adding value to big data in healthcare through future research initiatives
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Adding value to Big Data in Healthcare Through Future Research Initiatives. An IBM – NC State College of Management/CIMS Collaboration. How do we to transform data into Knowledge?. - PowerPoint PPT PresentationTRANSCRIPT
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Adding value to Big Data in Healthcare Through Future Research Initiatives
An IBM – NC State College of Management/CIMS Collaboration
1
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How do we to transform data into Knowledge?
Understand what concepts and relationships are important for your business – start small (prioritize) and then scale out.
Identify one or more data sources that may contribute to the model definition, such as SMEs, RDBs, and LODs.
Map the data into a flexible business ontology and enhance with lexical, syntactic, and semantic descriptions, as appropriate. Integrate (compile) the business ontology into your content analysis platform – concepts, relationships, and domain topology.
Understand
Harvest
Model
Integrate
BusinessIntegrate the content analysis service, and resulting knowledge repository, throughout your business to maximize the ROI realized by this new intelligence layer.
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Knowledge Maturity Model
3
Syntactic identification of
terms
Concepts defined, Capture context, assess sentiment, resolve references and identify
characteristics
Identify multi-level linkages, highlight asymmetry, classify (basic & deductive)
Derive knowledge,
Auto-refinement
Increasing Maturity
Entity Extraction
Disambiguation
Relationships & Inferencing
Semantic Reasoning
DictionaryThesaurusGlossary
ModelingTriples
Logic Engine
Taxonomies
Machine Learning
Business Value
Statistical Analysis
Ontologies
Inference Engine
Linked Data
Rules Engine
Rules,Heuristics
Visualization Capabilities Descriptive Analytics
Predictive Analytics
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Data Warehouse / Data Marts
Document Server(UIMA Pipeline)
REST WebService
Real-Time NLP
Search
Administration
Free Form Text
(Real-Tim
e NLP)
IBM
Con
tent
Ana
lytic
s ICA-LanguageWare Resource Workbench
ICA ExporterICA Crawlers
Apache Lucene Search Engine
ICA Text Miner
CustomApplications
Liberty Mutual
Applications
Business Analyst (ICA Text Miner)
Many others•••
IBM Master Data MgmtIBM Master Data Mgmt
Content Intelligence Consumers
Content Intelligence Consumers
•••
•••
ECM Case Management
Paper and Electronic Content Sources
Image Capture (OCR)
Enterprise Content Management Repository
Free Form Text
Relational Database
Paper Content
Web Content
Free Form
Text
Free Form Text
Progress Notes
Surgical Reports Pathology ReportsOperative Notes
Discharge NotesNuclear Medicine Reports
PubMed/Cochrane Web Content
Medical Records
Web Research DataCall Center Logs Customer Surveys
File ShareFree Form
Text
Claim Data
Electronic Content
4
IBM Content Analytics Components
Where we are today
Copy Right IBM 2011
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Where we are today
5
Facets
Instances of facets within documents
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Massively Parallel Probabilistic Architecture
Question/Topic
Analysis
Question
Hypothesis & Evidence Scoring
Answer, Confidence
Synthesis Final Merging& Ranking
QueryDecomposition
Hypothesis Generation
Hypothesis & Evidence Scoring
Soft Filtering
Hypothesis Generation
Hypothesis & Evidence Scoring
Soft Filtering
Hypothesis Generation
. . .
Trained Models
Primary Search
Candidate Answer
Generation
A. Sources SupportingEvidenceRetrieval
Deep Evidence Scoring
Answer Scoring
E. Sources
EvidenceRetrieval
DeepEvidenceScoring
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. . .
Where we are Going
Copy Right IBM 2011
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Where we are Going
7Copy Right IBM 2011
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Potential Watson-enabled Healthcare Solutions
8Watson for Healthcare | IBM Confidential 2-May-11
Patient Workup
Differential Diagnosis
Treatment Recommendation
Longitudinal Personalized Care
Specialty Research Genomic-based Analysis
Specialty Deep Diagnosis
Second Opinion
Pre-Authorization Treatment Analysis
Patients Caregivers Physicians
Healthcare Providers
Benefits Providers
Specialty Providers
Treatment Protocol AnalysisPatient
Inquiry
Ask Watson
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1. Visualization recommender Dynamically recommend/compose the most suitable visualization for user situations
(e.g., data, task, and environment)2. Adaptive visualization
Incrementally update existing visualization to accommodate changing situations (e.g., changing data or visual forms)
3. Weaving analytics and visualization together Analytics helps create more effective visualization Visualization assists analytics by allowing users to “see” the data
One path to explore - Smart Visualization
Visualization1
3
2
How do we get there?
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Interacting with Visual Summary
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“people” involved and their relationships
Interacting with Visual Summary