge on automatically determining the smoking status of patients from information found in their discharge records and analysis of the results highlighted the fact that discharge summaries express smoking status using a limited number of textual features.
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Three Approaches to Automatic Assignment of ICD-9-CM Codes to Radiology Reports
TLDR
A simple hand-crafted rule-based system with lexical elements and semantic information can outperform algorithmically more complex systems when these systems base their ICD-9-CM predictions only upon individual words, n-grams, or s grams.
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Challenges in Synthesizing Surrogate PHI in Narrative EMRs
TLDR
This chapter discusses the challenges associated with generating realistic surrogates and describes the algorithms used to prepare the 2014 i2b2/UTHealth shared task corpus for distribution and use in a natural language processing task focused on de-identification.
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Specializing for predicting obesity and its co-morbidities
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Does negation really matter?
TLDR
It is concluded that due to the nature of the machine learning classifiers, information on the polarity of phrase-level assertions does not improve performance on data in a multilabel document-level classification task.
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Role of preferred terminology in the classification of medical reports
TLDR
This work explores the role of the Unified Medical Language System Metathesaurus preferred terms in multi-label classification of medical reports and shows that in the data, some reports can be correctly classified not only in the absence of preferred terms, but in some cases, in the presence of direct references to the diseases themselves.
View on IEEE
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