TELKOMNIKA Telecommunication, Computing, Electronics and Control
Bigram feature extraction and conditional random fields model to improve text classification clinical trial document

TELKOMNIKA Telecommunication, Computing, Electronics and Control
Bigram feature extraction and conditional random fields model to improve text classification clinical trial document

Clinical trials
Conditional random fields
Deep learning
Text classification

In the field of health and medicine, there is a very important term known as
clinical trials. Clinical trials are a type of activity that studies how the safest
way to treat patients is. These clinical trials are usually written in unstructured
free text which requires translation from a computer. The aim of this paper is
to classify the texts of cancer clinical trial documents consisting of
unstructured free texts taken from cancer clinical trial protocols. The proposed
algorithm is conditional random Fields and bigram features. A new
classification model from the cancer clinical trial document text is proposed to
compete with other methods in terms of precision, recall, and f-1 score. The
results of this study are better than the previous results, namely 88.07
precision, 88.05 recall and f-1 score 88.06.

Jasmir Jasmir, Siti Nurmaini, Reza Firsandaya Malik, Bambang Tutuko

http://journal.uad.ac.id/index.php/TELKOMNIKA

Oct 29, 2020

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