Semantic Technologies for Healthcare

Daedalus presentation

Daedalus Technology in the Health Sector

Madrid, June 2014

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

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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


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


Structured

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Monitoring !  In the U.S.A.

75%

Internet Information about healthcare

42%

Social Networks Information about healthcare

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Monitoring !  In Spain

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Monitoring

!  What to monitor?

Drugs

Diseases

Reactions to drugs

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Monitoring !  Who’s interested?

Drugs companies

Health centers (hospitals, private

clinics)

Administrators of blogs, forums…

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!  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

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PROJECTS AND EXPERIENCES


!  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

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ONLINE HEALTH CONTENT MONITORING


Health Dashboard

Online Health Content Monitoring

!  Reputation in Pharma •  Drugs and diseases mentions •  Adverse drug reaction identification •  Trends detection

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PILOT EXPERIENCE IN THE DETECTION OF INTERACTIONS BETWEEN DRUGS


Objective:

Pilot experience in the detection of interactions between drugs

!  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.

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!  Syntactic information obtained by means of:

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eHealth


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Process for detecting interactions between drugs

Models: Detection Effect Mechanism Int Advise Negations

Train Documents

Drugs Relations Evaluation

SemEval

Sentence Simplification

jSRE x 5

Appositions Coordinates Clause splitting

Test Documents

Drugs Relations

Sentence Simplification

Ddi Detection jSRE

Ddi Classification

jSRE Cross-Validation

Negations

Pos-sintact

eHealth eHealth


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

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Evaluation


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!  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)


!  Corpora employed in the evaluation:

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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


SEMANTIC ENRICHMENT OF MEDICAL RECORDS


!  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

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Semantic enrichment of Medical Records


MK-2012-15-DAEDALUS-01 -23


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


!  Analysis process

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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


ANONYMIZATION OF MEDICAL RECORDS


!  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.

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Anonymization of Medical Records


!  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.

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Anonymization of Medical Records


MULTIMEDIA SEARCH IN MEDICAL RECORDS


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

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Multimedia Search in Medical Records


Medical Record search by voice

!  Voice interaction:

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Transcription

Archive

Search


Search on audio or video content !  Example of application:

!  Multimedia Search - Search of videos

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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.

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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

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!  Experiments in ImageCLEFMed (CLEF European Forum)

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!  Examples of multilingual information search on ImageCLEFMed experiments (European Forum CLEF)



PILOT EXPERIENCE TAGGING MEDICAL REPORTS


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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.

Pilot experience in tagging medical reports


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Linguistic resources


•  Terms in Spanish

•  Combination of SNOMED in Spanish and SNOMED in English

•  Use of semantic relationships (same_as) referring to concepts

UMLS ICD9 ES

Dict.


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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.


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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


PRODUCT: eHealth


!  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

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Textalytics eHealth


!  Specific multilingual dictionaries for the domain of healthcare based on UMLS:

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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


!  Daedalus technology for semantic enrichment:

eHealth

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Textalytics eHealth


Use case: integration in other linguistic processing platforms: GATE

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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.


WHO WE ARE



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.

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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

[email protected]

http://www.daedalus.es

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