Mining free-text medical records.
Text mining projects can be characterized along four parameters: 1) the demands of the market in terms of target domain and specificity and depth of queries; 2) the volume and quality of text in the target domain; 3) the text mining process requirements; and 4) the quality assurance process that validates the extracted data. In this paper, we provide lessons learned and results from a large-scale commercial project using Natural Language Processing (NLP) for mining the transcriptions of dictated clinical records in a variety of medical specialties. We conclude that the current state-of-the-art in NLP is suitable for mining information of moderate content depth across a diverse collection of medical settings and specialties.
Heinze
D T
DT
A-Life Medical, Inc., San Diego, CA, USA.
Morsch
M L
ML
Holbrook
J
J
D000208:Acute Disease; D001249:Asthma; D005705:Gallbladder Diseases; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D016347:Medical Records Systems, Computerized / Q000706:statistics & numerical data; D009203:Myocardial Infarction; D009323:Natural Language Processing; D018875:Vocabulary, Controlled