PONTE EU OPEN DAY 31 January-Cambridge

Transcription

1 BINET Business Intelligence for Social Network analysis, A novel data-mining platform using SNA and it s application in healthcare PONTE EU OPEN DAY 31 January-Cambridge Sabrina Molinaro Head of the Epidemiology and health Research Service -IFC National Research Council Italy

2 BINET aims to design a Business Intelligence technology framework using Social Network Analysis in the healthcare field, to establish a non-conventional graph analysis platform.

3 BINET FINAL GOAL - Assess the state of population health - Support clinical decisions - Support healthcare planning, cost analysis and policy making - Evaluate the accuracy and efficacy of prescriptions and treatments - Evaluate treatment outcome

4 What are we doing? The BINET research project aims to do studies in four main important areas involving all available healthcare data ( patients data fields, patient registries, prescription registries ) to accomplish: Modeling, Representation, Analysis Validation.

5 Modeling A system for assisted modeling of generic entities connected by heterogeneous relationships.

6 Representation, An environment for the representation of information and its navigation in order to optimize the accessibility of these entities and relationships through the use of dynamic graphs

7 Analysis A set of indices and algorithms for analyzing and extracting information from the social network.

8 Validation To validate the SNA model we need to. compare the results with classical statistical analyses P<0.001 ( d ) P<0.001 P<0.001 ( e ) Lifetime Adj Rate Male

9 A concrete example of application to health data

10 Information System Data from Italian Health Structures Patient Files Outpatient Drug prescriptions Deaths registry Addiction, Mental Health services Hospital Admissions Chronic diseases ticket Exemption

11 Pisa Region health structures EPID Dimension Data from Pisa health system area patients hospital admissions, 5 million diagnostic procedures, 10 million drug prescriptions Record Linkage, quality control

12 Diagnosis, drugs,health services, lab exams,. all records related to the same patient are linked together to build the Health state monitoring datawarehouse Information System

13 Data mart analysis The data-mart table: One row for each patient Many columns allinformation related to each patient Socio demographic information Health cost information Diagnostic information Drug prescription information Hospital admission information.

14 Epidemiological analysis P<0.001 ( d ) P<0.001 Standard analysis is concerned with formulation of a clear hypothesis to analyze P<0.001 ( e ) Lifetime Adj Rate Male

15 Social Network analysis Network analysis is concerned with formulation and solution of problems that have network structure usually captured in a graph. Nodes represent entities, Arcs shows relations between them

16 Our network is built of node/entities and relations (edge/link ) which are used to describe the structure OUR ANALYSIS Considers different relationships (prescription, time, costs ecc ), between the node (people, MD, drugs ) and aims to study a) who talks with whom b) those who ask for something from someone c) those within a functional structure d) who is in the company of those who are like someone else e) who depends on whom f) for those who are functional

17 Link analysis starting with people

18 Hospital admission diagnosis : IMA Drug prescribed: INSUMAN RAP OPT EV SC5CART 3ML Drug prescribed: HUMALOG SC 5CART 3ML 100U/ML Woman, 25 Y, Drug prescribed: Diazepam SC5CART 3ML Diagnostic category : diabetic-cardio

19 Link analysis starting with drugs Drug prescribed: Serotonin-norepinephrine reuptake inhibitors

20 Link analysis People who have had more then 15 prescriptions during 1 year Serotonin-norepinephrine reuptake inhibitors

21 People who have had more then 15 prescriptions during 1 year Serotonin-norepinephrine reuptake inhibitors

22 Link analysis Serotonin-norepinephrine reuptake inhibitors People who have had more then 15 prescriptions and their index analysis: CENTRALITY DEGREE CLOSENESS CENTRALITY BETWEENNESS CENTRALITY EIGENVECTOR CENTRALITY

23 Link analysis People who have had more then 15 prescriptions and their index analysis: And the description of their other drugs patterns

24 Graph Data Browser Framework BINET project research aims to deliver a framework oriented to link analysis and complex network analisys able to process large amount of data and analyze them from multiple perspectives: from maximum detail to higest abstraction level Database Graph Engine W e b S e r v i c e s The framework is modular and multilevel designed

25 Graph Data Browser Framework Modules functionalities Metadata hadler and phisical schema creation Load & simplified extraction from external data Content Metadata DataAnalisi Multifunctional Access Admin Visualizer Modeller ETL Search Graph Tables Interactive data access through graph or data table. Guided user interface System settings, user profiling, backups.

26 Conclusion Social Network Analysis provides a visualization approach to analyzing complex healthcare data and relationships and show the patterns of physician prescribing and patient pathologies in a comprehensive and more easily interpretable way than traditional statistical analysis

27 Next Steps To enable the addition of many kinds of data into the framework Create a interface to support uploading all available data To develop a common framework to address the needs of physicians for improving care, administration for improving performance and cost, and overall improvement of patient healthcare quality

28 BINET Business Intelligence for Social Network analisys, Acknowledgment Stefania Pieroni,IFC-CNR Fabio Mariani,IFC-CNR Ernesto Lastres,SISTER-srl Federica Sbrana,SISTER-srl Carlo Magnarapa,SISTER-srl Edoardo Bracci,SISTER-srl Filippo Geraci,IIT-CNR Marco Pellegrini,IIT - CNR Miriam Baglioni,IIT CNR

29 BINET Business Intelligence for Social Network analisys, Thank you for the attention