=Paper=
{{Paper
|id=Vol-1114/Poster_Doulaverakis
|storemode=property
|title=A Semantic-enabled Framework for Drug Recommendations
|pdfUrl=https://ceur-ws.org/Vol-1114/Poster_Doulaverakis.pdf
|volume=Vol-1114
|dblpUrl=https://dblp.org/rec/conf/swat4ls/DoulaverakisNKK13
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==A Semantic-enabled Framework for Drug Recommendations==
https://ceur-ws.org/Vol-1114/Poster_Doulaverakis.pdf
A Semantic-enabled Framework for Drug
Recommendations
Charalampos Doulaverakis1 , George Nikolaidis2 , Athanasios Kleontas MD2,3 ,
and Ioannis Kompatsiaris1
1
Information Technologies Institute, Centre for Research and Technology Hellas,
Thessaloniki, Greece, email: {doulaver,ikom}@iti.gr
2
Ergobyte S.A., Thessaloniki, Greece, email: {gnikolaidis,
kleontas}@ergobyte.gr
3
AHEPA, University General Hospital of Thessaloniki, Greece
Abstract. The paper presents Panacea, a semantic-enabled framework
capable of offering drug-drug and drug-diseases interaction discovery.
For enabling this kind of service, medical information and terminology
had to be translated to ontological terms and be appropriately coupled
with medical knowledge of the field. International standards for diseases
and dug classifications provide the backbone of the common representa-
tion of medical data while the medical knowledge of drug interactions
is represented by a rule base which makes use of the aforementioned
standards. The framework provides an efficient drug recommendations
service where Semantic Web technologies are coupled with traditional
business rule engines.
Keywords: ontologies, medical information modelling, rule-based rea-
soning
1 Introduction
One of the health sectors where intelligent information management and infor-
mation sharing compose valuable preconditions for the delivery of top quality
services is personalized drug prescription. This is more evident in cases where
more than one drug is required to be prescribed, a situation which is not uncom-
mon, as drug interactions may appear. The problem is magnified by the wide
range of available drug substances in combination with the various excipients
in which the former are present. Semantic Web technologies can play an im-
portant role in the structural organization of the available medical information
in a manner which will enable efficient discovery and access. Research projects
funded for enabling Semantic Web technologies in the diagnosis and therapeutic
procedures exist such as REMINE (http://www.remine-project.eu/) and PSIP
(http://www.psip-project.eu/) or works such as [1], but they don’t fully address
the problem of automated drug prescription using drug-drug and drug-disease
interactions.
In this work Panacea, a semantic-enabled system for discovering drug recom-
mendations and interactions, is presented. Panacea is based on experiences and
2
lessons drawn from the development of GalenOWL [2], a similar system which
had Semantic Web technologies in its core. As such, Panacea can be considered
the evolution of GalenOWL in terms of design and scalability. The paper is orga-
nized with Section 2 giving details of the framework’s architecture, the semantic
modelling and reasoning approaches, while Section 3 evaluates the framework,
discusses the results and concludes the paper.
2 Architecture, semantic modelling and reasoning
In order to demonstrate the benefits of the proposed drug recommendation sys-
tem, a use case is described: An elder man visits his family doctor complaining
for pain in his abdominal region which is accompanied with fever and he is di-
agnosed with right pyelonephritis. The patient is already receiving clopidogrel
for chronic atrial fibrillation, and candesartan and amlodipine for high arterial
blood pressure. For treating the patient, the doctor has to prescribe drugs in
order to treat the resulting inflammation and fever, the cause of the inflam-
mation (the pyelonephritis) and the abdominal pain. However, there should be
checks for interactions of the drugs that the patient is already prescribed with
the new condition and also verify that the new prescription will not have adverse
effects or interactions with the patient’s current medication and medical history.
It is clear that the task for the doctor is hard and using an automated drug
recommendation system can minimize this risk of wrong prescriptions.
The purpose of Panacea is to provide drug prescription recommendations
based on a patient’s medical record and drug prescription rules. For details
regarding the initial medical and pharmaceutical data that were available, the
reader is encouraged to read [2].
Panacea follows a layered reasoning process. During initialization, standard
medical terminologies and custom encodings are transformed to semantic enti-
ties, using an appropriate vocabulary, and the initial ontology is constructed. A
reasoner binds to the ontology to infer relations such as inheritance, unions and
equivalences. In order to get recommendations in Panacea, a patient instance
with the appropriate medical record data is created and is fed to the knowledge
base. The reasoning process enriches the patient instance with inferred knowl-
edge, thus making that knowledge explicit. On this enriched instance, and by
utilizing a different reasoning process, the set of medical rules is applied upon.
The result of this final stage of rule-based reasoning is the recommendations list
which can be retrieved through querying.
Semantic transformations Panacea is built on top of international standards
of medical terminology in order to represent medical and pharmaceutical in-
formation. The standard terminologies that are used are ICD-10 (International
Classification of Diseases), ATC (Anatomical Therapeutic Chemical classifica-
tion), UNII (Unique Ingredient Identifier) and IVT (International Virus Taxon-
omy). Besides these international standards, a number of domain classifications
have been declared in order to enhance the usability of the system or to rep-
resent data that are not included in the standards. These are a) a Substance
3
classification which represent human-readable names for drug active substances
and correspond to ATC codes, b) Custom Concepts where they represent dis-
eases or drugs that are not present in the international standards, and c) Custom
Collections where they represent “groups” of substances and diseases that are
frequently present in drug interactions.
SKOS vocabulary: The medical classifications were expressed using the SKOS4
vocabulary. Each of the classifications is placed under the skos:ConceptScheme
class and each entry is placed under the skos:Concept class. Classifications hierar-
chies are expressed with the skos:broaderTransitive and skos:narrowerTransitive
properties while Custom Collections are represented with the skos:member prop-
erty. Equivalences, e.g. between ATC codes and Substances, are expressed with
the skos:closeMatch property. A total of 64, 658 definitions of classification codes
have been expressed using SKOS.
Medical reasoning When querying the system for recommendations, a patient
instance is created with the initial patient data and is loaded in the knowledge
base. The reasoner, using RDFS inference and a small number of additional rules,
infers all the implicit patient data. As an example, a patient who suffers from
a form of thrombocytopenia (icd:D69.6 ) will be inferred as also suffering from
icd:D69, icd:D65-D69, icd:D50-D89 (through skos:broaderTransitive) and from
pnc-cc:deficiency-bone-marrow (through skos:member from the Custom Collec-
tion definition).
Rule-based reasoning Drug recommendations in Panacea are generated using
a rule-based approach. The rules express the indications and contraindications
of drug substances while their premises are the medical definitions and the pa-
tients’ age and sex group. The rules use the logical operators and (&) and or
(|) and parentheses. An example of a rule is for the substance “ lisuride” which
is expressed as “lisuride = icd:E22.0 | (icd:E22.1 & (icd:N91.0 | icd:N97)),
ageGroup=adult or elder ”.The rule reads that: substance “lisuride” is recom-
mended for adult and elder patients who suffer from E22.0, OR suffer from E22.1
AND one of the N91.0 OR N97.
The rule base consists of 1,342 rules whose validity has already been assessed
in [2]. These rules have to be properly parsed and transformed in order to match
the knowledge base and the, enriched with implicit knowledge, patient instance.
Any rule reasoner can be utilized to use the rule base. To demonstrate this con-
cept, two separate rule engine integrations, the Jena5 and Drools6 rule engines,
have been developed. While the Drools integration only required to translate the
rule base to the Drools Rule Language and develop an additional layer for Drools
to interface with the Patient instance, the Jena integration proved more complex
as the rule base is quite large to be handled efficiently and certain optimizations
had to be implemented. These optimizations involve the introduction of coarse
rule selection phase where a subset A of candidate rules is created from the
4
SKOS vocabulary, http://www.w3.org/2009/08/skos-reference/skos.html
5
Apache Jena, http://jena.apache.org/
6
Drools – Business logic integration platform, http://www.jboss.org/drools/
4
Table 1. Evaluation between the 2 Panacea reasoning approaches and GalenOWL
Panacea-Jena Panacea-Drools GalenOWL
Initialization time 32.0 s 34.7 s 148 s
Memory consumption 169 MB 280 MB 649 MB
Query response time 47 ms 5 ms 16 ms
initial rule base that match the patient’s sex and age group, and a fine selection
phase where rules from A that contain at least one of the patient’s data, i.e. a
skos term, in their premises are singled out and a fine set R ⊆ A is created from
them. R is loaded in Jena and can contain as little as 50 rules.
3 Evaluation and Conclusion
For evaluating the framework, a comparison was made between the two ap-
proaches for the rule-based reasoning and GalenOWL. The comparison was fo-
cused on the usability of the framework in a production environment. Three
parameters were measured: initialization time, memory consumption after ini-
tialization, and query response time, i.e. the time needed to retrieve the recom-
mendations. Results are shown in Table 1, where values for GalenOWL are from
[2]. The results show that both Panacea approaches exhibit satisfactory results
on all three parameters with Jena appearing to consume less memory as the rule
base is loaded during query time and with Drools being optimized to handle
large rule bases efficiently.
The paper presented Panacea, a framework for semantic-enabled drug rec-
ommendations discovery. The framework utilizes a layered reasoning approach
were the medical ontology and the patient data instances are fed to a RDF(S)
reasoner and drug recommendations are generated using the second, rule-based
reasoning layer where any rule engine can be employed. Concerning future work,
the possibility to add probabilistic reasoning will be investigated and an evalu-
ation of the framework on real world data will be carried out.
Acknowledgments This work has been supported by the national project
“Panacea”, funded by GSRT Hellas under the “Support for SMEs” programme.
References
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2. Doulaverakis, C., Nikolaidis, G., Kleontas, A., Kompatsiaris, I.: GalenOWL: Ontol-
ogy based drug recommendations discovery. Journal of Biomedical Semantics 3(14)
(2012)