Identification of Patients with Family History of Pancreatic Cancer--Investigation of an NLP System Portability.
In this study we have developed a rule-based natural language processing (NLP) system to identify patients with family history of pancreatic cancer. The algorithm was developed in a Unstructured Information Management Architecture (UIMA) framework and consisted of section segmentation, relation discovery, and negation detection. The system was evaluated on data from two institutions. The family history identification precision was consistent across the institutions shifting from 88.9% on Indiana University (IU) dataset to 87.8% on Mayo Clinic dataset. Customizing the algorithm on the the Mayo Clinic data, increased its precision to 88.1%. The family member relation discovery achieved precision, recall, and F-measure of 75.3%, 91.6% and 82.6% respectively. Negation detection resulted in precision of 99.1%. The results show that rule-based NLP approaches for specific information extraction tasks are portable across institutions; however customization of the algorithm on the new dataset improves its performance.
Department of Health Sciences Research, Mayo Clinic, Rochester, MN.
Saeed
S
Mehrabi
School of Informatics and Computing, Indiana University, Indianapolis, IN.
Anand
A
Krishnan
Department of Surgery, Indiana University, Indianapolis, IN.
Alexandra M
AM
Roch
Department of Surgery, Indiana University, Indianapolis, IN.
Heidi
H
Schmidt
Department of Health Sciences Research, Mayo Clinic, Rochester, MN.
DingCheng
D
Li
Regenstrief Institute Inc., Indianapolis, IN.
Joe
J
Kesterson
Regenstrief Institute Inc., Indianapolis, IN.
Chris
C
Beesley
Regenstrief Institute Inc., Indianapolis, IN.
Paul
P
Dexter
Department of Surgery, Indiana University, Indianapolis, IN.
Max
M
Schmidt
School of Informatics and Computing, Indiana University, Indianapolis, IN.
Mathew
M
Palakal
Department of Health Sciences Research, Mayo Clinic, Rochester, MN.
Hongfang
H
Liu
D000465:Algorithms; D057286:Electronic Health Records / Q000145:classification*; D020022:Genetic Predisposition to Disease / Q000453:epidemiology / Q000235:genetics; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D008487:Medical History Taking / Q000379:methods* / Q000706:statistics & numerical data; D008498:Medical Record Linkage; D009323:Natural Language Processing; D010190:Pancreatic Neoplasms / Q000175:diagnosis* / Q000453:epidemiology / Q000235:genetics*