Dept. of Biomedical Informatics, Emory Universitybusiness
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Biomedical Informatics (BMI) is a multidisciplinary field that studies and pursues the effective use of biomedical and clinical data, through novel computational approaches, driven by efforts to improve diagnosis, clinical care, and human health.
The BMI department is working in the area of Big Data Analytics for Healthcare. We use our expertise in computer science and informatics by developing various enabling tools, technologies, and algorithms to solve specific biomedical and clinical applications. And in doing so help advance our understanding of disease and treatment, and also develop useful software and applications.
Members of the department work in a variety of areas that go from healthcare middleware such as security, cloud computing, high end computing and metadata management, to clinical information systems, clinically oriented image analysis and biomedical knowledge modeling. The driving applications for the various ongoing projects include cancer research, organ transplant, HIV, medical imaging, radiation therapy, and clinical data analytics.
All development work that is undertaken by the department is free and open-source.
- A Timeline Viewer of Patient Medical Records In this project, we will examine novel ways to create an environment to display a patient's medical records and medical history. Currently patient medical records are resemble ugly reciepts straight from the 80s. In this project, we will help develop a timeline view (as in facebook timeline) of a patients complete medical history. When completed, it should allow a patient to see their entire medical history as a timeline with medications, treatments etc.
- Customized XML to JSON & JSON to XML Conversion Utility The main aim of the project is to design and implement a library to allow conversion of XML data sets to JSON in a custom defined manner - with the help of user defined mappings. Round trip conversion - XML -> JSON -> XML will also be supported. The use case is that currently all the open source tools which perform this conversion do not allow mapping specific attributes and nodes. They transform the complete XML into a JSON without taking into consideration user specifications. In most cases only a small portion of the XML is required to be transformed( to specific JSON fields) - which can be easily specified by XPath and JSON Schema - thus the requirement of this utility. The library designed will be easy to use and have an extensible and simple Interface. The mappings file will be a JSON Schema file with the description tag for each element containing the XPath expression for the same. Only those elements which are required to be converted from XML will be specified by the user in the JSON Schema. Some limitations might arise as with just XPath and JSON Schema there might be ambiguities and anomalies in the conversion. I will be documenting all these and trying to rectify and reduce the limitations of this library. I have expanded more on the features of this library in the course of this proposal.
- High Performance CPU and GPU Microscopy Image Analysis Algorithms and Implementations Project aims to develop parallel version of image processing algorithms. Some algorithms already have parallel implementation in Nvidia Cuda technology and the goal is to port those algorithm to more general OpenCL framework. There is also need to develop parallel versions of algorithms that have only serial version at this moment.
- Large Scale Biomedical Data Integration This project will retrieve different format such as XML and JSON from different Data Service. The problem is that these dataset are stored in their various database systems and it hard to allow users to give a general query and return result join from these various dataset. The objective of this project and aim to achieve is to create an “integrated dataset” and provide user an interface such as Restful to issue query and return result in JSON, XML format.
- Proposal for Hadoop-GIS: Extending Hive with Spatial Queries With the rapid development of positioning technology and imaging technology, the scale of spatial data has shown to have a exponential growth. There are two major challenges for managing and querying these massive spatial data: the multi-dimensional structure of spatial data and the high computational complexity of spatial queries. The rapid growth of spatial data requires a fast query system. In this project, we will present Hadoop-GIS by extending Apache Hive, a MapReduce based data warehousing system with spatial query capabilities. The main goal of Hadoop-GIS is to develop a highly scalable, cost-effective, efficient and expressive integrated spatial query processing system for data and compute intensive spatial applications, that can take advantage of MapReduce running on commodity clusters.