tags:
- ANDDigest
- ANDSystem
extra_gated_fields:
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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)'
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 tag.
Input:
Any biomedical text where a name of classified object is replaced with tag, for example, this pubmed abstract:
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.
In this example fever was replaced with <andsystem-candidate>. Please keep in mind that maximum length of input sequence for BERT is limited to 512 tokens.
Output:
LABEL_0 refers to the probability of the FALSE recognition, i.e. if the context of <andsystem-candidate> doesn't corresponds to the context specific for diseases or side effects.
LABEL_1 refers to the probability of the TRUE recognition, i.e. when the context of <andsystem-candidate> corresponds to the context specific for diseases or side effects.
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 >= 0.9999943971633911.
The Mathew Correlation Coefficient of the model for the long names (>= 15 symbols) is 0.990.
The ROC AUC value of the model, calculated for the short names (<= 4 symbols) is 0.955.