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DNorm: Disease Named Entity Recognition and Normalization
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<h3>DNorm: Disease Named Entity Recognition and Normalization with Pairwise Learning to Rank</h3>
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<h4>Authors: <a href="https://scholar.google.com/citations?user=FLnUx4cAAAAJ" target="_blank">Robert
Leaman</a>, <a href="/CBBresearch/Fellows/Dogan/" target="_blank">Rezarta
Islamaj Dogan</a> and <a href="/bionlp/" target="_blank">Zhiyong Lu</a> (PI)</h4>
<h4>Research highlights (<a
href="/CBBresearch/Lu/Demo/tmTools/demo/DNorm/demo.cgi" target="_blank">demo</a>)
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<p>
DNorm is an automated method for determining which diseases are mentioned in biomedical text, the
task of disease normalization. Diseases have a central role in many lines of biomedical research,
making this task important for many lines of inquiry, including etiology (e.g. gene-disease
relationships) and clinical aspects (e.g. diagnosis, prevention, and treatment). DNorm is a
high-performing and mathematically principled framework for learning similarities between mentions
and concept names directly from training data. DNorm is the first technique to use machine learning
to normalize disease names and also the first method employing pairwise learning to rank in a
normalization task. DNorm achieved the best performance in the 2013 ShARe/CLEF shared task on
disease normalization in clinical notes.
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<h4>Method overview</h4>
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The technique consists of series of processing steps summarized in Figure 1 and described below.
</p>
<div class="figure">
<img src="/research/bionlp/static/main/images/tools/DNorm.png" width="350"/>
<span><b>Figure 1.</b> Processing pipeline diagram.</span>
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<h4>Results</h4>
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We evaluated the system on the NCBI Disease Corpus test set at the level of associations between the
disease concept and the abstract, not individual mentions.
</p>
<table class="customtable">
<tbody>
<tr>
<td align="center"><strong>Method</strong></td>
<td align="center"><strong>Precision</strong></td>
<td align="center"><strong>Recall</strong></td>
<td align="center"><strong>F-measure</strong></td>
</tr>
<tr>
<td align="center" nowrap="">NLM Lexical Normalization</td>
<td align="center">0.218</td>
<td align="center">0.685</td>
<td align="center">0.331</td>
</tr>
<tr>
<td align="center" nowrap="">MetaMap</td>
<td align="center">0.502</td>
<td align="center">0.665</td>
<td align="center">0.572</td>
</tr>
<tr>
<td align="center" nowrap="">Inference Method</td>
<td align="center">0.533</td>
<td align="center">0.662</td>
<td align="center">0.591</td>
</tr>
<tr>
<td align="center" nowrap="">BANNER + Lucene</td>
<td align="center">0.612</td>
<td align="center">0.647</td>
<td align="center">0.629</td>
</tr>
<tr>
<td align="center" nowrap="">BANNER + cosine similarity</td>
<td align="center">0.649</td>
<td align="center">0.674</td>
<td align="center">0.661</td>
</tr>
<tr>
<td align="center" class="best">DNorm (BANNER + pLTR)</td>
<td align="center" class="best">0.803</td>
<td align="center" class="best">0.763</td>
<td align="center" class="best">0.782</td>
</tr>
</tbody>
</table>
<span><b>Table 1.</b> Evaluation of DNorm against several baseline techniques, using micro-averaged precision, recall and F-measure.</span>
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<h4>Downloads</h4>
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<p>
<a href="/CBBresearch/Lu/Demo/tmTools/download/DNorm/DNorm-0.0.7.tgz"
target="_blank">DNorm Software</a><br/>
<a href="/CBBresearch/Dogan/DISEASE" target="_blank">NCBI Disease
Corpus</a><br/>
DNorm-tagged PubMed results in <a href="/CBBresearch/Lu/Demo/PubTator/"
target="_blank">PubTator</a><br/>
<a href="/research/bionlp/APIs/">DNorm
RESTful API</a>
</p>
</div>
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<h4>Please cite</h4>
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<ul class="dot-list">
<li>Robert Leaman, Rezarta Islamaj Dog
<CC><8C>an and Zhiyong Lu. <a
href="http://bioinformatics.oxfordjournals.org/content/early/2013/09/26/bioinformatics.btt474.full"
target="_blank">DNorm: Disease Name Normalization with Pairwise Learning to Rank</a>.
Bioinformatics (2013) 29 (22): 2909-2917, doi:10.1093/bioinformatics/btt474
</li>
<li>Robert Leaman, Ritu Khare and Zhiyong Lu. <a
href="http://ceur-ws.org/Vol-1179/CLEF2013wn-CLEFeHealth-LeamanEt2013.pdf" target="_blank">NCBI
at 2013 ShARe/CLEF eHealth Share Task: Disorder Normalization in Clinical Notes with DNorm</a>.
Working Notes of the Conference and Labs of the Evaluation Forum (2013)
</li>
<li>Robert Leaman and Zhiyong Lu. <a href="http://www.aclweb.org/anthology/W14-3404"
target="_blank">Automated Disease Normalization with Low Rank
Approximations</a>. Proceedings of BioNLP 2014: pp 24-28
</li>
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