Internet of Things, data management for healthcare applications. Ontology and automatic classifications

Transcription

1 Internet of Things, data management for healthcare applications. Ontology and automatic classifications SAS Institute Norway

2 Different challenges same opportunities! Data capture in value chain Information available across value chains Share same understanding of data and content Globalization and universal access to information Empower analytics for right time decision making

3 Do we find the IoT in the strategic plans for Norwegian Healthcare?

4 Main objectives in the strategic plans for Norwegian Healthcare! Improve quality and interactions joint goals in healthcare and care sector Overall System of concepts to support continuity of care Improve healthcare and care sector through information technology

5 Improvement areas in the strategic plans for Norwegian Healthcare! Diffuse management model The organization is not adapt for interaction Large variation on productivity Private healthcare is more effective Weak quality measures Increasing proportion of unstructured data SOURCE:

6 How can IoT potentially fit into the strategic plans? Value / Cost efficiency RFID areas Logistics for patients and staff Logistics for equipment and supplies Security systems Tracing and tracing objects Tracing and tracing patients Maintenance and implementation Improve quality and interactions joint goals in healthcare and care sector Overall System of concepts to support continuity of care Improve healthcare and care sector through information technology Diffuse management model The organization is not adapt for interaction Large variation on productivity Private healthcare is more effective Weak quality measures Increasing proportion of unstructured data Today 2013

7 Some challenges and possible data management minefields Value / Cost efficiency Right to privacy Diffuse management model The organization is not adapt for interaction Large variation on productivity Private healthcare is more effective Weak quality measures Increasing proportion of unstructured data RFID areas Logistics for patients and staff Logistics for equipment and supplies Security systems Tracing and tracing objects Tracing and tracing patients Maintenance and implementation RFID tag prize pr. unit Improve quality and interactions joint goals in healthcare and care sector Overall System of concepts to support continuity of care Improve healthcare and care sector through information technology Lack of standardization Today 2013

8 Deviation from plan The data management challenge - interoperability Transactions + Org. unit A Org. unit B Org. unit C Org. unit D Org. unit E Electronic Health Record systems Theoretical progress - Staff data Patient data Equipment Maintenance Same format Mutual understanding of content Supply Activities

9 Interoperability Operational data Knowledge and Quality Data Key dimensions of interoperability Knowledge and skills Business processes and value chain ICT, data, applications and communication Semantic, definitions and insight Value creation through interoperability Semantics Business processes ICT Knowledge SERES, The register of semantic for electronic collaboration SEMICOLON, ICT-based methods, tools and metrics for semantic and organizational interoperability

10 The interoperability challenge related to data management Operational data Knowledge and Quality Data Data integrations Data feeds

11 The interoperability challenge related to applications Data feeds Enterprise End-user Application Automated or semiautomatic data feeds Decision processes Anaesthesia system Surgery planning Diagnostic Pain therapy Manually data entry Automatic data capure Work processes Surgery Transportation Medical treatment Births Databases did not bring data into structure 80% of data in healthcare is unstructured Unstructured data is increasing 60 % per. year

12 Text Analytics Information Organization and Access Predictive Modeling, Discover Trends and Patterns Content Categorization Ontology Management Text Mining Sentiment Analysis

13 Categorization Determine topics / subject area(s) of a particular document Example Relevance Why accessing a previous patioent in the Electronic Health Record systems? Associate rules to a category Example Reversing treatment D-vitamin is indicator for wrong medical treatment for diagnosis group Statistical or Rule based definition of topics Example Professional area Only above P20 is relevant for knowledge building Rule based types: Linguistic or Boolean Example Category matches if the sum of weights of terms exceeds certain threshold

14 Content Categorization, Entities Extraction, Fact and Event Extraction Automatic Categorization Map documents to one or more topics according to a taxonomy Taxonomy Management Design, test and development of a set of topics (taxonomy) Design automatic categorization rules Collaboration allowing several knowledge experts to work together Entities Extraction Find entities in text: people, location, companies, Fact and Event Extraction Extraction of relations between entities

15 Categorization Testing Multiple document formats supported (TXT, PDF, XML, HTML, RTF, etc.) Test documents are used to verify the performance of a rule Well performing rule will match all of the relevant test documents (recall) while not matching irrelevant documents (precision) Results are PASS/FAIL Fail: Document is NOT part of this group Documents Categorizer Pass: Document is part of this group

16 Analysis of Unstructured Data Integration of Text Mining and Content Categorization Enterprise Content SAS Text Miner Automated Discovery of Text Structure Content with Metadata SAS Enterprise Content Categorization Expert-based Refinement of Metadata Content with optimized Metadata SAS Enterprise Content Categorization SAS Text Miner Enterprise Content Expert-based Definition of Metadata Content with Metadata Automated Discovery of Text Structure with additional Metadata Content with optimized Metadata

17 SAS Ontology Management Build semantic repositories to manage companywide thesauri, vocabularies, and build relationships between them Create structure for integration with structured data and Contextual Analysis Maintain metadata across repositories and databases and to automatically tag documents according to the defined taxonomies Simplify the task of obtaining and returning knowledge from input documents

18 SAS Ontology Management Enables collaborative ontology development and maintenance Integrates existing document repository assets Identifies relationships between document repositories Build subject-matter expertise into search-and-retrieval activities Consistently applies subject-matter expertise across document repositories in real time Centralizes administration for collaborative ontology development

19 Examples Classification of Electronic Health Record data Rescue team and alert planning Detecting unauthorized access to patient data

20 Bringing information together Search and Summarization Electronic Health Record systems (EPJ)

21 Example Taxonomy

22 A study of Location of Rescue Teams, RT optimization

23 i I Open i LocationsOpen A Real time study on location data and demands Problem Formulation and Solving Objective Function: Minimize the maximum distance between stations and areas subject to: Max Distance Definition: define the largest distance (1) MaxDist Distance ij X ij Staffed Constraint: total stations with ambulances must equal the number available to open (2) Σ i Open i = Number of Ambulances Service Constraint: Stations that cover an area must have an ambulance assigned to them (3) X i j Open i for all i, j Cover Constraint: sum of coverage must meet demand (4) Σ i X ij = Demand j Supply Constraint: sum of coverage must not exceed supply (5) Σ j X ij Supply i Open i

24 Pattern recognition Detect unauthorized access to data in Electronic Health Record systems (EPJ), Association analysis Clustering MBR (K-nearest neighbors) Link analyses Dynamic building rules based on classification, profiling and white lists Access logs and EHR are analyzed through scenarios and scoring process Data intergartion Investigation Transformation and algoritmes Detection and scoring Desicion making Analyses White lists and scenarios

25 Test on Wk0001

26 Solving data management IoT enables a potential for value creation! Ability to define a hierarchical taxonomy where related topics are grouped together (Identify enterprise and structure according to ISO/CEN) Implement automatically classifies of documents using customizable rules for precise categorization new material to existing text sources (increase with 60%/year) Establish knowledge services (SOA) that extract, discover and predict knowledge from multiple text documents (i.e. including epicrisises contents can be added to structured data) Automated or semiautomatic data feeds Cluster documents, i.e. Electronic Health Record systems (EPJ), into related groups for descriptive or predictive modeling for operational risk analysis or performance monitoring of HF s and transparency between RHF s Ability for maintain ontology in enterprise content repositories and databases. The ontology can become the key element to integrate the Clinical Decision Support system with the new National health registries Manually data entry Automatic data capture Enable new services for semantic terms that are used to organize previously disassociated and isolated text repositories i.e. data from other specialized systems (e.g. at Ullevål there are more then 200 small special systems) Establish an enterprise semantic model for Norwegian healthcare that creates and maintains consistent and centralized metadata across all structured and non-strucured data collections (ref. Samspill 2.0 and Gode helseregistre bedre helse? )

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