Numerical Atrribute Extraction from Clinical Texts
  This paper describes about information extraction system, which is an
extension of the system developed by team Hitachi for "Disease/Disorder
Template filling" task organized by ShARe/CLEF eHealth Evolution Lab 2014. In
this extension module we focus on extraction of numerical attributes and values
from discharge summary records and associating correct relation between
attributes and values. We solve the problem in two steps. First step is
extraction of numerical attributes and values, which is developed as a Named
Entity Recognition (NER) model using Stanford NLP libraries. Second step is
correctly associating the attributes to values, which is developed as a
relation extraction module in Apache cTAKES framework. We integrated Stanford
NER model as cTAKES pipeline component and used in relation extraction module.
Conditional Random Field (CRF) algorithm is used for NER and Support Vector
Machines (SVM) for relation extraction. For attribute value relation
extraction, we observe 95% accuracy using NER alone and combined accuracy of
87% with NER and SVM.
