A Frequency-based Strategy of Obtaining Sentences from Clinical Data Repository for Crowdsourcing.
In clinical NLP, one major barrier to adopting crowdsourcing for NLP annotation is the issue of confidentiality for protected health information (PHI) in clinical narratives. In this paper, we investigated the use of a frequency-based approach to extract sentences without PHI. Our approach is based on the assumption that sentences appearing frequently tend to contain no PHI. Both manual and automatic evaluations on 500 sentences out of the 7.9 million sentences of frequencies higher than one show that no PHI can be found among them. The promising results provide potentials of releasing those sentences for obtaining sentence-level NLP annotations via crowdsourcing.
Mayo Clinic, Rochester, MN, USA.
Dingcheng
D
Li
Mayo Clinic, Rochester, MN, USA.
Majid
M
Rastegar Mojarad
Mayo Clinic, Rochester, MN, USA.
Yanpeng
Y
Li
Mayo Clinic, Rochester, MN, USA.
Sunghwan
S
Sohn
Mayo Clinic, Rochester, MN, USA.
Saeed
S
Mehrabi
Mayo Clinic, Rochester, MN, USA.
Ravikumar
R
Komandur Elayavilli
Mayo Clinic, Rochester, MN, USA.
Yue
Y
Yu
Mayo Clinic, Rochester, MN, USA.
Hongfang
H
Liu
D063045:Crowdsourcing / Q000379:methods*; D003627:Data Interpretation, Statistical; D057286:Electronic Health Records / Q000145:classification*; D007802:Language; D000069550:Machine Learning; D008910:Minnesota; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods; D012660:Semantics; D009626:Terminology as Topic; D018875:Vocabulary, Controlled