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S PA a Platform for Hereditary Disease Management and P edigree A nalytics Rizzoli FOAK

S PA a Platform for Hereditary Disease Management and P edigree A nalytics Rizzoli FOAK. Alex Melament, melament@il.ibm.com July 2010. IOR - Rizzoli Orthopaedic Institute. IOR- is the main Italian institute of orthopedics and has a status of a 'Scientific research hospital’

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S PA a Platform for Hereditary Disease Management and P edigree A nalytics Rizzoli FOAK

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  1. SPAa Platform for Hereditary Disease Management and Pedigree AnalyticsRizzoli FOAK Alex Melament, melament@il.ibm.com July 2010

  2. IOR - Rizzoli Orthopaedic Institute • IOR- is the main Italian institute of orthopedics and has a status of a 'Scientific research hospital’ • About 150,000 patients examined every year • Over 18,000 orthopedic operations every year • Nine laboratories at the institute employing a staff of 250 including doctors, biologists and technicians. • Medical Genetic Unit specializes in Rare Skeletal Hereditary diseases such as: • Multiple Osteochondromas (MO) • Osteochondroma is a cartilage capped bony projection arising onthe external surface of bone containing a marrow cavity that is continuous with that of the underlying bone • Osteogenesis Imperfecta (OI) • It is frequently caused by defect in the gene that produces type 1 collagen, an important building block of bone. There are many different defects that can affect this gene.

  3. Project Goals • Understanding the relationships between hereditary diseases and their genetic background • Analysis of inherited diseases and their associated phenotypes is of great importance to gain knowledge of underlying genetic interactions • Discovering and defining a correlation between Phenotype and Genotype data will enable • Fit adequate treatment protocol • Ensure appropriate clinical follow-up • Improve patient’s quality of life • Build Healthcare Platform that will enable efficient treatment and productive research in Hereditary Diseases

  4. IT for understanding of Hereditary Diseases • Collects and Integrates medical images, clinical and genomic data of each patient and his family • Leverages standards such as DICOM and HL7 • Maintains content and context aware associations to support clinical and research usage • Enables secure cross Hospital data and knowledge sharing • Leverages Industry’s Best Practices, Standards and IHE Profiles • Enables cross Hospital Patient ID correlation through PIX&PDQ interfaces • Enables data insights discovery • Supports federated queries • Provides on demand pedigree visualization • Provides a platform for data analytics & knowledge extraction • Enables to host third party analytics

  5. Pedigree Analysis • The pedigree documents biological relationships in families and the presence of diseases. • Pedigree includes number, gender and closeness of affected relatives, their ages at disease onset, and associated health conditions • Pedigree is needed to • Assess disease risk • BRCARPO risk model can indicate a chance of having BRCA1 or BRCA2 mutations • Investigate correlation between Phenotype and Genotype data

  6. Solution Architecture Hospitals (PACS, Clinical and Genetic Labs) Healthcare BUS (WAS7/WPS 6.1, HL7 v2&3, WS, IHE profiles) EHR, Knowledge Systems Physicians and Researchers DICOM WS WS HL7 v3, v2.x Pedigree Analytics Cognos Excel BO CMO Cross River/Rimon(Phenotype,Genotype) Data Federation Cube Services & IBM BI Infrastructure + WAS 7 WAS 7, DB2 Data Warehouse WAS 7, DB2 Data Warehouse WAS 7, IBM CM PIX/PDQ Server WAS 7 Partners (Disease risk assessment models, Pedigree clustering, classification and visualization tools)

  7. Pedigree Visualization • Dynamic pedigree visualization • Presentation of all available information for the persons in the pedigree • Clinical, genomic data and medical images • Standard pedigree representation • HL7 v3 Family History • Enables standard based pedigree interoperability • Enables disease risk assessment

  8. On Demand Analytics Platform – Currently Available Algorithms • Decision Trees – Explanation of one dimension by others; Clustering • Random ID3 • C4.5 (entropy based) • Bayesian Networks – Data Cleansing • Naive Bayes • Chu-Liu Trees • K-Means – Clustering • Hamming distance for category dimensions only • Continuous dimensions only • Discretization of Continuous dimensions by Entropy and application of Hamming distance on all the dimensions. • Statistical Analysis • Chi-Square – Association between two category dimensions • Spearman – Association between two category dimensions

  9. CMO – SOA based Medical Imaging Repository Imaging Archive Image Processing Image Consumers • Supports DICOM, WADO, HL7, XDS-I, PIX and PDQ • Provides secure cross-enterprise sharing • Complies with regulations for storing and managing data • Enables extensibility and reuse of existing legacy assets through an open and flexible architecture • Leverages IBM's market-proven middleware to ensure scalability, high availability and disaster recovery Data Analytics Fine-Grained Authorization Data Sources (CT, MRI, US…, PACS)

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