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 diseases and side effects 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/35108802/) pubmed abstract:<br>
<i>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 <b>\<andsystem-candidate\></b> and seizures.</i>
<br>
<br>In this example <i>fever</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 diseases or side effects.<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 diseases or side effects.<br>
<br>

The optimal threshold value for the short names of diseases and side effects for the LABEL_1, was calculated using a gold standard (add link). It is<b> >= 0.9999943971633911</b>.<br>
<br>
The Mathew Correlation Coefficient of the model for the long names (\>= 15 symbols) is 0.990.<br>
The ROC AUC value of the model, calculated for the short names (\<\= 4 symbols) is 0.955.
{"tags": ["ANDDigest", "ANDSystem"], "extra_gated_fields": {"I agree to share my contact information": "checkbox"}, "widget": [{"text": "Early detection of Parkinson's disease through patient questionnaire and predictive modelling. Early detection of Parkinson's disease (PD) is important which can enable early initiation of therapeutic interventions and management strategies. However, methods for early detection still remain an unmet clinical need in <andsystem-candidate>. In this study, we use the Patient Questionnaire (PQ) portion from the widely used Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) to develop prediction models that can classify early PD from healthy normal using machine learning techniques that are becoming popular in biomedicine: logistic regression, random forests, boosted trees and support vector machine (SVM). We carried out both subject-wise and record-wise validation for evaluating the machine learning techniques. We observe that these techniques perform with high accuracy and high area under the ROC curve (both >95%) in classifying early PD from healthy normal. The logistic model demonstrated statistically significant fit to the data indicating its usefulness as a predictive model. It is inferred that these prediction models have the potential to aid clinicians in the diagnostic process by joining the items of a questionnaire through machine learning.", "example_title": "PD (PMID: 30342689)"}, {"text": "X-linked adrenoleukodystrophy is a frequent cause of idiopathic Addison's disease in young adult male patients. X-Linked adrenoleukodystrophy (ALD) is a genetic disease associated with demyelination of the central nervous system, adrenal insufficiency, and accumulation of very long chain fatty acids in tissue and body fluids. <andsystem-candidate> is due to mutation of a gene located in Xq28 that encodes a peroxisomal transporter protein of unknown function. The most common phenotype of ALD is the cerebral form (45%) that develops in boys between 5-12 yr. Adrenomyeloneuropathy (AMN) involves the spinal cord and peripheral nerves in young adults (35%). Adrenal insufficiency (Addisons disease) is frequently associated with <andsystem-candidate> or cerebral ALD and may remain the only clinical expression of ALD (8% of cases). The prevalence of ALD among adults with Addisons disease remains unknown. To evaluate this prevalence, we performed biochemical analysis of very long chain fatty acids in 14 male patients (age ranging from 12-45 yr at diagnosis) previously diagnosed as having primary idiopathic adrenocortical insufficiency. In 5 of 14 patients (35%), elevated plasma concentrations of very long chain fatty acids were detected. None of these patients had adrenocortical antibodies. By electrophysiological tests and magnetic resonance imaging it was determined that two patients had cerebral ALD, one had adrenomyeloneuropathy with cerebral involvement, and two had preclinical AMN. Our data support the hypothesis that ALD is a frequent cause of idiopathic Addisons disease in children and adults..", "example_title": "AMN (PMID: 15485686)"}]}
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 diseases and side effects 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>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 <b>\<andsystem-candidate\></b> and seizures.</i>
<br>
<br>In this example <i>fever</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 diseases or side effects.<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 diseases or side effects.<br>
<br>

The optimal threshold value for the short names of diseases and side effects for the LABEL_1, was calculated using a gold standard (add link). It is<b> >= 0.9999943971633911</b>.<br>
<br>
The Mathew Correlation Coefficient of the model for the long names (\>= 15 symbols) is 0.990.<br>
The ROC AUC value of the model, calculated for the short names (\<\= 4 symbols) is 0.955.
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 
