Automated Disease-drug Ontology Generation Framework Powered by Linked Biomedical Ontologies
نام عام مواد
[Thesis]
نام نخستين پديدآور
Alobaidi, Mazen
نام ساير پديدآوران
Malik, Khalid
وضعیت نشر و پخش و غیره
نام ناشر، پخش کننده و غيره
Oakland University
تاریخ نشرو بخش و غیره
2019
يادداشت کلی
متن يادداشت
125 p.
یادداشتهای مربوط به پایان نامه ها
جزئيات پايان نامه و نوع درجه آن
Ph.D.
کسي که مدرک را اعطا کرده
Oakland University
امتياز متن
2019
یادداشتهای مربوط به خلاصه یا چکیده
متن يادداشت
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.
اصطلاحهای موضوعی کنترل نشده
اصطلاح موضوعی
Computer science
اصطلاح موضوعی
Information science
نام شخص به منزله سر شناسه - (مسئولیت معنوی درجه اول )