AIMC Topic: 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...
Databases, Genetic Disease Molecular Sequence Annotation Gene Ontology Computational Biology
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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...
Computational Biology Proteins Humans Machine Learning Disease Drug Therapy Knowledge Discovery Drug Repositioning 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...
Periodicals as Topic Publications Data Mining Humans Machine Learning Disease Databases as Topic Biological Ontologies
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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 ...
Gene Ontology Protein Interaction Mapping Disease Molecular Sequence Annotation Semantics
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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...
Support Vector Machine Data Mining Disease Humans
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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...
Support Vector Machine Natural Language Processing Data Mining Disease Terminology as Topic Animals Humans Models, Theoretical Neural Networks, Computer
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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...
Disease Internet Biological Ontologies Humans
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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 ...
Phenotype Electronic Health Records Supervised Machine Learning Disease Humans
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Text mining electronic hospital records to automatically classify admissions against disease: Measuring the impact of linking data sources.

Journal of biomedical informatics Oct 11, 2016
OBJECTIVE: Text and data mining play an important role in obtaining insights from Health and Hospital Information Systems. This paper presents a text mining system for detecting admissions marked as positive for several diseases: Lung Cancer, Breast ...
Patient Compliance Data Mining Hospital Records Support Vector Machine Disease Natural Language Processing Humans Hospitalization
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TaggerOne: joint named entity recognition and normalization with semi-Markov Models.

Bioinformatics (Oxford, England) Jun 9, 2016
MOTIVATION: Text mining is increasingly used to manage the accelerating pace of the biomedical literature. Many text mining applications depend on accurate named entity recognition (NER) and normalization (grounding). While high performing machine le...
Software Disease Forecasting Pattern Recognition, Automated Terminology as Topic Machine Learning Data Mining Computational Biology Information Storage and Retrieval Knowledge Bases Semantics
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