AIMC Topic: Disease

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Learning a Health Knowledge Graph from Electronic Medical Records.

Scientific reports Jul 20, 2017
Demand for clinical decision support systems in medicine and self-diagnostic symptom checkers has substantially increased in recent years. Existing platforms rely on knowledge bases manually compiled through a labor-intensive process or automatically...
Humans Electronic Health Records Knowledge Bases Disease Adult Middle Aged
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Disease Compass- a navigation system for disease knowledge based on ontology and linked data techniques.

Journal of biomedical semantics Jun 19, 2017
BACKGROUND: Medical ontologies are expected to contribute to the effective use of medical information resources that store considerable amount of data. In this study, we focused on disease ontology because the complicated mechanisms of diseases are r...
Computer Graphics Semantic Web Humans Biological Ontologies Disease
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A disease similarity matrix based on the uniqueness of shared genes.

BMC medical genomics May 24, 2017
BACKGROUND: Complex diseases involve many genes, and these genes are often associated with several different illnesses. Disease similarity measurement can be based on shared genotype or phenotype. Quantifying relationships between genes can reveal pr...
Gene Ontology Disease Molecular Sequence Annotation Computational Biology Databases, Genetic
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Network mirroring for drug repositioning.

BMC medical informatics and decision making May 18, 2017
BACKGROUND: Although drug discoveries can provide meaningful insights and significant enhancements in pharmaceutical field, the longevity and cost that it takes can be extensive where the success rate is low. In order to circumvent the problem, there...
Humans Proteins Drug Repositioning Machine Learning Computational Biology Knowledge Discovery Disease Drug Therapy Data Mining
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Using classification models for the generation of disease-specific medications from biomedical literature and clinical data repository.

Journal of biomedical informatics Apr 20, 2017
OBJECTIVE: Mining disease-specific associations from existing knowledge resources can be useful for building disease-specific ontologies and supporting knowledge-based applications. Many association mining techniques have been exploited. However, the...
Biological Ontologies Disease Data Mining Databases as Topic Publications Humans Machine Learning Periodicals as Topic
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Evaluating the effect of annotation size on measures of semantic similarity.

Journal of biomedical semantics Feb 13, 2017
BACKGROUND: Ontologies are widely used as metadata in biological and biomedical datasets. Measures of semantic similarity utilize ontologies to determine how similar two entities annotated with classes from ontologies are, and semantic similarity is ...
Protein Interaction Mapping Molecular Sequence Annotation Semantics Disease Gene Ontology
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Disorder recognition in clinical texts using multi-label structured SVM.

BMC bioinformatics Jan 31, 2017
BACKGROUND: Information extraction in clinical texts enables medical workers to find out problems of patients faster as well as makes intelligent diagnosis possible in the future. There has been a lot of work about disorder mention recognition in cli...
Humans Support Vector Machine Data Mining Disease
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Disease named entity recognition by combining conditional random fields and bidirectional recurrent neural networks.

Database : the journal of biological databases and curation Oct 24, 2016
The recognition of disease and chemical named entities in scientific articles is a very important subtask in information extraction in the biomedical domain. Due to the diversity and complexity of disease names, the recognition of named entities of d...
Neural Networks, Computer Terminology as Topic Humans Models, Theoretical Animals Disease Natural Language Processing Support Vector Machine Data Mining
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Dione: An OWL representation of ICD-10-CM for classifying patients' diseases.

Journal of biomedical semantics Oct 13, 2016
BACKGROUND: Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT) has been designed as standard clinical terminology for annotating Electronic Health Records (EHRs). EHRs textual information is used to classify patients' diseases into an...
Humans Biological Ontologies Internet Disease
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Semi-supervised learning of the electronic health record for phenotype stratification.

Journal of biomedical informatics Oct 12, 2016
Patient interactions with health care providers result in entries to electronic health records (EHRs). EHRs were built for clinical and billing purposes but contain many data points about an individual. Mining these records provides opportunities to ...
Humans Phenotype Disease Electronic Health Records Supervised Machine Learning
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