Ontotext is a company established in 2000 that specializes in semantic web technologies and AI. It has attracted over 15 million euros in research funding and works with top pharmaceutical companies. The document discusses challenges in processing clinical text like a lack of multilingual medical terminologies and ontologies. It presents various clinical ontologies and knowledge bases that can help with tasks like concept normalization, data integration, and predictive modeling. The company has developed technologies for processing clinical text, linking data to ontologies, and building predictive models for adverse drug reactions.

1. making sense of text and data
Turning Data into Knowledge - Semantic Technologies in Healthcare
Todor Primov
Nikola Tulechki
Svetla Boytcheva
June 2021

2. Why Ontotext?
Company profile –
Sirma AI (Ontotext)
Established in year 2000, as a
Research Lab of Sirma Holding
Unique mix of knowledge graphs and
advanced NLP
Leader in semantic web technology
and AI
Solid experience in
research projects
Attracted more than 15M Euro in
innovation funding through more than
50 research projects, funded by
H2020, FP7, FP6, and FP5.
Key competences include ontologies
and knowledge graphs, graph
embeddings, NLP and semantic
normalization, ML/DL, data analytics
High-profiled
commercial clients Life
sciences
Use cases: Enterprise KG; Enterprise
Semantic Search; Insights Platforms;
Decision support systems
Clients: from start-ups to top 10
pharma companies; from smart apps
to large hospitals and health
insurance companies

3. About 80% of
Electronic Health
Records are in
unstructured format
Need for NLP tools for
processing clinical text
Lack of multilingual
terminology
resources and
domain specific
ontologies
The automatic processing and knowledge extraction from
medical records is a task with public importance

4. Clinical text
OPERATIONS / PROCEDURES :Dobutamine stress test , cardiac
ultrasound , EGD , chest x-ray , PICC placement .The patient is a
62-year-old female with a history of diabetes mellitus ,
hypertension , COPD , hypercholesterolemia , depression and CHF

13. Why the task for concept normalization
is so important?
o Disambiguation
o Usage of URI
o Data integration
o Reasoning
o Similarity search
o Phenotypes

14. Text-based classification
a process of assigning tags or categories to text
according to its content.

15. https://icd.who.int/browse10/2019/en

16. https://icd.who.int/browse10/2019/en

17. https://ncpha.government.bg/index/301-mkb-10-mejdunarodna-statisticheska-klasifikacia-na-bolestite-i-problemite-svurzani-sus-zdraveto.html

18. https://mediately.co/bg/icd/?q=e11

19. E11,Неинсулинозависим захарен диабет
E11,диабетес мелитус- типус 2.
E11,зд2
E11,захарен диабет тип 2 с лош гликемичен контрол
E11,захарен диабет тип ІІ- неинсулинозависим тип
E11," захарен диабет -тип 2,компенсиран "
E11,захарен диабет т. 2
E11,захарен диабет 2 тип - новооткрит
E11," захарен диабет тип 2, новооткрит"
E11,захарен диабет тип2 с неврологични усложнения
E11," захарен диабет-тип 2, без усложнения "
E11,зд тип 2
E11," захарен диабет тип 2, незадовоблително компенсиран "
E11," захарен диабет- тип 2, инсулинозависим "
E11," захарен диабет тип 2, компесиран "
E11,захарен диабет тип 2 -в метаболитна декомпенсация
E11,захарен диабет тип II
E11," захарен диабет-тип 2, декомпенсиран "
E11,диабет тип 2
E11,захарен диабет
E11,Захарен диабет тип 2
E11,"Захарен диабет, тип 2"
E11,Type II diabetes Mellitus
E11,typus II diabete Mellitus
E11,типус II диабетес мелитус

20. Standard Classification & Ontologies
https://bioportal.bioontology.org/

21. SNOMED CT
https://bioportal.bioontology.org/ontologies/SNOMEDCT

23. SNOMED CT
https://www.snomed.org/

24. https://browser.ihtsdotools.org/?

25. ICD-10 CM
https://bioportal.bioontology.org/ontologies/ICD10CM

26. DOID
https://bioportal.bioontology.org/ontologies/DOID

27. https://disease-ontology.org/

30. Massive number of knowledge bases
#30
https://lod-cloud.net/

31. #31
Figure 1. The growth of the LOD Cloud diagram
over time. Diagrammatic representation of the
number of the LOD Cloud total datasets (blue
line) and LOD Cloud Life Sciences domain
datasets (red line) from 2009 to 2017 (top).
Evolution of the graphical view of the LOD
Cloud from 2007 to 2017 (bottom).
Published in 2018
Life Sciences Linked Open Data Datasets Connections to SNOMED CT, RxNORM & GO
Artemis Chaleplioglou, Sozon Papavlasopoulos, Marios Poulos

32. https://www.wikidata.org/wiki/Q84263196

33. https://wikidata.metaphacts.com/resource/wd:Q84263196

35. https://w.wiki/3Tqy

37. GraphDB Empowers Scientific Projects to Fight COVID-19 and Publish Knowledge Graphs
Ontotext’s GraphDB is used by Mayo Clinic to Publish CORD-19 with Semantic Annotations and by Cochranе for COVID-19 Study Register
https://www.ontotext.com/blog/graphdb-empowers-scientific-projects-to-fight-covid19-and-publish-knowledge-graphs/

41. Adverse Drug Reactions Predictions

43. #

44. Data
● Four types of input text segments
a. manufacturer - company names like
ALLERGAN --- Allergan --- Allergan plc --- Allergan, Inc --- Allergan, Inc.
GlaxoSmithKline --- GlaxoSmithKline Biologicals ---
GlaxoSmithKline Biologicals Dresden --- GlaxoSmithKline Pharmaceuticals
a. indication - disease, condition like Back pain, Hypertension, Osteoarthritis
b. adverse event - disease, condition
c. intervention - drugs like FLUCLOXACILLIN, HYDROCORTISONE SODIUM SUCCINATE,
NORVIR SOFT GELATIN CAPSULES
● Data sources manufacturer indication adverse
event
intervention
ClinicalTrials.gov 12 313 604 506 2 468 553 100 000
FDAERS 9 032 469 22 206 659 26 570 878 32 575 511

45. Standard Classifications and Ontologies
Snomed CT - Systematized Nomenclature of Medicine Clinical
Terms
UMLS - Unified Medical Language System
CHEBI - Chemical Entities of Biological Interest Ontology
MESH - Medical Subject Headings

46. Concepts Normalization. Gazetteer Creation
● Use of existing ontologies and databases - UMLS (disease), Drug Central (drugs),
Wikidata (company)
● Re-writing rules to adjust the ontology labels to our needs
original ontology term re-written new label
Oppenheim's Disease Oppenheim Disease
Congenital chromosomal disease Congenital chromosomal disorder
Congenital ocular coloboma (disorder) Congenital ocular coloboma
Choledochal Cyst, Type I Type I Choledochal Cyst
Abnormality of the pulmonary artery the pulmonary artery Abnormality

47. Training Data

48. Adverse reaction suggestions
Where:
● ?subject iterates over all drugs in the dataset
● ?object iterates over all possible entities

50. Adverse Drug Reaction Suggestion

51. EXtreme-scale Analytics via Multimodal Ontology Discovery & Enhancement
Develop weakly supervised
knowledge discovery
algorithms for extreme
scale data, to associate:
• visual content of clinical
images
• semantic content included
in the diagnoses
Develop prediction &
analysis tools for clinical
settings & research

52. Clinical data are highly heterogeneous
• EHR
• Clinical Notes
• Biomedical data
• Medical Imaging Results
Data

53. AOEC Digital Workflow
STAINING
Microscope
Diagnosis

54. AOEC Digital Workflow
• New possibility to manage the block and slides archiving…

55. AOEC Digital Workflow

56. WSI

57. Annotations for image regions WSI
Implemented annotation system Virtum that transforms
the input to variety of standard output formats

58. Development of new ontologies
Development of new ontologies - ExaMode OWL 2
Ontology, including all the four diseases treated in the
project: colon carcinoma, cervix carcinoma, lung
carcinoma, and celiac disease.
• The ontology models the clinical cases for all the
diseases, including their intervention and locations.
• The ontology can be accessed through Mappings for
some standard ontologies in Healthcare domain
(available at:
https://zenodo.org/record/4081387#.YEoaymgzaHs )

59. https://examode.ontotext.com

60. Similarity search results

61. Acknowledgements
This work was carried out under the FROCKG project:
Fact Checking For Large Enterprise Knowledge Graphs
https://www.ontotext.com
This work was carried out under the ExaMode project: EXtreme-scale
Analytics via Multimodal Ontology Discovery & Enhancement
https://frockg.eu/

62. Thank you!
See Ontotext demos
Patient Insights: http://patient.ontotext.com/
Linked Life Data:
https://www.ontotext.com/knowledgehub/de
moservices/linked-life-data/
ExaMode: http://examode.ontotext.com/