Note: <additional comments> Big Data) Requirements WG Use Case Template Aug 11 2013 NBD(NIST Use Case Title Individualized Diabetes Management Vertical (area) Healthcare Author/Company/Email Peter Li, Ying Ding, Philip Yu, Geoffrey Fox, David Wild at Mayo Clinic, Indiana University, UIC; dingying@indiana.edu Actors/Stakeholders and Mayo Clinic + IU/semantic integration of EHR data their roles and UIC/semantic graph mining of EHR data responsibilities IU cloud and parallel computing Goals Develop advanced graph-based data mining techniques applied to EHR to search for these cohorts and extract their EHR data for outcome evaluation. These methods will push the boundaries of scalability and data mining technologies and advance knowledge and practice in these areas as well as clinical management of complex diseases. Use Case Description Diabetes is a growing illness in world population, affecting both developing and developed countries. Current management strategies do not adequately take into account of individual patient profiles, such as co-morbidities and medications, which are common in patients with chronic illnesses. We propose to approach this shortcoming by identifying similar patients from a large Electronic Health Record (EHR) database, i.e. an individualized cohort, and their respective management outcomes to formulate one best solution suited for a given patient with diabetes. Project under development as below Stage 1: Use the Semantic Linking for Property Values method to convert an existing data warehouse at Mayo Clinic, called the Enterprise Data Trust (EDT), into RDF triples that enables us to find similar patients much more efficiently through linking of both vocabulary-based and continuous values, Needs efficient parallel retrieval algorithms, suitable for cloud Stage 2: or HPC, using open source Hbase with both indexed and custom search to identify patients of possible interest. Stage 3: The EHR, as an RDF graph, provides a very rich environment for graph pattern mining. Needs new distributed graph mining algorithms to perform pattern analysis and graph indexing technique for pattern searching on RDF triple graphs. Stage 4: Given the size and complexity of graphs, mining subgraph patterns could generate numerous false positives and miss numerous false negatives. Needs robust statistical analysis tools to manage false discovery rate and determine true subgraph significance and validate these through several clinical use cases. Current Compute(System) supercomputers; cloud Solutions Storage HDFS Networking Varies. Significant I/O intensive processing needed
Note: <additional comments> NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013 Use Case Title Individualized Diabetes Management Vertical (area) Healthcare Author/Company/Email Peter Li, Ying Ding, Philip Yu, Geoffrey Fox, David Wild at Mayo Clinic, Indiana University, UIC; dingying@indiana.edu Actors/Stakeholders and their roles and responsibilities Mayo Clinic + IU/semantic integration of EHR data UIC/semantic graph mining of EHR data IU cloud and parallel computing Goals Develop advanced graph-based data mining techniques applied to EHR to search for these cohorts and extract their EHR data for outcome evaluation. These methods will push the boundaries of scalability and data mining technologies and advance knowledge and practice in these areas as well as clinical management of complex diseases. Use Case Description Diabetes is a growing illness in world population, affecting both developing and developed countries. Current management strategies do not adequately take into account of individual patient profiles, such as co-morbidities and medications, which are common in patients with chronic illnesses. We propose to approach this shortcoming by identifying similar patients from a large Electronic Health Record (EHR) database, i.e. an individualized cohort, and their respective management outcomes to formulate one best solution suited for a given patient with diabetes. Project under development as below Stage 1: Use the Semantic Linking for Property Values method to convert an existing data warehouse at Mayo Clinic, called the Enterprise Data Trust (EDT), into RDF triples that enables us to find similar patients much more efficiently through linking of both vocabulary-based and continuous values, Stage 2: Needs efficient parallel retrieval algorithms, suitable for cloud or HPC, using open source Hbase with both indexed and custom search to identify patients of possible interest. Stage 3: The EHR, as an RDF graph, provides a very rich environment for graph pattern mining. Needs new distributed graph mining algorithms to perform pattern analysis and graph indexing technique for pattern searching on RDF triple graphs. Stage 4: Given the size and complexity of graphs, mining subgraph patterns could generate numerous false positives and miss numerous false negatives. Needs robust statistical analysis tools to manage false discovery rate and determine true subgraph significance and validate these through several clinical use cases. Current Solutions Compute(System) supercomputers; cloud Storage HDFS Networking Varies. Significant I/O intensive processing needed