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

1. 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
Presenting Prof. Mr. Manuel de la Villa
manuel.villa@dti.uhu.es
http://www.uhu.es/manuel.villa

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

3. The scenario
 The use of intelligent systems in higher
education is incresingly used as strategy to
improve learning and teaching processes.  The student reads new
concepts, he needs more
 Case-based learning, based on constructivist information to understand
them.
learning theories, is very practical in Medical
education.
 HOW???
 Making the internet sources available to
students may not be sufficient to promote  A free search?
their learning… let’s see an example.  One for every term??
A Learning Support Tool with Clinical Cases Based on Concept Maps and Medical Entity Recognition 3

4. The 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!
A Learning Support Tool with Clinical Cases Based on Concept Maps and Medical Entity Recognition 4

5. The problem (and II)
 Free search???
 User have problems to define 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.
A Learning Support Tool with Clinical Cases Based on Concept Maps and Medical Entity Recognition 5

6. Our proposal
The design of a support tool for Clinical Case-based learning that…
Freebase
… helps clinicians identifyNLP access the meaning of medical
and NCBO Open
MQL Topics Biomedical
concepts and … Module Annotator
Concepts table
… allow the teacher
Search Module … display concept UMLS
Graph Module
to define the paths maps automatically
of access to drawn from knowledge
Concepts map Freebase
information Freebase Medlineplus
sources.
avoiding dispersion
in the search and
A Learning Support Tool with Clinical Cases Based on Concept Maps and Medical Entity Recognition 6

7. 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).
Is-a-symptom-of Is-treated-with
Jaundice Hepatitis Adefovir
 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 (Unified Medical Language System),
SNOMED-CT (Systematized Nomenclature of Medicine - Clinical Terms), etc.
We decide to use MedlinePlus (Health Topics), Freebase and UMLS mainly due to the
ease of open information access through web services and XML files
A Learning Support Tool with Clinical Cases Based on Concept Maps and Medical Entity Recognition 7

8. Ontologies used
UMLS Metathesaurus
 UMLS (Unified Medical Language o Remote access with UTS Web
System), developed by the National services API.
Library of Medicine (NLM) of USA. o Source: MDR, The Medical
o Metathesaurus Dictionary for Regulatory Activities
o Concept (MedDRA), developed by ICH,
owned by IFPMA.
o CUI (Concept Unique Identifier)
o Translations: Czech, Dutch,
o Semantic Type(s) French, German, Italian, Japanese,
o Definition (if provided) Portuguese and Spanish.
o Atoms
o Contexts
o Concept Relations

9. 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.
• The 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": []}]}
A Learning Support Tool with Clinical Cases Based on Concept Maps and Medical Entity Recognition 9

10. Ontologies used
Metaweb Freebase
Ontology fragment for biomedical domain in Freebase

11. 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)
A Learning Support Tool with Clinical Cases Based on Concept Maps and Medical Entity Recognition 11

12. 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 field.
 Similarities between ontologies and concept maps.
A Learning Support Tool with Clinical Cases Based on Concept Maps and Medical Entity Recognition 12

13. 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.
A Learning Support Tool with Clinical Cases Based on Concept Maps and Medical Entity Recognition 13

14. Graph-based Interfaces based on Ontologies
Information retrieval
 Visual Concept Explorer: an automatic concept map generator with knowledge
from medical ontologies and thesauri.
A Learning Support Tool with Clinical Cases Based on Concept Maps and Medical Entity Recognition 14

15. Graph-based Interfaces based on Ontologies
Visual dictionaries
 Based on a Thesaurus (Wordnet™)
Visual Thesaurus
Snappy Words
A Learning Support Tool with Clinical Cases Based on Concept Maps and Medical Entity Recognition 15

16. Graph-based Interfaces based on Ontologies
Search engines
Wikimindmap
builds a mental map from the information you find on a concept in the
Wikipedia. It could be considered as a dynamically and automatically
generated interface to browse Wikipedia.
A Learning Support Tool with Clinical Cases Based on Concept Maps and Medical Entity Recognition 16

17. Graph-based Interfaces based on Ontologies
Search engines
Yahoo Correlator extracts and organizes
Google Wonder Wheel shows related search information from text, and searches for related
terms to the current searched query and thus names, concepts, places, and events to your query.
enable you to explore relevant search terms.
A Learning Support Tool with Clinical Cases Based on Concept Maps and Medical Entity Recognition 17

18. Graph-based interfaces based on ontologies
Semrep
 SemViz (Semantic Abstraction Summarization [Rindflesh, Fiszman and Kilicoglu, 2004])
 Takes as input a list of semantic predications produced by UMLS SemRep, from
a set of documents on a specified disorder topic. The output is a conceptual
condensate (a concept map in graphic format) containing only those predications
that represent key information in the input documents.
A Learning Support Tool with Clinical Cases Based on Concept Maps and Medical Entity Recognition 18

19. 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

20. Computer tool description
Freebase
NLP NCBO Open
MQL Topics Biomedical
Module Annotator
Concepts table
Search Module Graph Module UMLS
Concepts map Freebase
Freebase Medlineplus
A Learning Support Tool with Clinical Cases Based on Concept Maps and Medical Entity Recognition 20

21. The 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/
A Learning Support Tool with Clinical Cases Based on Concept Maps and Medical Entity Recognition stroke.pdf 21

22. 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 influence 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: Mitral
regurgitation:
a.-­
Is
the
less
common
valvulopathy
in
the
general
population
- With the system developed
b.
-­
Has
no
relation
with
the
cardiac
problem
presented
by
our
patient
- With free Internet access c.
-­
May
justify
the
mitral
regurgitation
d.
-­
Has
a
higher
prevalence
in
women
than
in
men
Test
question
example
A Learning Support Tool with Clinical Cases Based on Concept Maps and Medical Entity Recognition 22

23. Results user evaluation
 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 significant.
Learning perception questions
• O ver 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
A Learning Support Tool with Clinical Cases Based on Concept Maps and Medical Entity Recognition 23

24. Results user evaluation
Usability questions:
• the tool interface is nice (UQ1),
• it is easy to find the information
required (UQ2),
• they feel comfortable using the
tool (UQ3),
• the speed is reasonable (UQ4) and
Students'
usability
self-­perception
• it is easy to use (UQ5).
A Learning Support Tool with Clinical Cases Based on Concept Maps and Medical Entity Recognition 24

25. 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.
CrFreebase
∑corpus Ca
SnomedCT + CrFreebase
Novelty( corpus) =
N # documents
A Learning Support Tool with Clinical Cases Based on Concept Maps and Medical Entity Recognition 25
€

26. Conclusions.
 interfaces that simplify finding 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 fixed in their minds
and longer term.
 advantage for teachers: it allows pre-
selection of the knowledge sources
accessible to students.
 The students’ perception is good or very
good in both usability questions and
those related to the assistance provided
A Learning Support Tool with Clinical Cases Based on Concept Maps and Medical Entity Recognition 26

27. 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
A Learning Support Tool with Clinical Cases Based on Concept Maps and Medical Entity Recognition 27

28. Muito Obrigado
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
Presenting Prof. Mr. Manuel de la Villa
manuel.villa@dti.uhu.es
http://www.uhu.es/manuel.villa