عنوان
پدید آورنده
موضوع
رده
کتابخانه
محل استقرار
TLpq2276872361
انگلیسی
Automated Disease-drug Ontology Generation Framework Powered by Linked Biomedical Ontologies
[Thesis]
Alobaidi, Mazen
Malik, Khalid
Oakland University
2019
125
Ph.D.
Oakland University
2019
The exponential growth of unstructured data available in the biomedical literature and electronic health records requires powerful technologies and novel architectures. The success of smart healthcare applications in clinical decision support, disease diagnosis, and healthcare management depends on knowledge representation that is interpretable by machines in order to infer new knowledge from. To this end, ontological data models are expected to play a pivotal role in organizing, integrating, and representing knowledge machines can understand and act upon. Unfortunately, constructing such models using non-automated means can be prohibitively time-consuming for both domain experts and ontology engineers, thereby limiting the scale and/or the scope of the required ontological models.
Computer science
Information science
Alobaidi, Mazen
Malik, Khalid
مطالعه متن کتاب p
[Thesis]
276903
a
Y
