Disease Template Filling using the CTAKES YTEX Branch for ShareClef 2014 Task 2a

نویسنده

  • John Osborne
چکیده

Using an adapted version of the YTEX branch of CTAKES for disease template filling accuracies of 0.936, 0.974, 0.807 and 0.926 were achieved for the conditional, generic, negation and subject class respectively in Task 2a. Overall accuracy was 0.79. Unfortunately substantially poorer performance in F1 score, precision and recall for all 4 of these templating tasks indicates that it is not yet possible to get good performance using these CTAKES algorithms in this task.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

TeamUEvora at Clef eHealth 2014 Task2a

We present our first participation in a ShARe/CLEF eHealth Lab contributing for task 2a. Task 2 is an extension of the 2013 lab task 1 and consists of information extraction from clinical texts for Disease/Disorder Template Filling; task 2a aims at predicting each attribute’s normalization value. This work constitutes a preliminary approach to the problem of extracting and handling information ...

متن کامل

Optimizing Apache cTAKES for Disease/Disorder Template Filling: Team HITACHI in the ShARe/CLEF 2014 eHealth Evaluation Lab

This paper describes an information extraction system developed by team Hitachi for “Disease/Disorder Template filling” task organized by ShARe/CLEF eHealth Evaluation Lab 2014. We approached the task by building a baseline system using Apache cTAKES. We submitted two separate runs; in our first run, rule based assertion module predict the norm slot value of assertion attributes excluding train...

متن کامل

CUAB: Supervised Learning of Disorders and their Attributes using Relations

We implemented an end-to-end system for disorder identification and slot filling. For identifying spans for both disorders and their attributes, we used a linear chain conditional random field (CRF) approach coupled with cTAKES for pre-processing. For combining disjoint disorder spans, finding relations between attributes and disorders, and attribute normalization, we used l2-regularized l2-los...

متن کامل

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...

متن کامل

The Yale cTAKES extensions for document classification: architecture and application

BACKGROUND Open-source clinical natural-language-processing (NLP) systems have lowered the barrier to the development of effective clinical document classification systems. Clinical natural-language-processing systems annotate the syntax and semantics of clinical text; however, feature extraction and representation for document classification pose technical challenges. METHODS The authors dev...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2014