The Health Ontology Mapper (HOM) method is a proposed solution to the semantic gap problem. The HOM Method provides the following functionality to enable the scalable deployment of informatics systems involving data from multiple health systems. The HOM method allows a relatively small population of biomedical ontology experts to describe the interpretation and analysis of biomedical information collected at thousands of hospitals via a cloud based terminology server. As such the HOM Method is focused on the scalability of the human talent required for successful informatics projects. The HOM promotes a means of converting UML based medical data into OWL format via a cloud-based method of controlling the data loading process. HOM subscribes to a means of converting data into a HIPAA Limited Data Set format to lower the risk associated with developing large virtual data repositories. HOM also provides a means of allowing access to medical data over grid computing environments by translating all information via a centralized web-based terminology server technology.
An integrated data repository (IDR) containing aggregations of clinical, biomedical, economic, administrative, and public health data is a key component of research infrastructure, quality improvement and decision support. But most available medical data is encoded using standard data warehouse architecture that employs arbitrary data encoding standards, making queries across disparate repositories difficult. In response to these shortcomings the Health Ontology Mapper (HOM) translates terminologies into formal data encoding standards without altering the underlying source data. The HOM method promotes inter-institutional data sharing and research collaboration, and will ultimately lower the barrier to developing and using an IDR.
|Commitee:||Avrin, David, Cohen, Maurice, Cucina, Russ, Hudson, Donna, Sim, Ida, Sun, Yao|
|School:||University of California, San Francisco|
|Department:||Biological and Medical Informatics|
|School Location:||United States -- California|
|Source:||DAI-B 74/11(E), Dissertation Abstracts International|
|Subjects:||Bioinformatics, Health care management|
|Keywords:||Health ontology mapping, Interoperability, Semantic gap, Statistics, Warehouse|
Copyright in each Dissertation and Thesis is retained by the author. All Rights Reserved
The supplemental file or files you are about to download were provided to ProQuest by the author as part of a
dissertation or thesis. The supplemental files are provided "AS IS" without warranty. ProQuest is not responsible for the
content, format or impact on the supplemental file(s) on our system. in some cases, the file type may be unknown or
may be a .exe file. We recommend caution as you open such files.
Copyright of the original materials contained in the supplemental file is retained by the author and your access to the
supplemental files is subject to the ProQuest Terms and Conditions of use.
Depending on the size of the file(s) you are downloading, the system may take some time to download them. Please be