@inproceedings{f31566c59c124d1492312b8e733298d5,
title = "When metamap meets social media in healthcare: Are the word labels correct?",
abstract = "Health forums have gained attention from researchers for studying various topics on healthcare. In many of these studies, identifying biomedical words by using the MetaMap is often a pre-processing step. MetaMap is a popular tool for recognizing Unified Medical Language System (UMLS) concepts in free text. However, MetaMap favors identifying terminologies used by professionals rather than laymen terms by the common users. The word labels given by MetaMap on social media may not be accurate, and may adversely affect the next level studies. In this study, we manually annotate the correctness of medical words extracted by MetaMap from 100 posts in HealthBoards and get a precision of 43.75%. We argue that directly applying MetaMap on social media data in healthcare may not be a good choice for identifying the medical words.",
keywords = "Healthcare, MetaMap, Social media, UMLS",
author = "Hongkui Tu and Zongyang Ma and Aixin Sun and Xiaodong Wang",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 12th Asia Information Retrieval Societies Conference, AIRS 2016 ; Conference date: 30-11-2016 Through 02-12-2016",
year = "2016",
doi = "10.1007/978-3-319-48051-0_31",
language = "English",
isbn = "9783319480503",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "356--362",
editor = "Yi Chang and Ji-Rong Wen and Zhicheng Dou and Xin Zhao and Shaoping Ma and Yiqun Liu and Min Zhang",
booktitle = "Information Retrieval Technology - 12th Asia Information Retrieval Societies Conference, AIRS 2016, Proceedings",
}