Personalized Vaccination Using Ontology Based ... - Semantic Scholar

Personalized Vaccination Using Ontology Based Profiling Ozgu Can, Emine Sezer, Okan Bursa, and Murat Osman Unalir Ege University, Department of Computer Engineering, 35100 Bornova-Izmir, Turkey {ozgu.can,emine.unalir,okan.bursa,murat.osman.unalir}@ege.edu.tr

Abstract. Ontology-based knowledge representation and modeling for vaccine domain provides an effective mechanism to improve the quality of healthcare information systems. Vaccination process generally includes different processes like vaccine research and development, production, transportation, administration and tracking of the adverse events that may occur after the administration of vaccine. Moreover, vaccination process may cause some side effects that could cause permanent disability or even be fatal. Therefore, it is important to build and store the vaccine information by developing a vaccine data standardization. In the vaccination process, there are different stakeholders, such as individuals who get the vaccination, health professionals who apply the vaccination, health organizations, vaccine producers, pharmacies and drug warehouses. In this paper, a vaccine data standardization is proposed and a generic user modeling is applied in the context of personalized vaccination for healthcare information systems. Besides, policies are also used to strengthen the proposed personalized vaccination model by defining clinical guidances for individuals. The proposed personalized vaccination system offers a better management of vaccination process and supports the tracking of individual’s medical information. Keywords: Medical Knowledge Management, Semantic Web, Vaccine Ontology, Healthcare, Personalization.

1

Introduction

Healthcare systems around the globe are changing their information systems according to be able to share and reuse the patients’ information not only in a department where the information is produced, but also between the departments of an organization and also among the different organizations. Until recently, it was not reasonable to share a patient’s data between the departments of the healthcare organizations. In fact, the information obtained from records of a health information system is only the administrative data, such as patient’s name, age, insurance information and other personal data. Information about a patient is widely spread out among doctors, clinics, pharmacies, health agencies and hospitals. However, in recent years, information technologies are focused on E. Garoufallou and J. Greenberg (Eds.): MTSR 2013, CCIS 390, pp. 213–224, 2013. c Springer-Verlag Berlin Heidelberg 2013

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using and sharing the clinical data in a higher-level structured form of semantic rich information. Thus, sharing personal health information became more prevalent in distributed healthcare environments. Data standards are the common and consistent way of recording the information. Data, which is modeled by a particular standard, could be transmitted between different systems and it could be processed to have the same meaning in each system, program and institution. The main purpose of data standardization is to provide the common definition of the data. Data standards, generally, allow sending and receiving the data that has the same meaning and the same structure for different computer systems. Today, in the healthcare domain, approximately 2100 different data standards are being used [1]. It can be concluded from this number that each data standard has been developed for a different specific purpose, whereas it is troublesome to share and reuse these data between different systems. On the other hand, the information about a patient is needed to be shared between all principals of healthcare. Therefore, there is a need for a wider communication and interoperability between each stakeholder in the health domain [2]. However, according to other domains, any new application or technology is carried out very carefully to prevent death or permanent disability results. Thus, the adoption of a new technology must meet the highest standards of accuracy and effectiveness [3]. Vaccination is the most effective health event for individuals to improve their own immunity systems and acquire immunity against certain diseases. The administration of vaccine is considered to be the most effective way to improve individual’s immune system or prevent her from particular diseases which can result in death or permanent disability. Vaccination process starts with the individuals’ birth and continues for her lifelong care plan. Vaccinations are carried out by the departments of each country’s health ministries and takes place in distributed environments. Stakeholders of the vaccination domain can be consisted of individuals or their parents (if they are under 18), health professionals, public and private health agencies, vaccine research and development laboratories, vaccine production companies, pharmacies and also schools. All these stakeholders have different responsibilities and take roles in different vaccination processes. In vaccine system, an individual can be a doctor, a patient or medical companies, public and private health agencies. All these stakeholders can make the health care system more complex and difficult to apply to the real world applications. Profiling [4] is a solution to such problems with the help of profile languages like Friend of a Friend (FOAF)(http://www.foaf-project.org) and SIOC (http://sioc-project.org/). A patient has demographic properties like name, surname, blood-type and address. These properties must be gathered inside profiles to distribute to different domains. However, profiling is not as simple as business cards. Links to other profiles based on relationships can also be described inside profiles. Profiles can be expanded with other relationships and other profile definitions [5]. This gives the opportunity to localize user profiles but also keep them universal and sharable.

Personalized Vaccination Using Ontology Based Profiling

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Policies are generally used to control access to resources. A policy is a declarative rule set that is based on constraints to control the behavior of entities. This declarative rule set defines an information on what an entity can do or cannot do. In vaccine system, we use policies to define a clinical guidance for vaccination. In order to achieve this goal, we create clinical guidance policies based on the vaccination domain by using the user profiles of the health care domain. A brief contribution of this work is as follows: – developing a data standard to share and reuse vaccination information, – building an information base for vaccine information systems, – personalizing vaccine system by: – creating user profiles for health domain, – warning individuals about allergic reactions before vaccination, – tracking allergic reactions that could occur after vaccination, – using policies to define a clinical guidance for vaccination. The paper is organized as follows: Section 2 explains the knowledge representation of the proposed model. The development of vaccine, profile and policy ontologies are clarified in this section. Section 3 expresses the evaluation and validation process of ontologies. Section 4 presents the relevant related work. Finally, Section 5 contributes and outlines the direction of the future work.

2

Knowledge Representation

Knowledge representation will allow for the machines to meaningfully process the available information and provide semantically correct answers to imposed queries [6]. In order to perform the translation of information to knowledge: the information has to be put into context, the concepts have to be explained and defined,relationships between concepts and personal information have to be made explicit [7]. Ontologies are used to represent knowledge in the Semantic Web. Ontology is defined as an explicit specification of a conceptualization [8]. It is the formal representation of the concepts and the relations between these concepts for a specific domain. The new information could be inferred from the old information or defined rules. The main reasons for developing ontology could be summarized as: sharing the common semantics and the structure of the information between computers and people, providing the domain knowledge reusing, making assumptions of a domain explicitly, separating the computational knowledge from the domain knowledge and analyzing the domain knowledge [9]. As several ontology languages have been developed so far, the eXtensible Markup Language (XML) (http://www.w3.org/XML/), XML Schema (XMLS) (http://www.w3.org/XML/Schema), the Resource Description Framework (RDF) (http://www.w3.org/RDF/), RDF Schema (RDFS) (http://www.w3.org/TR/rdf-schema/ and the Web Ontology Language (OWL) (http://www.w3.org/OWL/) are the most common languages that are used to build ontologies in the Semantic Web. In this section, we use OWL to build ontologies which are used to represent vaccine information, personal health profiles and policies that are based on both of

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these vaccine and personal information. Ontologies are edited by Protégé 4.3 (http://protege.stanford.edu/), which is an open-source ontology editor and knowledge-base framework. Developed ontologies can be accessed from http://efe.ege.edu.tr/~odo/MTSR2013/. 2.1

Vaccine Ontology

In this work, we developed a vaccine ontology (Vaccine Ontology) to be used in vaccine information system in order to provide all services that occur in the vaccination process. The main purpose of developing the vaccine ontology is providing knowledge sharing among the health professionals. There are many other vaccine ontologies shared among different platforms, we developed our own ontology to give full support of all different types of diseases. As the knowledge sharing is important, using this data representation in the vaccine ontology has also the same level of importance in developing the interoperable electronic healthcare systems. Vaccine Ontology has the expressivity of SROIQ(D) DL (Description Logic) [10] and consists of 129 concepts, 50 object and 14 data properties. The core concepts and relations of Vaccine Ontology are represented in Figure 1. Vaccine Ontology is developed in OWL 2.0 which supports the compliance with all OWL and RDF described ontologies and XML Schemas.

Fig. 1. Vaccine Ontology concepts

As the primary objectives of this study are interoperability, information sharing and reusability, the vaccine domain is specifically handled with regards to the usage of stakeholders who are persons and organizations that participate in vaccination process. Stakeholders of the vaccine domain are individuals who are vaccinated - if the individual is not adolescent, the individual’s parents-, the health


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professionals (nurses and doctors), the ministry of health, private and official health agencies (e.g. doctor’s clinics, hospitals, polyclinics, public health centers), pharmaceutical warehouses, pharmacies, vaccine manufacturers, vaccine research and development centers defined as concepts of Vaccine Ontology. The human stakeholders are represented with Person concept, while the others are grouped under the Company concept. In this paper, we will focus on the main concepts of the Vaccine Ontology: Vaccination, Vaccine and VaccineTradeProduct. Vaccination is considered to be the most effective health application used for humans to prevent them from infectious diseases for many years. It is the administration of a vaccine to stimulate the immune system of an individual in order to prevent her from infectious diseases that may result in mortality or morbidity. Vaccination process starts with the birth of an individual and continues for her lifelong care plan. In Vaccine Ontology, Vaccination concept represents this real word event. The object property between Vaccination and VaccineTradeProduct is named with isAdmnistrationOf which is defined as:

The property chain description of isAdministrationOf object property is as follows: isV accinationOf o hasT radeP roduct −→ isAdministrationOf This description is used to infer that if Vaccine A is vaccination of Vaccination B and Vaccine A has a trade product as Vaccine Trade Product C, then Vaccination B is administration of Vaccine Trade Product C. The Vaccine and VaccineTradeProducts concepts are the other two semantically important core concepts of the Vaccine Ontology. Vaccine represents the general vaccines that are administrated to increase the immunity of a particular disease. BCGVaccine, MeaslesVaccine, HepBVaccine (defines Hepatitis B Vaccine) are example individuals of the Vaccine. The VaccineTradeProduct is defined to represent the commercial vaccines which are available on the market. For example, Hepavax Gene, Havrix and Prevenar are vaccine trade products for Hepatitis B Vaccine, Hepatitis A Vaccine and Pnomokok Vaccine, respectively. Moreover, there are new researches and development activities in the vaccine domain, and a new vaccine product may be placed in to the market. Therefore, in Vaccine Ontology, the concept of individuals that describe the general vaccines and the commercial products of these vaccines are separated from each other. But, they are associated with each other by using the hasTradeProduct and isTradedProductOf object properties. These object properties are also inverse of each other.

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Vaccine Ontology is an ontology that can grow during lifetime with the new instances of concepts. The main goal of developing the Vaccine Ontology is to use it as an information base for the vaccine information system. 2.2

Profile Ontology

Profiling is a common way to represent personal information. Personalized applications have internal profiling methodologies to create a user’s profile. Profiles can be demographical or social information based according to their usage. In a close structured application, like a banking system, profiles can only store limited information about users. However, social networks like Facebook (Facebook,http://www.facebook.com) have much more information about a user, such as their choices, clicks, friends or games. But this information is also restricted to other applications and it is not easy to reach this information from outside. A general profiling method, such as FOAF, defines an open world profile definition which can be accessed by only an URI. In our proposed model, we are extending FOAF with a new profiling methodology which gives the opportunity to describe group profiles, restricted or limited profiles based on demographical or set-based profile properties. Group profiles are a new way of describing user’s roles in daily life like father, doctor or computer game player. All these roles can have different preferences and properties. So, we define group profiles to store person’s different preferences. Besides, this methodology gives us the opportunity to assemble people with similar choices and properties. These collections of users are also gathered within this group profile. Figure 2 represents the general view of our architecture.

REI

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subclass uses

Fig. 2. General view of profiling architecture

Our profiling methodology includes Facility(MOF)(MOF,http://www.omg.org/spec/MOF/2.4.1/)

Meta-Objectinformation


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levels and it is connected by ontological imports and object level connections. Inside instance level, M0, a person’s FOAF profile, which can include its group profiles and demographical information, can be described. At M1 level, we specify tools and definitions to describe the group profiles. M2 level includes only MetaProfile definition which defines set-based profile properties, restrictions and demographical property definitions. Besides, MetaProfile covers the different types of profile properties based on their usage. Behavioral, demographical and social properties and their property restrictions based on demographical and set-based properties are all defined inside MetaProfile. Overall, MetaProfile, Profile and FOAF Profile definitions construct our multi-level profiling architecture to create more complex policies and healthcare definitions. 2.3

Policy Ontology

A policy is a regulation of constraints that defines what is permitted, prohibited or obliged. In this manner, policies enforce rules based upon the requester or the resource that is going to be accessed by the requester and rules express who can or cannot execute which action on which resources. As policies are generally used for access control, we are using policies in order to define restrictions of the vaccine domain ontology. In this work, Rei policy language [11] is used for semantic representation of policies. Rei is based on OWL-Lite and allows users to develop declarative policies over domain specific ontologies. Rei has its own syntax and consists of seven ontologies: ReiPolicy, ReiMetaPolicy, ReiEntity, ReiDeontic, ReiConstraint, ReiAnalysis, and ReiAction. Rei also includes specifications for speech acts, policy analysis and conflict resolution. Rei policy language endorses four deontic objects: permission, prohibition, obligation, and delegation. Permission is what an entity can do, prohibition is what an entity can not do, obligation is what an entity should do, and finally dispensation is what an entity need no longer do. The following example represents the definition of a permission deontic object that specifies ‘A doctor can read a patient’s file’ :

The Doctor policy definition of this permission example is represented as:

We specify policies based on the vaccine domain and profiles. Profiles are used as actors of policies rather than using the individual variables that are created in entity:Variable class of the ReiEntity ontology as the actor of the policy

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definition. Also, individuals of the vaccine domain are used as objects and actions of policy definitions. Figure 3 represents a policy example that specifies a prohibition indicating ‘A person who is pregnant cannot be vaccinated with live attenuated vaccine’. Live Attenuated Vaccine is a type of vaccine which includes the live pathogen whose virulence is reduced in the laboratory. Live attenuated vaccines should not be given to individuals with weakened or damaged immune systems. So in this prohibition policy example, as pregnancy can be weaken the mother’s immune system, LiveAttenuatedVaccine and Pregnant are individuals of Vaccine Ontology and Profile ontology, respectively.

Fig. 3. Prohibition example for Pregnant profile

3

Validation of the Ontology Model

As Pellet (http://clarkparsia.com/pellet/) supports datatype-ABox reasoning, SWRL support, reasoning debugging and incremental reasoning; we used it to reason ontologies. The defined restrictions are executed and as a result, a consistent ontology ecosystem is built, where profile and policy definitions inside the vaccination domain have become convenient to be executed by queries.

Fig. 4. Query example


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We used SPARQL (http://www.w3.org/TR/sparql11-overview/) query language to execute the defined policies. Figure 4 shows the query example that is executed on the defined policies. In this example, we queried policies that are defined for any profile in the vaccination domain. In order to achieve this, first, we found profiles of all patients. Secondly, we queried policies that are related with these profiles. Finally, we listed policies according to their deontic definitions. Figure 5 shows the result of this query. In this result, the prohibition policy defined for pregnant profiles can be seen.

Fig. 5. Query result

4

Related Work

Medical domain has a huge amount of domain knowledge which is described by controlled dictionaries or vocabulary standards. Standards like SNOMED-CT (Systematized Nomenclature of Medicine-Clinical Terms, http://www.ihtsdo.org/snomed-ct/), GALEN (Generalized Architecture for Languages, Encyclopedias and Nomenclatures in Medicine, http://www.openclinical.org/prj_galen.html) [12] and MesH (Medical Subject Headings, http://www.nlm.nih.gov/mesh/) are rich semantic ontologies that are described by formal ontology languages. These high level biomedical ontologies are developed to be used as a terminological vocabulary among biomedical domains. Domain specific ontologies, such as GO (Gene Ontology, http://www.geneontology.org/) [13] and OBR-Prox-Femur Application Ontology [14], are specific ontologies to represent the certain part of the human body. EHRs (Electronic Health Records) that are expressed in these formal terms, could be used for semantic intermediary (e.g. a content, which is defined in an ontology could be defined in another ontology) or inferencing (e.g. determining a statement as a statement of a more general or more specific statement) [15]. In SNOMED-CT, concepts have unique numeric identifiers called ConceptIDs, but these identifiers do not have a hierarchical or implicit meaning. The terms and concepts are connected each other with the predefined relationships. These predefined relationships are grouped in four main relationships: defining, qualifying, historical and additional. All the terms for vaccine and vaccination in SNOMED-CT are described at the concept level and also are very specialized terms. Creating instances by inheriting from these concepts are found nearly impossible. As known, in the software systems the maintenance and support are very important issues for the usage of the system. For example, adding a new instance for a new developed vaccine changes the ontology model of the system and this situation results with high costs but further more it is not a desirable situation for software systems. A vaccine ontology, named VO (Vaccine Ontology), is developed in [16] [17]. VO focuses on vaccine categorization,

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vaccine components, vaccine quality and vaccine-induced host responses. VO describes vaccines not only administrated to humans but also to animals. The ontology includes nearly about four thousands classes with seventy object properties and four data type properties. However, everything in the domain is described as classes in VO. Therefore, VO has the same problems described for SNOMED-CT. Most of the healthcare information systems define a patient as a basic profile. However, the concept of a patient is much more complex. A healthcare information system may need a process to create a patient profile based on different health domains[18]. This process narrows all the information about a patient. Although this process may have risks as the calculation persists. A new information about a patient needs an overall calculation. So, this kind of profiling is not suitable for a dynamic system. In [19], profiling is at the ontological level and based on FOAF and SIOC. A patient or a doctor can have interests. Thus, interests, skills and interactions are structured and represented inside user profiles. But, defining an interest and capturing an interest are not the same processes. Capturing an interest is based on domain knowledge and needs a deeper tailoring. In addition, overall representation of a user profile needs to be maintained in a semantically multi-level architecture. There are no restrictions nor definitions to represent interests, skills and interactions inside [19]. The most common policy languages in Semantic Web are KAoS [20], Rei [11], and Ponder [21]. KAoS is a DAML/OWL policy language. It is a collection of policy and domain management services for web services. KAoS distinguishes between authorizations and obligations. Rei is a policy specification language based on OWL-Lite. It allows users to express and represent the concepts of permissions, prohibitions, obligations, and dispensations. Rei uses speech-acts to make the security control decentralized. Speech-acts include delegation, revocation, request and cancel. Ponder is a declarative, object-oriented policy language for several types of management policies for distributed systems and also provides techniques for policy administration. Ponder has four basic policy types: authorizations, obligations, refrains and delegations. [22] gives a comparison of KAoS, Rei and Ponder policy languages. In [23], profile based policy management is studied in order to make use of semantically rich policies in terms of the personalization scope.

5

Conclusion

Vaccine Ontology introduced in this paper is developed according to the vaccine and vaccination knowledge in health domain. However, this ontology is not based on any ontology or any standard. The defined concepts in Vaccine Ontology uses SNOMED-CT terminology. If any concept is found with the same meaning with our ontology, this concept’s ConceptID is inserted as a property to the related class. The main idea is building an information base for vaccine information systems. Therefore, if there is an information system using SNOMED-CT vocabulary, this system could exchange health information with


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the vaccine information system which is based on Vaccine Ontology. We integrate user profiling method and policies with the Vaccine Ontology in order to improve the maintenance quality of the vaccine process. The developed ontology in this work is being used for the information base for national vaccination system in Turkey. From the beginning of the developing the model of the vaccine ontology, the aim is using this model as an information base for a software system. We are now developing the user interface of the vaccine information system by using Java and JSF technologies. Thus, it can be said that Vaccine Ontology will be a living ontology for the vaccine information system. As a future work, ICD-10 (International Statistical Classification of Diseases and Related Health Problems, http://www.who.int/classifications/icd/en/) codes about vaccination and allergic reactions that can occur after vaccination is going to be added into the Vaccine Ontology. ICD-10 is developed by WHO and it is a medical standardization to code diseases, signs and symptoms. We will also extend the policy concept as to achieve the management process of the vaccine information system. Acknowledgment. The medical part of Vaccine Ontology is supported by Prof. Dr. Fadil VARDAR, works in Department of Pediatric Infectious Diseases, Ege University.

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