=Paper=
{{Paper
|id=Vol-1589/MultiLingMine1
|storemode=property
|title=Identification of Disease Symptoms in Multilingual Sentences: An Ontology-Driven Approach
|pdfUrl=https://ceur-ws.org/Vol-1589/MultiLingMine1.pdf
|volume=Vol-1589
|authors=Angelo Ferrando,Silvio Beux,Viviana Mascardi,Paolo Rosso
|dblpUrl=https://dblp.org/rec/conf/ecir/FerrandoBMR16
}}
==Identification of Disease Symptoms in Multilingual Sentences: An Ontology-Driven Approach==
https://ceur-ws.org/Vol-1589/MultiLingMine1.pdf
Identification of Disease Symptoms
in Multilingual Sentences:
an Ontology-Driven Approach?
Angelo Ferrando1 , Silvio Beux1 , Viviana Mascardi1 , and Paolo Rosso2
1
DIBRIS, Università degli Studi di Genova, Italy
angelo.ferrando@dibris.unige.it, silviobeux@gmail.com,
viviana.mascardi@unige.it
2
PRHLT, Universitat Politècnica de València, Spain
prosso@dsic.upv.es
Abstract. In this paper we present a Multilingual Ontology-Driven
framework for Text Classification (MOoD-TC). This framework is highly
modular and can be customized to create applications based on Multi-
lingual Natural Language Processing for classifying domain-dependent
contents. In order to show the potential of MOoD-TC, we present a
case study in the e-Health domain.
Key words: Multilingual Natural Language Processing, Ontology-Driven
Text Classification, BabelNet, Symptom Disease Identification
1 Introduction
The large amount of digital data made available in the last years from a wide
variety of sources raises the need for automatic methods to extract meaningful
information from them. The extracted information is precious for many purposes,
and especially for commercial ones. Jackson and Moulinier [12] observe that
“there is no question concerning the commercial value of being able to classify
documents automatically by content. There are myriad potential applications of
such a capability for corporate Intranets, government departments, and Internet
publishers”.
The problem of classifying multilingual pieces of text was addressed since the
end of the last millennium [17] but it is still a significant problem because each
language has its own peculiar features, making the automatic management of
multilingualism an open issue.
The use of ontologies to classify multilingual texts [5] is a good alternative
to standard machine learning approaches in all those situations where a training
set of documents is not available or it is too small to properly train the clas-
sifier. Ontology-driven text classification does not depend on the existence of a
training set, as it relies solely on the entities, their relationships, and the tax-
onomy of categories represented in an ontology, that becomes the driver of the
?
The first author of this paper is a PhD student in Computer Science at the Uni-
versity of Genova, Italy. The work of the last author was in the framework of the
SomEMBED MINECO TIN2015-71147-C2-1-P research project.
Title Suppressed Due to Excessive Length 7
classification. Another advantage of ontology-driven classification is that ontol-
ogy concepts are organized into hierarchies and this makes possible to identify
the category (or the categories) that best classify the document’s content, by
traversing the hierarchical structure.
In this paper we present MOoD-TC (Multilingual Ontology Driven Text
Classifier [3, 13]), a highly modular system which has been conceived, designed
and implemented to be customized by the system developer for obtaining differ-
ent domain-dependent behaviors, always centered around the multilingual text
classification process. The original contribution of this paper is the exploitation
of the core “multilingual word identification” functionalities of MOoD-TC for a
challenging scenario in the e-Health domain, where classification is a by-product
of disease symptoms identification in multilingual pieces of text, driven by a
standard symptoms ontology. A customization of MOoD-TC with an ad-hoc
module equipped with pre- and post-processing facilities suitable for the scenar-
ios that motivate our work, is also described.
The paper is organized as follows: Section 2 introduces three motivating
scenarios where an ontology-driven multilingual text classification may prove
useful, Section 3 analyzes the state of the art, Section 4 describes MOoD-TC,
Section 5 provides examples and experimental results, and Section 6 concludes.
2 Motivating scenarios
Alice is enjoying her holidays in Stockholm. Suddenly, she feels a painful spasm
to her stomach and in a few minutes a strong feeling of nausea appears. Spasms
go on for half an hour, and she starts to feel worried. She does not think it is
the case to go to the hospital, but she would at least ask for advice over the
phone. However, she cannot speak Swedish and, in the stressful situation she is
experiencing, she cannot recall how to express her health problems in English.
She could speak in her native language Italian, but it is not so likely that the
doctor can speak Italian as well.
Bob is making a walk in his town. He notices a young man bending over
his knees, with a scared expression on his face. He runs to help him, and he
understands that the problem is with his chest. The young man speaks French
only and Bob cannot understand him: he calls the first aid emergency number
and explains what he is seeing and what he supposes to be taking place. If he
could understand what the young man says, he would be definitely more helpful.
Carol is a volunteer in Honduras. She is neither a physician nor a nurse.
She has a very basic knowledge of first aid procedures and a first aid kit with
medicines that she knows how to administer, given a clear diagnosis. A woman
runs towards her asking for her assistance. The woman’s small boy has a problem
with his head and he has a high fever but, without understanding the other
symptoms that the woman is trying to explain in Spanish, Carol cannot recognize
and classify the problem. In the remote place where she is, she cannot contact
the doctor. Carol should need to understand the other symptoms besides fever
and headache, in order to select the correct medicine.
The three scenarios above are all characterized by the impossibility for the
doctor to visit the patient on-the-fly and the need for the patient to be under-
stood despite language barriers, in order to get advice for minor problems or to
8 Angelo Ferrando1 , Silvio Beux1 , Viviana Mascardi1 , and Paolo Rosso2
speed up the assistance procedure for major ones. The patient’s need could be
suitably addressed by identifying and translating symptoms from her language
to the assistant’s or the doctor’s one. If automatic tools for facing this issue
were available, for example as an app installed on the mobile phone, the three
situations could evolve in the following way:
– Scenario 1: through the use of an app, the person needing care commu-
nicates with the “health emergency” software application in her own lan-
guage. The application performs a speech-to-text translation, identifies the
symptoms in the text based on a standard ontological representa-
tion of symptoms, and sends the list of symptoms expressed in the doctor’s
language to a center where they are managed either by intelligent software
agents or by human experts.
– Scenario 2: the “health emergency” software application is not directly used
by the person needing care, but by the one who assists her. Like before, the
assisted person can “tell” her problems to the application which performs a
speech-to-text translation and identifies the symptoms represented in a
domain ontology which appear in the text. The symptoms, translated
into the language of the person who his giving the first assistance, may
be read on the screen. That person can call the national first aid number,
telling what is happening, what she sees, and the symptoms which have been
understood, classified, and translated by the app.
– Scenario 3: also in this case, besides a speech-to-text translation, the
symptoms expressed in the language of the patient are identified
w.r.t. a symptoms ontology and translated into the target language.
The way this information is used can require a further automatic processing
stage, if the doctor cannot be involved in the loop and the person providing
aid needs an automatic support for making a diagnosis and identifying the
right therapy to administer.
In all the three situations above, a standard machine translation application
and a symptoms classifier based on machine learning might not be powerful
enough: the pre- and post-processing stages require to have a machine-readable
explicit representation of symptoms, in some vocabulary agreed upon by all the
application components and by the humans involved in the loop, in order to share
them among the application components (both at the client and at the server
side) and to reason about them if needed. A multilingual ontology-driven text
classification approach seems the right way to face these challenging scenarios.
3 State of the art
According to [8], in 1996 more than 80% of Internet users were native English
speakers. This percentage has dropped to 55% in 2000 and to 27.3% in 2010.
However, about 80% of the digital resources available today on the Web (includ-
ing deep Web and digital libraries) are in English [10]. This calls for the urgent
need of establishing multilingual information systems and Cross-Language In-
formation Retrieval (CLIR) facilities. How to manipulate the large volume of
multilingual data has now become a major research question.
Title Suppressed Due to Excessive Length 9
In this paper we are interested in Natural Language Processing (NLP) tech-
niques for solving multilingual term identification and text classification prob-
lems in the e-Health domain where extracting information from clinical notes
has been the focus of a growing body of research in the past years [14]. Common
characteristics of narrative text used by physicians in electronic health records
make the automatic extraction of meaningful information hard. NLP techniques
are needed to convert data from unstructured text to a structured form read-
ily processable by computers [15]. This structured representation can be used
to extract meaning and enable Clinical Decision Support systems that assist
healthcare professionals and improve health outcomes [6].
Signs and symptoms have seldom been studied for themselves in the field
of biomedical information extraction. Indeed, they are often included in more
general categories such as “clinical concepts” [22], “medical problems” [21] or
“phenotypic information” [19]. Moreover, most of the available studies are based
on clinical reports or narrative corpora. In [11, 18], indeed, the aim consists in
symptom extraction from clinical records and in [20] the authors identify the risk
factors for heart disease based on the automated analysis of narrative clinical
records of diabetic patients.
Another recent project in e-Health NLP context is the IBM Watson for On-
cology1 . It has an advanced ability to analyze the meaning and context of struc-
tured and unstructured data in clinical notes and reports, easily assimilating
key patient information written in plain English that may be critical to select
a treatment pathway. These works are different from ours because they do not
address multilingual aspects and, furthermore, because they have to manage
the differences between the “signs”, which are identified by clinicians, and the
“symptoms”, which can be described directly by the sick person.
In our work we do not have to manage clinical records but directly the infor-
mation provided by the person who feels sick. This difference is crucial in works
using an ontology-driven approach, because clinical reports contain many more
technical words2 compared to a text written (or a sentence told) by a normal
person describing how she feels. This allows us to use simpler ontologies. Es-
pecially from the multilingual viewpoint, having an ontology containing simple
concepts, omitting useless technicalities, allows achieving better results with less
effort, considering that a technical word could be less supported by the tools we
use during our text classification pipeline.
The assumption upon which MOoD-TC relies, is the availability of ontolo-
gies in the domain of interest. Even if the application developer might design and
implement her own domain ontology from scratch, integrating well-founded and
widely used ontologies into MOoD-TC would be the most modular, reusable
and scientifically acceptable approach. Luckily, many domain ontologies exist, in
particular in the biomedical domain. Panacea [7], the Ontology for General Med-
ical Science3 , and the Gene Ontology4 are just a few recent examples, besides
the “symptoms ontology” used for our experiments and discussed in Section 5.
1
http://www.ibm.com/smarterplanet/us/en/ibmwatson/watson-oncology.html
2
A clinical report is written by a doctor.
3
https://bioportal.bioontology.org/ontologies/OGMS
4
http://geneontology.org/
10 Angelo Ferrando1 , Silvio Beux1 , Viviana Mascardi1 , and Paolo Rosso2
4 MOoD-TC
MOoD-TC has been developed as part of Silvio Beux’ Masters Thesis [3], start-
ing from [13]. Its aim is to classify multilingual textual documents according to
classes described in a domain ontology. MOoD-TC consists of the Text Clas-
sifier (TC) and the Application Domain Module (ADM). It provides a set of
core modules offering functionalities which are common to any text classifica-
tion problem (text pre-processing, tagging, classification) plus a customizable
structure for those modules which can be implemented by the developer in order
to offer application-specific functionalities. It returns a classification of the text
w.r.t. the ontology taken as input. The classification performed by TC which
is implemented in Java and exploits the Language Detector Library5 , BabelNet
[16], and TreeTagger6 .
The Language Detector Library detects, with a precision greater than 99%,
53 languages making use of Naive Bayesian filters. It is devoted to recognize
the language Lo of the ontology o and the language Ld of the textual document
d. The TreeTagger tool performs the tagging of d in order to obtain, for each
word w ∈ d different from a stop word, its lemma (the canonical form of the
word) and its part of speech (POS). This information is used by BabelNet to
perform the translation of w into the ontology language. Finally, the translated
word w0 is searched inside the ontology and contributes to the classification of
d in the category modeled by the ontology concept c having the same semantics
as w0 . The ClassifierObject is the object that stores a correctly classified word
(and additional information) of the document d with respect to o. TC returns
a list of such objects. ADM specializes the text classifier task by implementing
Fig. 1. Integration pipeline of TC and ADM.
functionalities for pre- and post- processing a multilingual textual document. If
an ADM is used, the entire system specializes its behaviour in the domain repre-
sented by that particular ADM (e.g., from text classifier to disease recognizer).
In our system TC can work alone, but an ADM is meant to work in close con-
nection with the core system. The core modules are implemented to work for the
European languages (which share some common features like, for example, the
relationship between noun and adjective), but they could be extended to cope
with the peculiar features of other languages; in fact, thanks to the modularity
of the system, it is possible to integrate different algorithms created specifically
to handle that peculiarities, without modifying the entire system. The ADM
processes the TC input and output in order to obtain a new domain oriented
tool. An ADM is composed by two sub-components: pre-processing and post-
processing. The pre-processing component takes as input a digital object (for
5
https://code.google.com/p/language-detection/
6
http://code.google.com/p/tt4j/
Title Suppressed Due to Excessive Length 11
example a spoken sentence, in the scenarios discussed in Section 2) and returns
a new processed text, while the post-processing component takes as input the
TC output and returns a domain dependent result. Figure 1 shows the entire
pipeline of the integration process between the TC and the ADM.
5 Exploiting MOoD-TC for Symptom Identification
As illustrated in Section 2, the scenarios we aim to address require that disease
symptoms appearing in a text are correctly identified w.r.t. a domain ontology.
The pre-processing stage consists of moving from a spoken sentence to a text
and the post-processing in translating the identified symptoms into a target lan-
guage and, depending on the scenario, moving back from text to speech and/or
reasoning over them. In the sequel we discuss the experiments related with our
main task, namely that of symptoms identification.
The domain ontology used for describing symptoms is a standard ontology
named the symptoms ontology 7 , partially shown in Figure 2. It is an ontology of
disease symptoms with symptoms encompassing perceived changes in function,
sensations or appearance reported by a patient and indicative of a disease. We
stress that our experiments in exploiting MOoD-TC for symptom identifica-
tion did not require to build any new ontology. Rather, consistently with the
good principle of reusing existing software whenever available and, in particular,
reusing existing ontologies, we just passed the symptoms ontology as input to
the TC, obtaining the results discussed in the next section.
Fig. 2. Symptoms ontology (the three branches are children of “Symptom”).
In the sequel we discuss our initial experiments with phrases in five differ-
ent languages (English, French, German, Italian, Spanish), where symptoms are
7
http://purl.obolibrary.org/obo/symp.owl
12 Angelo Ferrando1 , Silvio Beux1 , Viviana Mascardi1 , and Paolo Rosso2
identified by the TC module. The classification of two sample sentences is shown
below, where the TC GUI screenshot associated with each sentence shows the
ontology concepts which appear in the text along with the number of their oc-
currences in the text.
Phrase 1 (Italian language): “Credo di
avere la febbre, continuo a sudare e ho i
brividi. Non la smetto di tossire e fatico
a mangiare a causa del male alla gola,
come un forte bruciore. Mi sento stan-
chissimo e ho dolore a tutti i muscoli.”
Phrase 3 (Spanish language): “Me
siento fatal. Tengo temperatura, vòmito
y diarrea. Hace dos dı̀as que no consigo
comer nada. Tengo nausea y mareos.”
The experiments have been carried out on 32 sentences for each of the 5
languages, for a total of 160 sentences. Each sentence describes symptoms re-
lated to one of the following sixteen disease: tinnitus, food allergy, cervical,
dehydration, hyperthyroidism, flu, appendicitis, food poisoning, labyrinthitis,
narcolessia, pneumonia, diabetes type 1, hyperglycemia, hypoglycemia, bronchi-
tis, jet lag (two sentences for each disease). To cover the widest range of cases
we considered the diseases with the most varied symptoms. The description of
symptoms associated with each disease has been retrieved from [9] and each sen-
tence contains 2 up to 9 symptom words. The sentences were manually created
by the authors.
Since the final purpose of this work is to provide an automatic diagnostic
system with as many symptoms as possible, in order to devise the correct di-
agnosis, we were mainly interested in symptoms which appear in the text but
which are not identified by our classifier (false negatives). We also looked for
false positives, but their number is so low to be irrelevant for our experiments.
Also, false positives are due to an under classification, rather than an actually
wrong classification: if the text contains the “abdominal cramp” symptom, for
example, and it is classified with the more general “abdominal symptom” con-
cept, we consider this result a false positive as a more specific concept could have
been returned. Figure 3 shows the average number of symptoms that should have
been identified w.r.t the correctly identified symptoms in the five considered lan-
guages. Figure 4 shows the number of false negatives (y axis) for disease (x axis).
Figure 3 demonstrates that the results greatly vary with the disease. For exam-
ple, symptoms related to tinnitus are hardly classified, but this can be easily
explained by observing the ontology we used, where problems related to ears are
not modeled at all. By carefully analyzing the obtained results, we also realized
that sometimes the performances of the classifier are worsened by the presence of
a symptom in the text which has a different grammatical role than the symptom
in the ontology (usually a noun), making their matching impossible although the
word root and the meaning are the same. For example, the ontology contains
the noun “irritability”, but if the text contains the adjective “irritable” (in any
Title Suppressed Due to Excessive Length 13
Fig. 3. For each disease, the leftmost column (in black) measures the average number
of symptoms that should have been identified; the next five columns show the aver-
age number of correctly identified symptoms in Italian, French, German, Spanish and
English sentences respectively.
Fig. 4. Trend of errors for disease in the five languages (False Negatives). On the x
axis the diseases are reported (labels are omitted) and on the y axis the number of
false negatives for disease is reported: each line in the graphic is associated with one
language.
language), the identification fails. This problem is due to the way the root of a
word is computed, and to the way words are managed in BabelNet.
What emerges from Figure 4 is that false negatives have a very similar be-
havior despite the language of the sentence. This is again due to the two reasons
discussed above. Despite these problems, which have a clearly understood mo-
tivation and which can be addressed by extending the ontology and by refining
the management of word root extraction, MOoD-TC has demonstrated to be
a flexible and ready-to-use approach for multilingual symptoms identification
driven by a standard ontology we retrieved on the web.
6 Conclusions and Future Work
In this paper we presented the MOoD-TC architecture showing its possible use
in the symptoms identification problem. The speech-to-text pre-processing stage
can be faced using existing tools, and the post-processing stage with a translation
of the identified symptoms into the doctor’s language can be addressed using Ba-
belNet, in the same way we exploit BabelNet for bridging the text, whatever its
language, and the ontology. The more challenging post-processing stage of sup-
porting the user in providing a diagnosis given a set of identified symptoms could
be addressed by means of sophisticated expert system such as the old and well
known MYCIN [4] and more recent projects (http://www.easydiagnosis.com/,
https://www.diagnose-me.com/, [2]), some of which are ontology-driven [1].
Our framework does not face many well known open problems in multilingual
text classification and information extraction such as negation [23] and named
entities, but rather it provides a flexible and modular approach ready for in-
tegrating, with limited effort, the results and algorithms addressing the above
problems coming from the research community.
In the short time, our work will be devoted to overcome the problems that
limit the performances of MOoD-TC in the considered scenario: we will make
the word identification more flexible and we will extend the symptoms ontology
with those symptoms which have not been modeled so far.
In the future, it would be interesting to run an experimental comparison
between our approach and a machine learning one. In case of a limited number
of labeled examples, in fact, it would be feasible to apply semi-supervised learning
methods. Depending on the comparison results, we will also consider to combine
both approaches, using a domain ontology to improve the results of a traditional
machine learning approach.
References
1. B. Al-Hamadani. CardioOWL: An ontology-driven expert system for diagnosing
coronary artery diseases. In 2014 IEEE Conference on Open Systems (ICOS),
pages 128–132, 2014.
2. R. P. Ambilwade, R. R. Manza, and B. P. Gaikwad. Medical expert systems for
diabetes diagnosis: A survey. Int. J. of ARCSSE, 4(11), 2014.
3. S. Beux. MOoD-TC: A general purpose multilingual ontology driven text classifier.
Master’s Degree Thesis in Computer Science, University of Genova, Italy, 2015.
4. B. G. Buchanan and E. H. Shortliffe. Rule Based Expert Systems: The Mycin Ex-
periments of the Stanford Heuristic Programming Project. Addison-Wesley, 1984.
14
5. G. de Melo and S. Siersdorfer. Multilingual text classification using ontologies. In
ECIR Conference, Proceedings, volume 4425 of LNCS, pages 541–548. Springer,
2007.
6. D. Demner-Fushman, W. Chapman, and C. McDonald. What can natural language
processing do for clinical decision support? Journal of Biomedical Informatics,
42(5):760–772, 2009.
7. C. Doulaverakis, G. Nikolaidis, A. Kleontas, and I. Kompatsiaris. Panacea, a
semantic-enabled drug recommendations discovery framework. J. Biomedical Se-
mantics, 5:13, 2014.
8. Global Reach. Global internet statistics (by language). Technical report, Global
Reach, June 2005.
9. H. W. Griffith. Complete guide to symptoms, illness & surgery for people over 50.
Body Press/Perigee New York, NY, 1992.
10. B. Guo-Wei and C. Hsin-Hsi. Cross-language information access to multilingual
collections on the Intenet. Journal of the American Society for Information Sci-
ence, 51, 2000.
11. H. Harkema, I. Roberts, R. Gaizauskas, and M. Hepple. Information extraction
from clinical records. In S. Cox, editor, Proceedings of the 4th UK e-Science All
Hands Meeting, Nottingham, UK, 2005.
12. P. Jackson and I. Moulinier. Natural Language Processing for Online Applications:
Text Retrieval, Extraction & Categorization. John Benjamins, 2002.
13. M. Leotta, S. Beux, V. Mascardi, and D. Briola. My MOoD, a multimedia and
multilingual ontology driven MAS: design and first experiments in the sentiment
analysis domain. In ESSEM Workshop, Proceedings, pages 51–66, 2015.
14. S. M. Meystre, G. K. Savova, K. C. Kipper-Schuler, and J. F. Hurdle. Extracting
information from textual documents in the electronic health record: a review of
recent research. Yearbook of medical informatics, pages 128–144, 2008.
15. P. Nadkarni, L. Ohno-Machado, and W. Chapman. Natural language process-
ing: An introduction. Journal of the American Medical Informatics Association,
18(5):544–551, 2011.
16. R. Navigli and S. P. Ponzetto. Babelnet: The automatic construction, evaluation
and application of a wide-coverage multilingual semantic network. Artif. Intell.,
193:217–250, 2012.
17. D. W. Oard and B. J. Dorr. A survey of multilingual text retrieval. Technical
report, College Park, MD, USA, 1996.
18. A. Rosier, A. Burgun, and P. Mabo. Using regular expressions to extract informa-
tion on pacemaker implantation procedures from clinical reports. In Proceedings
of the AMIA Annual Symposium, Washington DC, USA, 2008.
19. B. R. South, S. Shen, M. Jones, J. H. Garvin, M. H. Samore, W. W. Chapman,
and A. V. Gundlapalli. Developing a manually annotated clinical document corpus
to identify phenotypic information for inflammatory bowel disease. BMC Bioin-
formatics, 10(S-9):12, 2009.
20. A. Stubbs, C. Kotfila, H. Xu, and Özlem Uzuner. Identifying risk factors for heart
disease over time: Overview of 2014 i2b2/UTHealth shared task Track 2. Journal
of Biomedical Informatics, 58, Supplement:S67 – S77, 2015.
21. O. Uzuner, B. R. South, S. Shen, and S. L. DuVall. 2010 i2b2/VA challenge on
concepts, assertions, and relations in clinical text. JAMIA, 18(5):552–556, 2011.
22. K. B. Wagholikar, M. Torii, S. Jonnalagadda, H. Liu, et al. Pooling annotated
corpora for clinical concept extraction. J. Biomedical Semantics, 4:3, 2013.
23. M. Wiegand, A. Balahur, B. Roth, D. Klakow, and A. Montoyo. A survey on
the role of negation in sentiment analysis. In Proceedings of the Workshop on
Negation and Speculation in Natural Language Processing, NeSp-NLP ’10, pages
60–68, Stroudsburg, PA, USA, 2010. Association for Computational Linguistics.
15