MVilla IUI 2012 Lisbon

Presenting Prof. Mr. Manuel de la Villa [email protected] http://www.uhu.es/manuel.villa

A Learning Support Tool with Clinical Cases Based on Concept Maps and Medical Entity Recognition

Manuel de la Villa1, Fernando Aparicio2, Manuel J. Maña1, Manuel de Buenaga2

1Universidad de Huelva, 2Universidad Europea de Madrid

 !e problem. An Use Case.

 Related work. - Biomedical Ontologies

- Concept map and Mind map

- Graph-based Interfaces based on Ontologies

 A rough prototype as a “proof of concept”.

 Evaluation

 Conclusions and future works.

Index

A Learning Support Tool with Clinical Cases Based on Concept Maps and Medical Entity Recognition 2

 !e use of intelligent systems in higher education is incresingly used as strategy to improve learning and teaching processes.

 Case-based learning, based on constructivist learning theories, is very practical in Medical education.

 Making the internet sources available to students may not be sufficient to promote their learning… let’s see an example.

!e scenario


 !e student reads new concepts, he needs more information to understand them.

 HOW???

 A free search?  One for every term??

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!e problem (I)


 Physicians in the early stages of learning face several drawbacks among [Luo & Tang 2008]: - Lack of experience and domain knowledge to perform a proper search - Lack of awareness about the medical terminology found

Oughhh!


 Free search???  User have problems to de"ne their information needs in a query string

[Jansen, Spink & Koshman, 2007].   Queries contain less than three terms (75,2%) and the majority of queries contain one

(18,5%), two (32,2%)  But also when the user initiates a search not really know what can be useful

and, therefore, it is difficult to specify the features of the elements of potentially useful information [Belkin, 2000].

Search engines usually return thousands of documents recovered, leading to inadequate results, with no semantic connection with the query and little to do with the user's needs.

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!e problem (and II)

Our proposal


NLP Module

NCBO Open Biomedical Annotator

MQL Topics

Freebase

Concepts table

Search Module Graph Module

Freebase Medlineplus Concepts map

… helps clinicians identify and access the meaning of medical concepts and …

… allow the teacher to de#ne the paths of access to information avoiding dispersion in the search and

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Freebase

UMLS … display concept maps automatically drawn from knowledge sources.

!e design of a support tool for Clinical Case-based learning that…

Related work: Biomedical Ontologies

 May include a wide range of medical concepts, basic information such as the type or class they belong to and how they are related (e.g. symptom / disease / treatment).

  Increasingly used to tackle concept recognition and annotation tasks in biomedical research.

 Some examples of ontologies are: -  GO (Gene Ontology), MeSH (Medical Subject Headings), FMA (Foundational

Model of Anatomy), GALEN, UMLS (Uni#ed Medical Language System), SNOMED-CT (Systematized Nomenclature of Medicine - Clinical Terms), etc.


Jaundice Hepatitis Adefovir

Is-a-symptom-of Is-treated-with

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We decide to use MedlinePlus (Health Topics), Freebase and UMLS mainly due to the ease of open information access through web services and XML #les

  UMLS (Uni#ed Medical Language System), developed by the National Library of Medicine (NLM) of USA. o Metathesaurus o  Concept o  CUI (Concept Unique Identi#er) o  Semantic Type(s) o  De#nition (if provided) o  Atoms o  Contexts o  Concept Relations

o Remote access with UTS Web services API.

o Source: MDR, !e Medical Dictionary for Regulatory Activities (MedDRA), developed by ICH, owned by IFPMA. o Translations: Czech, Dutch,

French, German, Italian, Japanese, Portuguese and Spanish.

Ontologies used UMLS Metathesaurus


Ontologies used Metaweb Freebase

•  Freebase is a large collaborative knowledge base consisting of metadata composed mainly by data integration processes and by its community members.

•  Domain independent nature: possibilities of applying results to other disciplines. •  !e information can be accesed through an API, MQL (Metaweb Query

Language), ACRE (an own platform to host applications) o RDF.

Our MQL Query for Concepts Map: http://api.freebase.com/api/service/mqlread?query= {"query":”[{\"type\":\"/medicine/disease\",\ "name\":\""+search_string+"\",\"/common/topic/article\":{\"guid\":null,\"limit\":1,\"optional\":true}, \"/common/topic/image\": {\"id\":null,\"limit\":1,\"optional\":true},\"symptoms\":[],\"treatments\":[], \"/medicine/disease/notable_people_with_this_condition\": [],\"/medicine/disease/risk_factors\": [], \"/medicine/disease/causes\": [],\"/medicine/disease/prevention_factors\": []}]}

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Ontologies used Metaweb Freebase

Ontology fragment for biomedical domain in Freebase

Concept map and Mindmap approaches.

  Widely applied in educational activities   2-dimensional graphics used to represent knowledge

comprised of nodes (representing concepts) connected by direct arcs (representing relationships)


Related work: Concept map and Mindmap approaches.

 Advantages: - Graphic presentation of knowledge enables quickly evaluation for experts -  In medical studies:

-  [Daley & Torre, 2010] Concept mapping in medical and healthcare learning: -  Promotes learning, provides additional resources, provides feedback to

students and conducts assessment -  [D’Antoni et al., 2009] Mind maps are very useful in medical education.

-  Problems: many topics to be covered in medicine, fair amount of time to design them

 Knowledge visualization, an emerging #eld.  Similarities between ontologies and concept maps.


Our metaphor? A graph (Concept Map)

 Concept Map extracts and displays only the information needed to determine a diagnosis of a disease in a medical case.


Graph-based Interfaces based on Ontologies Information retrieval

 Visual Concept Explorer: an automatic concept map generator with knowledge from medical ontologies and thesauri.


  Based on a !esaurus (Wordnet™)

Snappy Words

Visual !esaurus


Graph-based Interfaces based on Ontologies Visual dictionaries

builds a mental map from the information you #nd on a concept in the Wikipedia. It could be considered as a dynamically and automatically generated interface to browse Wikipedia.


Wikimindmap

Graph-based Interfaces based on Ontologies Search engines

Google Wonder Wheel shows related search terms to the current searched query and thus enable you to explore relevant search terms.


Yahoo Correlator extracts and organizes information from text, and searches for related names, concepts, places, and events to your query.

Graph-based Interfaces based on Ontologies Search engines


 SemViz (Semantic Abstraction Summarization [Rind$esh, Fiszman and Kilicoglu, 2004])  Takes as input a list of semantic predications produced by UMLS SemRep, from

a set of documents on a speci#ed disorder topic. !e output is a conceptual condensate (a concept map in graphic format) containing only those predications that represent key information in the input documents.

Graph-based interfaces based on ontologies Semrep

Computer tool description

http://orion.esp.uem.es:8080/MedicalFaceV2/ A Learning Support Tool with Clinical Cases Based on Concept Maps and Medical Entity Recognition 19

Computer tool description


NLP Module

NCBO Open Biomedical Annotator

MQL Topics

Freebase

Concepts table

Search Module Graph Module

Freebase Medlineplus Concepts map

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Freebase

UMLS

!e system working…


http://youtu.be/Dp9flQpvJdE http://www.medicalminer.org/MedicalFaceV2/ http://www.uhu.es/manuel.villa/viewmed http://sciencecases.lib.buffalo.edu/cs/files/stroke.pdf 21

User evaluation


 User oriented evaluation - Users: 60 second-year medical degree students from the School of Biomedical

Sciences at the Universidad Europea de Madrid, divided into 2 groups. - Objectives: To measure the in#uence of the system when student make a test,

besides usability and learning support provided. - Technique:

- Exam with 10 multiple choice questions about a selected case study -  34 self-perception Likert questionnaires for system users.

 Measure the differences between the results of the activity carried out in two ways: - With the system developed - With free Internet access

Mitral regurgitation: a.- Is the less common valvulopathy in the general population

b. - Has no relation with the cardiac problem presented by our patient

c. - May justify the mitral regurgitation

d. - Has a higher prevalence in women than in men

Test question example

Results user evaluation


Learning perception questions • Over 58% believe that the tool has helped them to extract relevant information about the case study (LQ1), and • more than 60% believe that the tool has helped them by reducing the time needed to understand the case study (LQ2).

Students' learning self-perception

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  Slightly better results for students who employed the tool (78.53% correct answers) than students who used unrestricted searches (76.92% correct answers). No statistically signi#cant.

Results user evaluation


Students' usability self-perception

Usability questions: • the tool interface is nice (UQ1), •  it is easy to "nd the information required (UQ2), • they feel comfortable using the tool (UQ3), • the speed is reasonable (UQ4) and • it is easy to use (UQ5).

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Systematic evaluation

 measure the ability of the tool to provide medical concepts in the graph, in relation to the original concepts annotated in the source document (as recall in information retrieval)


 measure novelty, the tool’s ability to discover and show us new relevant information related with the source document.

€

Novelty corpus( ) =

CrFreebaseCaSnomedCT + CrFreebasecorpus∑N # documents

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Conclusions.

  interfaces that simplify #nding and comprehension of information are needed.

 we have presented a tool that represent biomedical knowledge resources in a human and machine usable way (as ontologies and concept maps)

  the knowledge acquired through an active role is better #xed in their minds and longer term.

  advantage for teachers: it allows pre-selection of the knowledge sources accessible to students.

 !e students’ perception is good or very good in both usability questions and those related to the assistance provided


Future work.

 Focus our efforts on enhancing all the available features in the tool: -  usability of the interface, -  expansion and improvement of the

annotation process and -  enrichment of the information and concept

mapping.  Expand the user experience evaluation, to

measure the tool’s capacity to support teachers in active learning methodologies


Presenting Prof. Mr. Manuel de la Villa [email protected] http://www.uhu.es/manuel.villa


Manuel de la Villa1, Fernando Aparicio2, Manuel J. Maña1, Manuel de Buenaga2

1Universidad de Huelva, 2Universidad Europea de Madrid

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Technology

MVilla IUI 2012 Lisbon