Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Medicine and Health Sciences
Ontology-Based Clinical Information Extraction Using Snomed Ct, Jun Li
Ontology-Based Clinical Information Extraction Using Snomed Ct, Jun Li
Dissertations & Theses (Open Access)
Extracting and encoding clinical information captured in unstructured clinical documents with standard medical terminologies is vital to enable secondary use of clinical data from practice. SNOMED CT is the most comprehensive medical ontology with broad types of concepts and detailed relationships and it has been widely used for many clinical applications. However, few studies have investigated the use of SNOMED CT in clinical information extraction.
In this dissertation research, we developed a fine-grained information model based on the SNOMED CT and built novel information extraction systems to recognize clinical entities and identify their relations, as well as to encode them …
Characterizing The Information Needs Of Rural Healthcare Practitioners With Language Agnostic Automated Text Analysis, Melissa Resnick
Characterizing The Information Needs Of Rural Healthcare Practitioners With Language Agnostic Automated Text Analysis, Melissa Resnick
Dissertations & Theses (Open Access)
Objectives – Previous research has characterized urban healthcare providers' information needs, using various qualitative methods. However, little is known about the needs of rural primary care practitioners in Brazil. Communication exchanged during tele-consultations presents a unique data source for the study of these information needs. In this study, I characterize rural healthcare providers' information needs expressed electronically, using automated methods.
Methods – I applied automated methods to categorize messages obtained from the telehealth system from two regions in Brazil. A subset of these messages, annotated with top-level categories in the DeCS terminology (the regional equivalent of MeSH), was used to …