Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
Upcoming SlideShare
Sustainable nutrition security - ILSI 2014
Next
Download to read offline and view in fullscreen.

16

Share

Download to read offline

Semantic Technologies for Healthcare

Download to read offline

Daedalus develops technology to extract the meaning and structure all types of multimedia content. In the field of Healthcare or e-Health, Daedalus' semantic technology allows to exploit automatically the information featured in the Electronic Health Record (EHR).
This presentation covers Daedalus experience in:
• Online health content monitoring
• Semantic enrichment (tagging) of medical records
• Anonymization of medical records
• Multimedia search in medical records
• Detection of interactions between drugs
• Text analytics and data analytics in the health sector

Related Books

Free with a 30 day trial from Scribd

See all

Related Audiobooks

Free with a 30 day trial from Scribd

See all

Semantic Technologies for Healthcare

  1. 1. Daedalus presentation Daedalus Technology in the Health Sector Madrid, June 2014
  2. 2. Daedalus Technology in the Health sector ! Daedalus develops technology to extract the meaning and structure all types of multimedia content. Our customers can monetize their content automatically. ! In the field of Healthcare, Daedalus' semantic technology allows to exploit automatically the information featured in the Electronic Health Record (EHR). ! Multilingual environment: English, Spanish, Portuguese, Catalan 2 DAEDALUS in Healthcare
  3. 3. Daedalus Technology in the Health sector DAEDALUS in Healthcare CONTENTS ! Presentation ! Projects and Experiences • Online health content monitoring • Pilot experience in the detection of interactions between drugs • Semantic enrichment of medical records • Anonymization of medical records • Multimedia search in medical records • Pilot experience tagging medical reports ! Product Features ! Who we are eHealth
  4. 4. Daedalus Technology in the Health sector Operations • How many structured data from the Electronic Health Record are processed? What happens with the unstructured ones? • Applications: • Support to codifications ICD9/10, SNOMED CT, CIMA… • Support systems for human operators: codification processes (e.g. diagnoses registered in parts in the emergency room) Unstructured DAEDALUS in Healthcare Structured 4
  5. 5. Daedalus Technology in the Health sector DAEDALUS in Healthcare Monitoring ! In the U.S.A. 75% Internet Information about healthcare Social Networks Information about healthcare 42% 5
  6. 6. Daedalus Technology in the Health sector DAEDALUS in Healthcare Monitoring ! In Spain 6
  7. 7. Daedalus Technology in the Health sector DAEDALUS in Healthcare Monitoring ! What to monitor? Drugs Diseases Reactions to drugs 7
  8. 8. Daedalus Technology in the Health sector DAEDALUS in Healthcare Monitoring ! Who’s interested? Drugs companies Health centers (hospitals, private clinics) Administrators of blogs, forums… 8
  9. 9. Daedalus Technology in the Health sector ! Problems that DAEDALUS can help solving ! Is the information of the Electronic Health Record (EHR) all structured? ! Is it well codified? ! Are there fields in which users can type any text, without restrictions? ! How much manual work is required to introduce information in the EHR? ! Can that information be reused? ! Are the archetypes enough to define a semantic interpretation? ! Location of information in different formats ! Considering the amount of information that is generated, both in EHRs and in scientific literature, tools to ease the search are necessary ! Interaction by means of natural language, including voice ! Analysis processes for Big Data tasks on health data 9 DAEDALUS in Healthcare
  10. 10. Daedalus Technology in the Health sector DAEDALUS in Healthcare PROJECTS AND EXPERIENCES
  11. 11. Daedalus Technology in the Health sector ! Online Health Content Monitoring ! Detection of interactions between drugs mentioned in biomedical literature ! Semantic enrichment of Medical Records ! Anonymization of Medical Records ! Multimedia search on Medical Records ! Pilot experience tagging medical reports 11 DAEDALUS in Healthcare
  12. 12. Daedalus Technology in the Health sector ONLINE HEALTH CONTENT MONITORING
  13. 13. Daedalus Technology in the Health sector Online Health Content Monitoring Health Dashboard ! Reputation in Pharma • Drugs and diseases mentions • Adverse drug reaction identification • Trends detection 13
  14. 14. Daedalus Technology in the Health sector PILOT EXPERIENCE IN THE DETECTION OF INTERACTIONS BETWEEN DRUGS
  15. 15. Daedalus Technology in the Health sector Pilot experience in the detection of interactions between drugs Objective: ! Application of Textalytics eHealth to the detection of interactions between drugs mentioned in biomedical literature. ! Within the framework of Challenge DDIExtraction 2013, organized as part of the conference SemEval, experiments related to the identification of interactions between drugs in medical texts are performed, in the style of the summaries available in MedLine. ! Model of hybrid analysis that combines Natural Language Processing techniques (based on Textalytics eHealth) with machine learning techniques. 15
  16. 16. Daedalus Technology in the Health sector Pilot experience in the detection of interactions between drugs ! Syntactic information obtained by means of: 16 eHealth
  17. 17. Daedalus Technology in the Health sector 17 Process for detecting interactions between drugs Drugs Relations Drugs Relations Evaluation Models: Detection Effect Mechanism Int Advise Negations Train Documents SemEval Sentence Simplification jSRE x 5 Appositions Coordinates Clause splitting Test Documents Sentence Simplification Ddi Detection jSRE Ddi Classification Cross-Validation jSRE Negations Pos-sintact eHealth eHealth
  18. 18. Daedalus Technology in the Health sector Pilot experience in the detection of interactions between drugs Evaluation ! The quality of the recognition process is measured in terms of: ! Precision: number of drugs and relationships identified correctly ! Recall: number of drugs and relationships extracted compared to the total in the existing test texts ! F-Score: weighting of the previous two. ! In the task, systems capabilities are measured in: ! DEC: detection of interactions between drugs ! CLA: classification of the type of drug (can be a drug, a brand, a chemical or pharmacological relation among a group of drugs and chemical agents that affect living organisms) ! MEC, EFF, ADV, INT: depending on the type of interaction: mechanisms (MEC), effects (EFF), notices (ADV) and interactions (INT) ! MAVG: average value of F-Score for the 4 types of interactions 18
  19. 19. Daedalus Technology in the Health sector Pilot experience in the detection of interactions between drugs Evaluation 19 ! The measure F-Score comes to represent how good an information extraction system is by taking into account both the precision (correct detection) and the coverage (wanted elements that have been extracted compared to the ones gone unnoticed). ! Results: ! In the detection of interactions between drugs from text, F-Score values greater than 70% are obtained (67% precision, 77% recall) ! In the classification in terms of the type of drug that is being referenced (a medical product, a brand or a compound) F-Score values around the 50% are obtained (51% precision, 57% recall)
  20. 20. Daedalus Technology in the Health sector ! Corpora employed in the evaluation: 20 SPilot experience in the detection of interactions between drugs Corpus Description GENIA 2,000 summaries, 400,000 words and 100,000 annotations of biological terms Cincinnati 600,000 words with anonymized clinical data MedLine 200 MedLine summaries noted BioText 3,500 phrases in which diseases, treatments and semantic relations among them have been tagged. EBI diseases 600 phrases in which diseases and symptoms have been tagged (around 350 UMLS terms) EDGAR: 100 MedLine summaries in which more than 400 genes and more than 350 drugs have been tagged DDi 2,800 phrases in which more than 11,000 drugs and 2,400 interactions between them have been tagged
  21. 21. Daedalus Technology in the Health sector SEMANTIC ENRICHMENT OF MEDICAL RECORDS
  22. 22. Daedalus Technology in the Health sector ! Objective: Semantic interoperability ! Elements: • Vocabularies: UMLS " SNOMED CT, ICD-9, ICD-10, CIE-9, CIE-10, LOINC • Archetypes: reusable clinical models, openEHR • Templates: views of the archetypes, HL7 • Reference models: specification for the definition of the archetype, ISO13606 ! Automatic linguistic treatment helps to structure the Medical Record providing: • Automatic tagging according to vocabularies • Links between medical reports with templates • Multilingual treatment based on Daedalus’ technology 22 Semantic enrichment of Medical Records
  23. 23. Daedalus Technology in the Health sector MK-2012-15-DAEDALUS-01 -23 Semantic enrichment of Medical Records Use case: automatic classification of Medical Records ! Example of application: automatic assignation of ICD codes to radiology reports. • ICD (International Statistical Classification of Diseases and Related Health Problems), standard by the World Health Organization ! Objective: • Analysis of the justification of medical tests for insurance companies ! Case data: • Data from urology reports • Period 1 year • 978 documents and 45 ICD-9-CM tags with 94 combinations • Provided by the Department of Radiology at Cincinnati Children's Hospital Medical Center
  24. 24. Daedalus Technology in the Health sector ! Analysis process 24 Semantic enrichment of Medical Records Morphological Analysis • Pre-processing • Part-of- Speech (POS) tagging Identification of medical concepts • Semantic tagging (domain dictionaries) • Treatment of acronyms • Specific vocabularies Evaluation • Measurement of the quality of the resulting tagging Result Text
  25. 25. Daedalus Technology in the Health sector ANONYMIZATION OF MEDICAL RECORDS
  26. 26. Daedalus Technology in the Health sector ! Why? ! To fully exploit the information already collected on multiple dimensions. Information to: ! Improve control panels ! Ease the development of clinical tests ! Big Data environment ! Presents the 3 main characteristics of this type of problem: ! Volume: large amounts of data ! Speed: very dynamic ! Variety: very different types ! Privacy ! It is necessary to ensure that the privacy of patient data is not violated. 26 Anonymization of Medical Records
  27. 27. Daedalus Technology in the Health sector ! Objective: to ease the analysis and exploitation of the information contained in Medical Records. ! Linguistic processing technology for the detection of names of persons, addresses, phone numbers with the purpose of hiding the identity of patients in medical transactions. 27 Anonymization of Medical Records
  28. 28. Daedalus Technology in the Health sector MULTIMEDIA SEARCH IN MEDICAL RECORDS
  29. 29. Daedalus Technology in the Health sector Information search by voice ! Voice access to the information: • Voice recognition applied to systems of data search in medical records and documentation in general: o Diagnosis indication by voice o Treatment indication by voice o Immediate access to the EHR of the patient by voice 29 Multimedia Search in Medical Records
  30. 30. Daedalus Technology in the Health sector Medical Record search by voice ! Voice interaction: 30 Multimedia Search in Medical Records Transcription Archive Search
  31. 31. Daedalus Technology in the Health sector Search on audio or video content ! Example of application: ! Multimedia Search - Search of videos 31 Multimedia Search in Medical Records
  32. 32. Daedalus Technology in the Health sector Search on Medical Records from text ! Location of information: • Offers alternative search options in situations in which results cannot be obtained. • Construction of alternatives that correct common orthographic mistakes, calculating the similarity between search terms and the indexed ones, also offering the user selection possibilities (e.g. “Did you mean...?") • Semantic search using domain ontologies as UMLS. 32 Multimedia Search in Medical Records
  33. 33. Daedalus Technology in the Health sector Use case: search on medical records and images ! Searches over a collection of medical cases consisting of: • Images (50,000 approx.) • Textual descriptions of the cases (in English and French) ! To search, only images are used (X-rays, scanners...) and, occasionally, text ! Context of work: experiments at the European Forum CLEF (Cross Language Evaluation Forum) on search for information 33 Multimedia Search in Medical Records
  34. 34. Daedalus Technology in the Health sector Use case: search on medical records and images ! Experiments in ImageCLEFMed (CLEF European Forum) 34 Multimedia Search in Medical Records
  35. 35. Daedalus Technology in the Health sector Multimedia Search in Medical Records Use case: search on medical records and images 35 ! Examples of multilingual information search on ImageCLEFMed experiments (European Forum CLEF)
  36. 36. Daedalus Technology in the Health sector PILOT EXPERIENCE TAGGING MEDICAL REPORTS
  37. 37. Daedalus Technology in the Health sector 37 Pilot experience in tagging medical reports Steps: ! Obtaining resources in the appropriate format for Textalytics infrastructure. Based on UMLS. ! Building a tagger able to analyze the input text, extract noun phrases and get the corresponding ICD9 code according to their similarity to the resources’ entries. ! Actual reports provided by a hospital have been transcribed combining OCR techniques and manual processes. Codes have been noted down and used to evaluate the tagging prototype.
  38. 38. Daedalus Technology in the Health sector 38 Pilot experience in tagging medical reports Linguistic resources UMLS • Terms in Spanish • Combination of SNOMED in Spanish and SNOMED in English • Use of semantic relationships (same_as) referring to concepts ICD9 ES Dict.
  39. 39. Daedalus Technology in the Health sector 39 Pilot experience in tagging medical reports Linguistic resources ! Filtering of UMLS to obtain terms in Spanish and their respective ICD9 code. ! Filtering of the resulting thesaurus consisting of more than 45,000 terms. ! Many of these are common polysemic words leading to a top labelling. ! The frequency of appearance in the thesaurus is considered to filter words with poor semantic content. ! An additional dictionary with acronyms and abbreviations of the medical domain has been included.
  40. 40. Daedalus Technology in the Health sector 40 Pilot experience in tagging medical reports Architecture of the solution ! Some elements: 1. Preprocessing: Linguistic analysis of the input text by means of Textalytics to identify noun phrases. 2. Rules Inference to identify ICD9 codes by characterizations. Example: if a phrase contains the structure “number”+ “measurement unit”, at least the name of a drug and the word ‘treatment’, then its code will be V58.69
  41. 41. Daedalus Technology in the Health sector PRODUCT: eHealth
  42. 42. Daedalus Technology in the Health sector ! Daedalus technology for semantic enrichment in Healthcare: eHealth ! Functionality: ! Semantic tagging according to ontologies in the domain of healthcare (UMLS): diseases, procedures, drugs, symptoms... relations between elements ! Treatment of linguistic variants: gender and number, acronyms and abbreviations, aliases ! Multilingual environment: English, Spanish, Portuguese, Catalan 42 Textalytics eHealth
  43. 43. Daedalus Technology in the Health sector ! Specific multilingual dictionaries for the domain of healthcare based on UMLS: 43 Textalytics eHealth Dictionary Coverage (terms) Diseases 81.119 Symptoms 5.505 Organisms 23.941 Organs/Body parts 73.863 Functional concepts 3.885 Treatments and procedures 134.782 Drugs/Chemicals 264.709 Proteins 42.117 Genes 58.300 TOTAL 688.221
  44. 44. Daedalus Technology in the Health sector ! Daedalus technology for semantic enrichment: eHealth 44 Textalytics eHealth
  45. 45. Daedalus Technology in the Health sector Use case: integration in other linguistic processing platforms: GATE 45 Textalytics eHealth ! GATE: General Architecture for Text Engineering ! GATE is a tool aimed at non-technical staff for the analysis of large collections of texts through the combination of different linguistic processes.
  46. 46. Daedalus Technology in the Health sector DAEDALUS in Healthcare WHO WE ARE
  47. 47. Daedalus Technology in the Health sector Who we are ! Since 1998 we offer solutions and services for the information society. ! Private limited company. ! Our main line of activity focuses on the extraction of meaning from multimedia content in order to monetize to the maximum the content managed by our customers. ! Clients: big companies in all sectors: media, defense, telecommunication, energy, public administration, etc. ! Vocation: innovation, with active participation in national and European R&D projects. 47 DAEDALUS in Healthcare
  48. 48. Daedalus Technology in the Health sector DAEDALUS, S.A. Head Office: López de Hoyos 15 28006 Madrid Technical Department: Edificio Vallausa II Albufera 321 28031 Madrid Tel: +34 913.32.43.01 info@daedalus.es http://www.daedalus.es 48 DAEDALUS in Healthcare
  • miss-coffee

    Nov. 15, 2015
  • nitin-doshi

    Aug. 18, 2015
  • docbrady

    Aug. 6, 2015
  • srishtiindia

    Nov. 14, 2014
  • moraymareyes

    Oct. 21, 2014
  • adrianlunacobos

    Oct. 20, 2014
  • AlbertoMorenoDaz1

    Sep. 12, 2014
  • CACARDOO

    Sep. 9, 2014
  • phongphaninlek

    Sep. 8, 2014
  • AlvinaPaulineSantiag

    Sep. 8, 2014
  • cdepablo

    Aug. 28, 2014
  • CarlosAbadSnchez

    Aug. 28, 2014
  • JosLuisMartnezFernnd

    Aug. 26, 2014
  • Antonio.Matarranz

    Aug. 25, 2014
  • josecarlos.gonzalez

    Aug. 25, 2014
  • Daedalus_SA

    Aug. 25, 2014

Daedalus develops technology to extract the meaning and structure all types of multimedia content. In the field of Healthcare or e-Health, Daedalus' semantic technology allows to exploit automatically the information featured in the Electronic Health Record (EHR). This presentation covers Daedalus experience in: • Online health content monitoring • Semantic enrichment (tagging) of medical records • Anonymization of medical records • Multimedia search in medical records • Detection of interactions between drugs • Text analytics and data analytics in the health sector

Views

Total views

1,729

On Slideshare

0

From embeds

0

Number of embeds

36

Actions

Downloads

31

Shares

0

Comments

0

Likes

16

×