Dataset Information

Knowledge retrieval from PubMed abstracts and electronic medical records with the Multiple Sclerosis Ontology.

ABSTRACT:

Background

In order to retrieve useful information from scientific literature and electronic medical records (EMR) we developed an ontology specific for Multiple Sclerosis (MS).

Methods

The 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.

Results

Validation 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.

Conclusion

The 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.

SUBMITTER: Malhotra A

PROVIDER: S-EPMC4321837 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Publications

Knowledge retrieval from PubMed abstracts and electronic medical records with the Multiple Sclerosis Ontology.

Malhotra Ashutosh A Gündel Michaela M Rajput Abdul Mateen AM Mevissen Heinz-Theodor HT Saiz Albert A Pastor Xavier X Lozano-Rubi Raimundo R Martinez-Lapiscina Elena H EH Zubizarreta Irati I Mueller Bernd B Kotelnikova Ekaterina E Toldo Luca L Hofmann-Apitius Martin M Villoslada Pablo P

PloS one 20150209 2

<h4>Background</h4>In order to retrieve useful information from scientific literature and electronic medical records (EMR) we developed an ontology specific for Multiple Sclerosis (MS).<h4>Methods</h4>The 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/comor ...[more]

PMID: 25665127

Similar Datasets

Project description:ObjectiveTo optimally leverage the scalability and unique features of the electronic health records (EHR) for research that would ultimately improve patient care, we need to accurately identify patients and extract clinically meaningful measures. Using multiple sclerosis (MS) as a proof of principle, we showcased how to leverage routinely collected EHR data to identify patients with a complex neurological disorder and derive an important surrogate measure of disease severity heretofore only available in research settings.MethodsIn a cross-sectional observational study, 5,495 MS patients were identified from the EHR systems of two major referral hospitals using an algorithm that includes codified and narrative information extracted using natural language processing. In the subset of patients who receive neurological care at a MS Center where disease measures have been collected, we used routinely collected EHR data to extract two aggregate indicators of MS severity of clinical relevance multiple sclerosis severity score (MSSS) and brain parenchymal fraction (BPF, a measure of whole brain volume).ResultsThe EHR algorithm that identifies MS patients has an area under the curve of 0.958, 83% sensitivity, 92% positive predictive value, and 89% negative predictive value when a 95% specificity threshold is used. The correlation between EHR-derived and true MSSS has a mean R(2) = 0.38±0.05, and that between EHR-derived and true BPF has a mean R(2) = 0.22±0.08. To illustrate its clinical relevance, derived MSSS captures the expected difference in disease severity between relapsing-remitting and progressive MS patients after adjusting for sex, age of symptom onset and disease duration (p = 1.56×10(-12)).ConclusionIncorporation of sophisticated codified and narrative EHR data accurately identifies MS patients and provides estimation of a well-accepted indicator of MS severity that is widely used in research settings but not part of the routine medical records. Similar approaches could be applied to other complex neurological disorders.

Dataset Information

Knowledge retrieval from PubMed abstracts and electronic medical records with the Multiple Sclerosis Ontology.

Background

Methods

Results

Conclusion

Publications

Knowledge retrieval from PubMed abstracts and electronic medical records with the Multiple Sclerosis Ontology.

Similar Datasets

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