ArticleBrain sciences2021
Bayesian Network as a Decision Tool for Predicting ALS Disease.
Article in Brain sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 22 citations in OpenAlex.
- Construction of a prediction model for the early efficacy of 577-nm-SML in treating CSC based on Bayesian network.BMC ophthalmology · 2026Article
- A Bayesian network for identifying causes of breathlessness using a national electronic medical records (EMR) database.Scientific reports · 2026Article
- Artificial intelligence in pediatric allergy research.European journal of pediatrics · 2024Review
- Improving prediction models of amyotrophic lateral sclerosis (ALS) using polygenic, pre-existing conditions, and survey-based risk scores in the UK Biobank.Journal of neurology · 2024Article
- Improving prediction models of amyotrophic lateral sclerosis (ALS) using polygenic, pre-existing conditions, and survey-based risk scores in the UK Biobank.medRxiv : the preprint server for health sciences · 2024Article
- Quantitative association between lead exposure and amyotrophic lateral sclerosis: a Bayesian network-based predictive study.Environmental health : a global access science source · 2024Article
- A Bayesian network perspective on neonatal pneumonia in pregnant women with diabetes mellitus.BMC medical research methodology · 2023Article
- Applied Bayesian Approaches for Research in Motor Neuron Disease.Frontiers in neurology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Clinical diagnosis of amyotrophic lateral sclerosis (ALS) is difficult in the early period. But blood tests are less time consuming and low cost methods compared to other methods for the diagnosis. The ALS researchers have been used machine learning methods to predict the genetic architecture of disease. In this study we take advantages of Bayesian networks and machine learning methods to predict the ALS patients with blood plasma protein level and independent personal features. According to the comparison results, Bayesian Networks produced best results with accuracy (0.887), area under the curve (AUC) (0.970) and other comparison metrics. We confirmed that sex and age are effective variables on the ALS. In addition, we found that the probability of onset involvement in the ALS patients is very high. Also, a person's other chronic or neurological diseases are associated with the ALS disease. Finally, we confirmed that the Parkin level may also have an effect on the ALS disease. While this protein is at very low levels in Parkinson's patients, it is higher in the ALS patients than all control groups.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.