Numerical Atrribute Extraction from Clinical Texts

TL;DR

This paper presents a system for extracting numerical attributes and their values from clinical texts, combining NER and relation extraction techniques to improve accuracy in medical information retrieval.

Contribution

It introduces an integrated approach using Stanford NER and SVM-based relation extraction within cTAKES for improved attribute-value pairing in clinical documents.

Findings

NER achieves 95% accuracy in attribute extraction

Relation extraction with SVM achieves 87% accuracy

System enhances clinical information extraction processes

Abstract

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.