1. SEMCARE – Semantic Data Platform for Healthcare
Demo –
Stefan Schulz, Markus Kreuzthaler, Pablo López-García
MUG – IMI
Philipp Daumke, Kornél Markó
Averbis GmbH

2. Vision and Aims of SEMCARE
SEMCARE aims to promote better patient care by supporting patient diagnosis and identifying patients for optimal treatment.
European FP7 project, , 1.5 Mio EUR budget
Multiple languages, multiple data privacy regulations  
Identify patient cohorts based on patient-level criteria
Different use cases in cardiology  
Gain insights in (un)structured data
Open infrastructure, easy 3rd party integration (e.g. tranSMART)  

3. Most EHR Information in Unstructured Texts
Characteristics:
"telegram style" (incomplete sentences)
abundance of potentially ambiguous acronyms and abbreviations (HT, "Amp.digg")
Jargon ("Cor normokard")
Spelling variants ("Systolicum")
Spelling errors ("Symtomatik")
"Zuweisung zu RR-Einstellung bei St.p. HTX Anamnese Dyspnoe NYHA II, keine AP, keine Vertigo, US-Ödeme re > li, keine Nykturie
Klin.-phys. Symtomatik Cor normokard, etwas arrhythmisch, HT rein, (leises) Systolicum; US- Ödeme re > li bei St.p. Amp.digg. II+III ped.dext."

4. Use cases (examples) Patient recruitment
With no searchable patient database, identifying and recruiting suitable patients delay clinical trials
Delayed trials waste resources and slow access to new therapies
Decision support for genetic and rare diseases
deficient knowledge about rare diseases, although as a whole represent a large medical challenge
Combined with the lack of incentives to treat or cure them , serious public health problems may arise
Other potential use cases
Administrative coding support
Predictive analytics
Quality assessment
Patient summary

5. SEMCARE Components
SEMCARE aims to promote better patient care by supporting patient diagnosis and identifying patients for optimal treatment.
Services
Terminology Management
Text Mining
Search & Analytics
ETL, Data Integration
Data Privacy, Anonymization
3rd party applications (i2b2)
3rd

6. Terminology Management

7. Terminology Management
HEALTHCARE
Terminology
Categories
PHARMA
Terminologies
ATC
Drugs
ChEBI
Compounds
FMA
Anatomy
ChEMBL
HL7
Misc
DrugBank
ICD-10
Indications
EntrezGene
Genes
ICD-O
Oncology
Phenotype Ontology
Phenotypes
LOINC
Lab
Uberon
M. Anatomy
MedDRA
Uniprot
MeSH
OTHERS
NCI-Thesaurus
Agrovoc
Agriculture
OPS
Procedures
Averbis Thesaurus
RadLex
Radiology
eClass
Products
RxNorm
GeoNames
Geo
SNOMED CT
GND
UCUM
Units
ProfiClass
UMLS
Quantities
Term
Removal
Too general terms
Mapping Mode
Abbreviations
Acronyms
Short terms
Term
Enrichment
Missing Synonyms
Missing Concepts

8. Text Mining Preprocessing Information Extraction Writer File Readers
Diagnosis
File Consumer
Document Zoning
Medication
Search Engine
Sentence Detection
Laboratory Values
I2B2/tranSMART
Tokenizing
ECG results
Tableau, Pentaho, Spotfire, Qlikview
POS-Tagging
Abbreviations
Chunking
Negation Detection
Stemming
Date Recognizer
Decompounding …

9. Text Mining Integration of different terminologies
Parallel search within and across different terminologies
Creation, editing and deletion of terminologies and concepts
Automated synonym enrichment
Multilingual support
NLP view of a terminology

10. Observables, Numbers, Units

11. ECG Annotation Example

12. Diagnosis

13. Other Findings

14. Numbers and Units

15. Modifiers (incl. Leads)

16. = ECG Type

17. SEMCARE Demo Use Cases based on sample of 31921 clinical texts from Cardiology department)
* Acknowledgements: Daniel Scherr, Cardiology of the University Hospital Graz, Medical University Graz

18. Use Case: Implantable Cardioverter Defibrillator
Identify those who maybe suitable for implantable cardiac device (ICD) therapy according to NICE1 guidelines (and therefore at high risk of sudden death or worsening heart failure)
NICE guideline: Implantable cardioverter defibrillators and cardiac re-synchronisation therapy for arrhythmias and heart failure (review of TA95 and TA120) | NICE technology appraisal guidance 314 | Issued June 2014
Maybe suitable for “Implantable Cardioverter Defibrillator (ICD)”: EF ≤35% AND Class I-III Heart Failure Symptoms AND QRS duration 120–149ms but NOT with LBBB
1National Institute for Health & Care Excellence

19. Use Case CRT-P
Identify those who maybe suitable for implantable cardiac device therapy according to NICE1 guidelines (and therefore at high risk of sudden death or worsening heart failure)
NICE guideline: Implantable cardioverter defibrillators and cardiac resynchronisation therapy for arrhythmias and heart failure (review of TA95 and TA120) | NICE technology appraisal guidance 314 | Issued June 2014
Maybe suitable for cardiac resynchronization therapy pacemaker (CRT-P): EF ≤35% AND Class IV Heart Failure Symptoms but NOT QRS duration <120ms

20. Use Case Loss of Consciousness
Identify Miscellaneous High Risk Loss of Consciousness patients
Have had syncope AND FHx of sudden death AND/OR FHx of Brugada syndrome AND/OR Brugada type pattern on ECG AND/OR prolonged QTc (adjusted range for sex)

21. Use Case CPVT
Catecholaminergic Polymorphic Ventricular Tachycardia (CPVT)
Based on Executive summary: HRS/EHRA/APHRS expert consensus statement on the diagnosis and management of patients with inherited primary arrhythmia syndromes
Structurally normal heart AND normal ECG AND [ exertional AND/OR stress-induced bidirectional VTAND/OR polymorphic ventricular premature beats AND/OR VT ] AND under 40 years old

22. Use Case Lab Values Cholesterol > 300 mg/dl Hämoglobin erhöht
Kalium > 5 mmol/l

23. Gold Standard Creation
brat rapid annotation tool

24. Automatic Annotations

25. Indexing Performance
Total time for add: 11,7845 minutes, average documents per minute 2708,7330
Process finished, committed documents

26. Outlook CBmed project IICCAB
"Innovative use of Information for Clinical Care and Biomarker Research"
Closely related topic
Main target: Re-use of clinical data for biomarker research (cancer / metabolism)
Secondary targets: Re-use of clinical data for patient quick view, coding support, and predictive analytics (KAGes)
Same text processing / information extraction tools: Averbis
SAP tools for semantic data warehouse and query interface
Ultimate goal:
large-scale semantic processing of textual content of patient records in order no develop novel tools and interfaces for clinical care and secondary use of clinical data for research
Multiple challenges, especially resource generation (user interface terminology)