ArticlePloS one2015
Knowledge retrieval from PubMed abstracts and electronic medical records with the Multiple Sclerosis Ontology.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 30 citations in OpenAlex.
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- The Epilepsy Ontology: a community-based ontology tailored for semantic interoperability and text mining.Bioinformatics advances · 2023Article
- Ontology for Symptomatic Treatment of Multiple Sclerosis.Healthcare informatics research · 2022Article
- Ontology-based identification and prioritization of candidate drugs for epilepsy from literature.Journal of biomedical semantics · 2022Review
- STO: Stroke Ontology for Accelerating Translational Stroke Research.Neurology and therapy · 2021Article
- Model-Driven Decision Making in Multiple Sclerosis Research: Existing Works and Latest Trends.Patterns (New York, N.Y.) · 2020Review
- Biomedical Holistic Ontology for People with Rare Diseases.International journal of environmental research and public health · 2020Article
- Article
- Ontology-Driven Search and Triage: Design of a Web-Based Visual Interface for MEDLINE.JMIR medical informatics · 2017Article
- Neuroimaging Feature Terminology: A Controlled Terminology for the Annotation of Brain Imaging Features.Journal of Alzheimer's disease : JAD · 2017Article
- Analysis of molecular networks and targets mining of Chinese herbal medicines on anti-aging.BMC complementary and alternative medicine · 2016Article
- The Pre-Eclampsia Ontology: A Disease Ontology Representing the Domain Knowledge Specific to Pre-Eclampsia.PloS one · 2016Article
- Bioinformatics Mining and Modeling Methods for the Identification of Disease Mechanisms in Neurodegenerative Disorders.International journal of molecular sciences · 2015Review
- Correction: Knowledge retrieval from PubMed abstracts and electronic medical records with the multiple sclerosis ontology.PloS one · 2015Article
Corrections and comments
- Erratum issued
- Erratum issuedMartinez-Lapsicina, Elena H [corrected to Martinez-Lapiscina, Elena H]
Authors and funding
15 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn order to retrieve useful information from scientific literature and electronic medical records (EMR) we developed an ontology specific for Multiple Sclerosis (MS).
methodsThe MS Ontology was created using scientific literature and expert review under the Protégé OWL environment. We developed a dictionary with semantic synonyms and translations to different languages for mining EMR. The MS Ontology was integrated with other ontologies and dictionaries (diseases/comorbidities, gene/protein, pathways, drug) into the text-mining tool SCAIView. We analyzed the EMRs from 624 patients with MS using the MS ontology dictionary in order to identify drug usage and comorbidities in MS. Testing competency questions and functional evaluation using F statistics further validated the usefulness of MS ontology.
resultsValidation of the lexicalized ontology by means of named entity recognition-based methods showed an adequate performance (F score = 0.73). The MS Ontology retrieved 80% of the genes associated with MS from scientific abstracts and identified additional pathways targeted by approved disease-modifying drugs (e.g. apoptosis pathways associated with mitoxantrone, rituximab and fingolimod). The analysis of the EMR from patients with MS identified current usage of disease modifying drugs and symptomatic therapy as well as comorbidities, which are in agreement with recent reports.
conclusionThe MS Ontology provides a semantic framework that is able to automatically extract information from both scientific literature and EMR from patients with MS, revealing new pathogenesis insights as well as new clinical information.
Indexed as
Identifiers
What OpenQuestion holds
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.