
This model is a fine-tuned model of [BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://arxiv.org/abs/2007.15779) ([hugging-face card](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext)). The current model was developed for the web-based [ANDDigest](https://anddigest.sysbio.ru/) system for the classification of the short names of drugs and metabolites in texts on the basis of their context (the name considered to be short if it\'s length is 4 symbols or less). The analyzed name should be replaced in text with <andsystem-candidate> tag.<br>
<br>
<b>Input:</b><br>
Any biomedical text where a name of classified object is replaced with <andsystem-candidate> tag, for example, [this](https://pubmed.ncbi.nlm.nih.gov/18475230/) pubmed abstract:<br>
<i>Intermittent obstruction of jejunostomy tube due to Ascaris lumbricoides infection. A 45-year-old Costa Rican woman was seen for a jejunostomy tube malfunction. There was no evidence of tube malposition or intestinal obstruction. During endoscopy, a long worm was retrieved from the distal duodenum; it was later confirmed to be Ascaris lumbricoides. After treatment with <b>\<andsystem\-candidate\></b>, no further episodes of tube occlusion were observed. This case reminds us of the importance of considering helminthic infections and their atypical manifestations in patients from endemic regions.</i>
<br>
<br>In this example <i>mebendazole</i> was replaced with <i>\<andsystem\-candidate\></i>. Please keep in mind that maximum length of input sequence for BERT is limited to 512 tokens.
<br>
<b>Output:</b><br>
<i>LABEL_0</i> refers to the probability of the <i>FALSE</i> recognition, i.e. if the context of \<andsystem\-candidate\> doesn't corresponds to the context specific for drugs or metabolites.<br>
<i>LABEL_1</i> refers to the probability of the <i>TRUE</i> recognition, i.e. when the context of \<andsystem\-candidate\> corresponds to the context specific for drugs or metabolites.<br>
<br>

The optimal threshold value for the short names of drugs or metabolites for the LABEL_1, was calculated using a gold standard (add link). It is<b> >= 0.999992847442627</b>.<br>
<br>
The Mathew Correlation Coefficient of the model for the long names (\>= 15 symbols) is 0.983.<br>
The ROC AUC value of the model, calculated for the short names (\<\= 4 symbols) is 0.907.
{"tags": ["ANDDigest", "ANDSystem"], "extra_gated_fields": {"I agree to share my contact information": "checkbox"}, "widget": [{"text": "Intermittent obstruction of jejunostomy tube due to Ascaris lumbricoides infection. A 45-year-old Costa Rican woman was seen for a jejunostomy tube malfunction. There was no evidence of tube malposition or intestinal obstruction. During endoscopy, a long worm was retrieved from the distal duodenum; it was later confirmed to be Ascaris lumbricoides. After treatment with <andsystem-candidate>, no further episodes of tube occlusion were observed. This case reminds us of the importance of considering helminthic infections and their atypical manifestations in patients from endemic regions.", "example_title": "mebendazole"}, {"text": "Neurobrucellosis Presenting with Features of Demyelinating Disorder in a Pediatric Patient. Brucellosis is an endemic disease in Saudi Arabia, which can present with variable clinical manifestations. It is a zoonotic disease transmitted from animals to humans. Brucellosis is a multisystemic disease that can present with any system involvement; And neurobrucellosis is a serious complication, sometimes leading to permanent neurological deficit, if treatment is not started promptly. Herein, we present a 6-year boy with neurobrucellosis, who developed demyelination of cerebral white matter and presented with <andsystem-candidate> and seizures.", "example_title": "fever"}]}
transformers pytorch bert text-classification ANDDigest ANDSystem arxiv:2007.15779 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #ANDDigest #ANDSystem #arxiv-2007.15779 #autotrain_compatible #endpoints_compatible #region-us 


This model is a fine-tuned model of BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext (hugging-face card). The current model was developed for the web-based ANDDigest system for the classification of the short names of drugs and metabolites in texts on the basis of their context (the name considered to be short if it\'s length is 4 symbols or less). The analyzed name should be replaced in text with <andsystem-candidate> tag.<br>
<br>
<b>Input:</b><br>
Any biomedical text where a name of classified object is replaced with <andsystem-candidate> tag, for example, this pubmed abstract:<br>
<i>Intermittent obstruction of jejunostomy tube due to Ascaris lumbricoides infection. A 45-year-old Costa Rican woman was seen for a jejunostomy tube malfunction. There was no evidence of tube malposition or intestinal obstruction. During endoscopy, a long worm was retrieved from the distal duodenum; it was later confirmed to be Ascaris lumbricoides. After treatment with <b>\<andsystem\-candidate\></b>, no further episodes of tube occlusion were observed. This case reminds us of the importance of considering helminthic infections and their atypical manifestations in patients from endemic regions.</i>
<br>
<br>In this example <i>mebendazole</i> was replaced with <i>\<andsystem\-candidate\></i>. Please keep in mind that maximum length of input sequence for BERT is limited to 512 tokens.
<br>
<b>Output:</b><br>
<i>LABEL_0</i> refers to the probability of the <i>FALSE</i> recognition, i.e. if the context of \<andsystem\-candidate\> doesn't corresponds to the context specific for drugs or metabolites.<br>
<i>LABEL_1</i> refers to the probability of the <i>TRUE</i> recognition, i.e. when the context of \<andsystem\-candidate\> corresponds to the context specific for drugs or metabolites.<br>
<br>

The optimal threshold value for the short names of drugs or metabolites for the LABEL_1, was calculated using a gold standard (add link). It is<b> >= 0.999992847442627</b>.<br>
<br>
The Mathew Correlation Coefficient of the model for the long names (\>= 15 symbols) is 0.983.<br>
The ROC AUC value of the model, calculated for the short names (\<\= 4 symbols) is 0.907.
TAGS
#transformers #pytorch #bert #text-classification #ANDDigest #ANDSystem #arxiv-2007.15779 #autotrain_compatible #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #bert #text-classification #ANDDigest #ANDSystem #arxiv-2007.15779 #autotrain_compatible #endpoints_compatible #region-us 
