TY - JOUR
T1 - Semantic characteristics of NLP-extracted concepts in clinical notes vs. biomedical literature.
AU - Wu, Stephen
AU - Liu, Hongfang
N1 - Copyright:
This record is sourced from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
PY - 2011
Y1 - 2011
N2 - Natural language processing (NLP) has become crucial in unlocking information stored in free text, from both clinical notes and biomedical literature. Clinical notes convey clinical information related to individual patient health care, while biomedical literature communicates scientific findings. This work focuses on semantic characterization of texts at an enterprise scale, comparing and contrasting the two domains and their NLP approaches. We analyzed the empirical distributional characteristics of NLP-discovered named entities in Mayo Clinic clinical notes from 2001-2010, and in the 2011 MetaMapped Medline Baseline. We give qualitative and quantitative measures of domain similarity and point to the feasibility of transferring resources and techniques. An important by-product for this study is the development of a weighted ontology for each domain, which gives distributional semantic information that may be used to improve NLP applications.
AB - Natural language processing (NLP) has become crucial in unlocking information stored in free text, from both clinical notes and biomedical literature. Clinical notes convey clinical information related to individual patient health care, while biomedical literature communicates scientific findings. This work focuses on semantic characterization of texts at an enterprise scale, comparing and contrasting the two domains and their NLP approaches. We analyzed the empirical distributional characteristics of NLP-discovered named entities in Mayo Clinic clinical notes from 2001-2010, and in the 2011 MetaMapped Medline Baseline. We give qualitative and quantitative measures of domain similarity and point to the feasibility of transferring resources and techniques. An important by-product for this study is the development of a weighted ontology for each domain, which gives distributional semantic information that may be used to improve NLP applications.
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M3 - Article
C2 - 22195220
AN - SCOPUS:84874219801
VL - 2011
SP - 1550
EP - 1558
JO - AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
JF - AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
SN - 1559-4076
ER -