Front. Med. 2014, 8(3): 358–361 DOI 10.1007/s11684-014-0348-9
RESEARCH ARTICLE
Ontological reconstruction of the clinical terminology of traditional Chinese medicine ✉)1, Qi Xie1, Shusong Mao2, Zhiwei Cui2
Li Ma1,2, Baoyan Liu ( 1
China Academy of Chinese Medicine Sciences, Beijing 100700, China; 2 Hubei University of Chinese Medicine, Wuhan 430064, China
© Higher Education Press and Springer-Verlag Berlin Heidelberg 2014
Abstract This study proposes the ontological reconstruction of the current clinical terminology of traditional Chinese medicine (TCM). It also provides an overview of preliminary work related to the said reconstruction, including the ontology-based analysis of TCM clinical terminology. We conclude that the ontological reconstruction of TCM clinical terminology provides a proper translation from the idealized organizational model to real-world implementation and to a formalized, shared, and knowledge-based framework. Keywords
ontology; traditional Chinese medicine; clinical terminology
Introduction Recent advancements in computer technology have expanded the parameters of healthcare data collection services in the medical domain, such as network, artificial intelligence, and database management. However, different collection standards and system heterogeneity hinder data integration and reuse. Ontology development has become a research hotspot in medical informatics in recent years. Ontology can provide a welldefined basis for addressing the abovementioned problems. SNOMED CT and UMLS are well-known ontological methods or thesauri in the medical domain that fulfill the need of modeling the terms and making the terms explicit. The integrated clinical and research information system of traditional Chinese medicine (TCM) is now widely used in many TCM hospitals in China after more than 10 years of development. This system attempts to standardize data collection, transform large amounts of clinical data to analyzable and reusable knowledge, and fully integrate clinical practice and research [1]. The implementation of this system into clinical data management is effective. However, a gap still exists between real-world clinical practice and expected research outcome. In addition, the existing quality of clinical data needs to be further improved,
Received May 6, 2014; accepted June 27, 2014 Correspondence: [email protected]
and methods to reuse and share all these spotty data remain lacking. These conditions are the reasons why the current TCM clinical terminology needs to be reconstructed based on ontological methods. This paper provides an overview of preliminary work related to the ontological reconstruction of TCM clinical terminology: (1) a review of ontology development; (2) the role of TCM clinical terminology in current medical records; (3) the reason why the current clinical terminology should be evaluated based on ontological methods; (4) the necessity for the ontological reconstruction of the current TCM clinical terminology. Finally, we present an overall discussion and suggestion for future work.
Related works Review of ontology development Gruber stated that “an ontology is a formal, explicit specification of a shared conceptualization” [2]. This statement means that ontology presents a shared and formal understanding of major concepts and terms applied in a domain, as well as the relationships between these concepts. Formal means that ontology should be machine readable, and shared reflects the notion that ontology captures consensual knowledge. Several ontological methods are well known in the medical domain. These methods include SNOMED CT and UMLS, which are applied to medical information systems. Other ontological methods have also been applied
Li Ma et al.
to decision support processes in the medical domain. For example, Mark et al. [3] proposed the EON architecture for protocol-based therapy, which represents clinical protocols such as drug therapy in a precise and consistent structure. However, no case study on terminological ontological methods has been directly applied to medical data collection in the medical informatics domain. The four elements of ontology development are concepts (classes), subclasses, properties (slots), and associated relationships. The four common steps in domain ontology development are as follows. Step 1 involves specifying the ontology scope and objectives. Step 2 includes acquiring knowledge and identifying key concepts. Building/coding is a part of step 3. The last step refers to refinement based on the feedback of domain experts. Ontology development is a formalism widely used in knowledge engineering and artificial intelligence science, which represents knowledge related to a particular domain in a computer-readable manner. Ontological methods have been employed particularly in the medical domain to define a common terminology. For example, both ICD-11 and SNOMED subscribe to the principles of applied ontology; domain concepts and classes are described by logic rooted in an ontological framework [4]. Successful ontology development is characterized by the creation of a reliable mechanism for the collection, representation, and storage of domain knowledge. Ontology can increase the reusability and generality of developed healthcare software. Ontological methods are key components for building effective, distributed, knowledge-driven ehealthcare systems. These methods are needed to process and explain information content by presenting information to humans and by providing concepts with formal semantics. Overview of the current TCM clinical terminology The current TCM clinical terminology was developed on the basis of SNOMED CT, which is the most comprehensive clinical healthcare terminology worldwide [5]. This TCM terminology attempts to offer a standard and computerized representation of clinical concepts in TCM electric health records and lower the high variability of human language through a set of controlled terms for the TCM electric medical record. These unified and formal clinical concepts can then be shared and reused among different users. They should be utilized as a common knowledge base to assist practitioners in accessing formal medical concepts in practice and to facilitate mass medical data integration for management and analysis. The current terminology is typically large and complex, making it difficult to visualize and comprehend. Erroneous and inconsistent contents are unavoidable and are often too difficult to detect. A preliminary question is whether it is regarded as an ontology method. If an explicit principle on how the concepts of an ontological method are coded into the internal format of a database is available, this database can be
359
considered as an implementation of the method [6]. However, we cannot regard the current terminology as an ontological method based on the said criteria. Although this terminology requires the reconstitution of the hierarchy of concepts and set of interrelations between concepts, representing the terminology explicitly in an understandable and readable representation by a user is more important. Thus, the major motivation of this study is to reuse this current terminology linked to healthcare information systems after the ontological reconstruction. Ontology evaluation of the current TCM clinical terminology Ontology evaluation means the objective should be evaluated as if it is oriented to an ontological method. No standard approach exists for evaluating ontological methods from the perspective of their intrinsic or extrinsic characteristics. Ontology evaluation is performed to ensure that the oriented ontology adheres to the predefined standards to accurately represent the related domain it covers. Evaluating the current terminology as a basis of reconstruction and upgrade and determining whether it is suitable within the electrical medical record domain are imperative because of the extensive applicability of such a terminology. Brank et al. [7] categorized four main approaches for ontology evaluation, namely, the gold-standard evaluation, data-driven evaluation, evaluation by humans, and application-based evaluation. Yu et al. [8] also suggested two other main categories for ontology evaluation, namely, application-based and criteriabased evaluation. These approaches have some unavoidable deficiencies. No single approach can perfectly fit all the objectives of ontology evaluation. We selected a combination of criteria-based and application-based evaluation. We applied a set of ontology evaluation principles to measure ontology correctness and usefulness in terms of structure and content. These principles include clarity, consistency, extendibility, correctness, completeness, and minimal ontological commitment. Several obstacles hinder the hierarchical conceptualization and terminology navigation in evaluating the current terminology because no core concepts exist in this terminology. Clinicians also expressed that interacting with the current terminology is difficult. Therefore, the current terminology cannot be characterized by representing natural language and by transforming clinical concepts to knowledge. We also addressed one knowledge gap related to using ontological methods as components of clinical decision support systems. Ontological methods must be tested on the basis of a consistent logical framework and must be refined through large-scale applications in data annotation in advancing data integration and reuse [9]. The current terminology clearly does not meet the abovementioned requirements. We developed several artifacts to evaluate whether or not
360
the current terminology can be integrated into other related ontological methods such as ICD and SNOMED CT. We also proposed a method by comparing the hierarchy of the TCM terminology to ICD-11, which is a predefined gold standard ontological method. The overall performance of this matching would be expectedly poor because most of the matched concepts completely differ in meaning. Conversely, comparing similar or even identical concepts that are lexicalized with different concepts would possibly never lead to a match, unless a lexicon of synonyms is used. Necessity for reconstructing ontology-based TCM clinical terminology
Ontological reconstruction of the clinical terminology of TCM
The future terminology after the ontological reconstruction that should be used in the annotation of clinical data is a common approach for knowledge representation in support of clinical knowledge-based building research. As a high-quality ontological terminology, it can break down the barriers between different types of clinical information relevant to the understanding of diseases, particularly for TCM clinical information characterized by abstract and complicated concepts. The ontological terminology can facilitate the export of terms to the electric medical record to improve the quality of collecting data and reusing terms in formulating logical definitions.
Conclusions The concepts involved are intrinsically complex; the medical domain is a field that actively defines and uses ontological methods [10]. They mainly focus on the development of medical terminologies with clear and unambiguous concept representation. Examples of widely used medical ontological methods include GALEN, SNOMED CT, and UMLS. Most of these ontological methods focus on the terminological and taxonomical aspects of medical knowledge. However, healthcare institutions rarely share a common vocabulary of medical terms or the same encoding standard. Thus, integrating and reusing a large amount of distributed clinical information are complicated tasks for researchers. An increasing number of shared electronic resources of medical records within TCM hospital institutions have been developed in recent years, but effectively performing and leveraging resources are challenging for TCM researchers. Several sophisticated terminological requirements have been noted in the clinical and research information sharing projects of TCM. The most pivotal issue is the lack of a complete domain term coverage. Existing terminologies could not deal with various terms submitted by members of user communities because the development and validation of accurate definitions for a single term are not always a simple process. In addition, the need to supplement new terms may imply the need to create new sub-branches of new additional terms. However, ontology building is a long-term, time-consuming, and human labor-intensive process. Developing a new particular terminological ontology from scratch consumes large amounts of resources. Thus, we propose the idea of reconstructing the current TCM terminology based on ontology. After accomplishing the ontological terminology, a future ontological term model is designed to simplify the development of term dictionaries embedded in the TCM electric medical record. It can also contribute to the design of an extensible database schema to capture and manage data. Basic Formal Ontology (BFO) is implemented as the upper ontology to maximize the interoperability and reusability of our ontology. BFO provides a basic structured framework and is designed to support information retrieval, analysis, and integration in scientific and other domains.
The amount of computed TCM clinical data has exponentially increased as various EMR systems have been applied to clinical data capture. The first steps to effectively manage and reuse real-world clinical data and to transform data processing into sophisticated knowledge processing are to build the clinical terminological ontology and to formalize the data collection. Ontology is a powerful tool for computerizing medical knowledge formally. It is also a formal specification of TCM concepts and their interrelationships in data collection. The ontological reconstruction of TCM clinical terminology provides a proper translation from the idealized organizational model to real-world implementation and to a formalized, shared, and knowledge-based framework. Using the on-to-knowledge method to build an ontological terminology is proposed based on the knowledge engineering methodology. The future upgrade of TCM clinical terminology involves extracting conceptual knowledge from itself and mapping them semi-automatically into a common medical knowledge base. We propose that each core concept in the TCM clinical terminological ontology must correspond to exactly one class in ICD-11 in future works. How ICD-11 is imported into the future terminological ontology and how the class description in the terminological ontology can directly use all the entities (class/concepts, properties/relations) of ICD-11 should also be considered. We plan to verify the terminological ontology through automatic checking using the translational program dedicated to RDF and through manual checking by physicians in actual healthcare networks. The construction of a common semantic model should only contain general concepts and relationships common to all TCM diseases so that this model can be reused and extended by certain mechanisms (such as ontology import) for a particular disease. This study provides preliminary insights into the ontological reconstruction of TCM clinical terminology. Although much effort needs to be exerted to obtain this objective, we believe that we are heading in the right direction.
Li Ma et al.
Acknowledgements This study was supported by the National High Technology Research and Development Program of China (863 Program, No. 2012AA02A609).
Compliance with ethics guidelines Li Ma, Baoyan Liu, Qi Xie, Shusong Mao, and Zhiwei Cui declare that they have no conflicts of interest. This article does not contain any studies with human or animal subjects performed by any of the authors.
References 1. Liu BY, Xie Q, Shi HX. The organization management strategy for building real-world clinical research technology platform. J Tradit Chin Med (Zhong Yi Za Zhi) 2013; 54(24): 2071–2075 (in Chinese) 2. Valls A, Gibert K, Sánchez D, Batet M. Using ontologies for structuring organizational knowledge in Home Care assistance. Int J Med Inform 2010; 79(5): 370–387
361 3. Delir Haghighi P, Burstein F, Zaslavsky A, Arbon P. Development and evaluation of ontology for intelligent decision support in medical emergency management for mass gatherings. Decis Support Syst 2013; 54(2): 1192–1204 4. Schulz S. Competing interpretations of disorder codes in SNOMED CT and ICD. AMIA Annual Symposium Proceedings. American Medical Informatics Association, 2012 5. Schulz S, Cornet R, Spackman K. Consolidating SNOMED CT’s ontological commitment. Appl Ontol 2011; 6(1): 1–11 6. Dieng-Kuntz R, Minier D, Růzicka M, Corby F, Corby O, Alamarguy L. Building and using a medical ontology for knowledge management and cooperative work in a health care network. Comput Biol Med 2006; 36(7–8): 871–892 7. Brank J, Grobelnik M, Mladenic D. A Survey of Ontology Evaluation Techniques in Proceedings of Conference on Data Mining and Data Warehouses (SiKDD 2005). Ljubljana, Slovenia, 2005 8. Delir Haghighi P, Burstein F, Zaslavsky A, Arbon P. Development and evaluation of ontology for intelligent decision support in medical emergency management for mass gatherings. Decis Support Syst 2013; 54(2): 1192–1204 9. Smith B, Scheuermann RH. Ontologies for clinical and translational research: introduction. J Biomed Inform 2011; 44(1): 3–7 10. Pisanelly D. Ontologies in Medicine. IOS Press, 2004
RESEARCH ARTICLE
Ontological reconstruction of the clinical terminology of traditional Chinese medicine ✉)1, Qi Xie1, Shusong Mao2, Zhiwei Cui2
Li Ma1,2, Baoyan Liu ( 1
China Academy of Chinese Medicine Sciences, Beijing 100700, China; 2 Hubei University of Chinese Medicine, Wuhan 430064, China
© Higher Education Press and Springer-Verlag Berlin Heidelberg 2014
Abstract This study proposes the ontological reconstruction of the current clinical terminology of traditional Chinese medicine (TCM). It also provides an overview of preliminary work related to the said reconstruction, including the ontology-based analysis of TCM clinical terminology. We conclude that the ontological reconstruction of TCM clinical terminology provides a proper translation from the idealized organizational model to real-world implementation and to a formalized, shared, and knowledge-based framework. Keywords
ontology; traditional Chinese medicine; clinical terminology
Introduction Recent advancements in computer technology have expanded the parameters of healthcare data collection services in the medical domain, such as network, artificial intelligence, and database management. However, different collection standards and system heterogeneity hinder data integration and reuse. Ontology development has become a research hotspot in medical informatics in recent years. Ontology can provide a welldefined basis for addressing the abovementioned problems. SNOMED CT and UMLS are well-known ontological methods or thesauri in the medical domain that fulfill the need of modeling the terms and making the terms explicit. The integrated clinical and research information system of traditional Chinese medicine (TCM) is now widely used in many TCM hospitals in China after more than 10 years of development. This system attempts to standardize data collection, transform large amounts of clinical data to analyzable and reusable knowledge, and fully integrate clinical practice and research [1]. The implementation of this system into clinical data management is effective. However, a gap still exists between real-world clinical practice and expected research outcome. In addition, the existing quality of clinical data needs to be further improved,
Received May 6, 2014; accepted June 27, 2014 Correspondence: [email protected]
and methods to reuse and share all these spotty data remain lacking. These conditions are the reasons why the current TCM clinical terminology needs to be reconstructed based on ontological methods. This paper provides an overview of preliminary work related to the ontological reconstruction of TCM clinical terminology: (1) a review of ontology development; (2) the role of TCM clinical terminology in current medical records; (3) the reason why the current clinical terminology should be evaluated based on ontological methods; (4) the necessity for the ontological reconstruction of the current TCM clinical terminology. Finally, we present an overall discussion and suggestion for future work.
Related works Review of ontology development Gruber stated that “an ontology is a formal, explicit specification of a shared conceptualization” [2]. This statement means that ontology presents a shared and formal understanding of major concepts and terms applied in a domain, as well as the relationships between these concepts. Formal means that ontology should be machine readable, and shared reflects the notion that ontology captures consensual knowledge. Several ontological methods are well known in the medical domain. These methods include SNOMED CT and UMLS, which are applied to medical information systems. Other ontological methods have also been applied
Li Ma et al.
to decision support processes in the medical domain. For example, Mark et al. [3] proposed the EON architecture for protocol-based therapy, which represents clinical protocols such as drug therapy in a precise and consistent structure. However, no case study on terminological ontological methods has been directly applied to medical data collection in the medical informatics domain. The four elements of ontology development are concepts (classes), subclasses, properties (slots), and associated relationships. The four common steps in domain ontology development are as follows. Step 1 involves specifying the ontology scope and objectives. Step 2 includes acquiring knowledge and identifying key concepts. Building/coding is a part of step 3. The last step refers to refinement based on the feedback of domain experts. Ontology development is a formalism widely used in knowledge engineering and artificial intelligence science, which represents knowledge related to a particular domain in a computer-readable manner. Ontological methods have been employed particularly in the medical domain to define a common terminology. For example, both ICD-11 and SNOMED subscribe to the principles of applied ontology; domain concepts and classes are described by logic rooted in an ontological framework [4]. Successful ontology development is characterized by the creation of a reliable mechanism for the collection, representation, and storage of domain knowledge. Ontology can increase the reusability and generality of developed healthcare software. Ontological methods are key components for building effective, distributed, knowledge-driven ehealthcare systems. These methods are needed to process and explain information content by presenting information to humans and by providing concepts with formal semantics. Overview of the current TCM clinical terminology The current TCM clinical terminology was developed on the basis of SNOMED CT, which is the most comprehensive clinical healthcare terminology worldwide [5]. This TCM terminology attempts to offer a standard and computerized representation of clinical concepts in TCM electric health records and lower the high variability of human language through a set of controlled terms for the TCM electric medical record. These unified and formal clinical concepts can then be shared and reused among different users. They should be utilized as a common knowledge base to assist practitioners in accessing formal medical concepts in practice and to facilitate mass medical data integration for management and analysis. The current terminology is typically large and complex, making it difficult to visualize and comprehend. Erroneous and inconsistent contents are unavoidable and are often too difficult to detect. A preliminary question is whether it is regarded as an ontology method. If an explicit principle on how the concepts of an ontological method are coded into the internal format of a database is available, this database can be
359
considered as an implementation of the method [6]. However, we cannot regard the current terminology as an ontological method based on the said criteria. Although this terminology requires the reconstitution of the hierarchy of concepts and set of interrelations between concepts, representing the terminology explicitly in an understandable and readable representation by a user is more important. Thus, the major motivation of this study is to reuse this current terminology linked to healthcare information systems after the ontological reconstruction. Ontology evaluation of the current TCM clinical terminology Ontology evaluation means the objective should be evaluated as if it is oriented to an ontological method. No standard approach exists for evaluating ontological methods from the perspective of their intrinsic or extrinsic characteristics. Ontology evaluation is performed to ensure that the oriented ontology adheres to the predefined standards to accurately represent the related domain it covers. Evaluating the current terminology as a basis of reconstruction and upgrade and determining whether it is suitable within the electrical medical record domain are imperative because of the extensive applicability of such a terminology. Brank et al. [7] categorized four main approaches for ontology evaluation, namely, the gold-standard evaluation, data-driven evaluation, evaluation by humans, and application-based evaluation. Yu et al. [8] also suggested two other main categories for ontology evaluation, namely, application-based and criteriabased evaluation. These approaches have some unavoidable deficiencies. No single approach can perfectly fit all the objectives of ontology evaluation. We selected a combination of criteria-based and application-based evaluation. We applied a set of ontology evaluation principles to measure ontology correctness and usefulness in terms of structure and content. These principles include clarity, consistency, extendibility, correctness, completeness, and minimal ontological commitment. Several obstacles hinder the hierarchical conceptualization and terminology navigation in evaluating the current terminology because no core concepts exist in this terminology. Clinicians also expressed that interacting with the current terminology is difficult. Therefore, the current terminology cannot be characterized by representing natural language and by transforming clinical concepts to knowledge. We also addressed one knowledge gap related to using ontological methods as components of clinical decision support systems. Ontological methods must be tested on the basis of a consistent logical framework and must be refined through large-scale applications in data annotation in advancing data integration and reuse [9]. The current terminology clearly does not meet the abovementioned requirements. We developed several artifacts to evaluate whether or not
360
the current terminology can be integrated into other related ontological methods such as ICD and SNOMED CT. We also proposed a method by comparing the hierarchy of the TCM terminology to ICD-11, which is a predefined gold standard ontological method. The overall performance of this matching would be expectedly poor because most of the matched concepts completely differ in meaning. Conversely, comparing similar or even identical concepts that are lexicalized with different concepts would possibly never lead to a match, unless a lexicon of synonyms is used. Necessity for reconstructing ontology-based TCM clinical terminology
Ontological reconstruction of the clinical terminology of TCM
The future terminology after the ontological reconstruction that should be used in the annotation of clinical data is a common approach for knowledge representation in support of clinical knowledge-based building research. As a high-quality ontological terminology, it can break down the barriers between different types of clinical information relevant to the understanding of diseases, particularly for TCM clinical information characterized by abstract and complicated concepts. The ontological terminology can facilitate the export of terms to the electric medical record to improve the quality of collecting data and reusing terms in formulating logical definitions.
Conclusions The concepts involved are intrinsically complex; the medical domain is a field that actively defines and uses ontological methods [10]. They mainly focus on the development of medical terminologies with clear and unambiguous concept representation. Examples of widely used medical ontological methods include GALEN, SNOMED CT, and UMLS. Most of these ontological methods focus on the terminological and taxonomical aspects of medical knowledge. However, healthcare institutions rarely share a common vocabulary of medical terms or the same encoding standard. Thus, integrating and reusing a large amount of distributed clinical information are complicated tasks for researchers. An increasing number of shared electronic resources of medical records within TCM hospital institutions have been developed in recent years, but effectively performing and leveraging resources are challenging for TCM researchers. Several sophisticated terminological requirements have been noted in the clinical and research information sharing projects of TCM. The most pivotal issue is the lack of a complete domain term coverage. Existing terminologies could not deal with various terms submitted by members of user communities because the development and validation of accurate definitions for a single term are not always a simple process. In addition, the need to supplement new terms may imply the need to create new sub-branches of new additional terms. However, ontology building is a long-term, time-consuming, and human labor-intensive process. Developing a new particular terminological ontology from scratch consumes large amounts of resources. Thus, we propose the idea of reconstructing the current TCM terminology based on ontology. After accomplishing the ontological terminology, a future ontological term model is designed to simplify the development of term dictionaries embedded in the TCM electric medical record. It can also contribute to the design of an extensible database schema to capture and manage data. Basic Formal Ontology (BFO) is implemented as the upper ontology to maximize the interoperability and reusability of our ontology. BFO provides a basic structured framework and is designed to support information retrieval, analysis, and integration in scientific and other domains.
The amount of computed TCM clinical data has exponentially increased as various EMR systems have been applied to clinical data capture. The first steps to effectively manage and reuse real-world clinical data and to transform data processing into sophisticated knowledge processing are to build the clinical terminological ontology and to formalize the data collection. Ontology is a powerful tool for computerizing medical knowledge formally. It is also a formal specification of TCM concepts and their interrelationships in data collection. The ontological reconstruction of TCM clinical terminology provides a proper translation from the idealized organizational model to real-world implementation and to a formalized, shared, and knowledge-based framework. Using the on-to-knowledge method to build an ontological terminology is proposed based on the knowledge engineering methodology. The future upgrade of TCM clinical terminology involves extracting conceptual knowledge from itself and mapping them semi-automatically into a common medical knowledge base. We propose that each core concept in the TCM clinical terminological ontology must correspond to exactly one class in ICD-11 in future works. How ICD-11 is imported into the future terminological ontology and how the class description in the terminological ontology can directly use all the entities (class/concepts, properties/relations) of ICD-11 should also be considered. We plan to verify the terminological ontology through automatic checking using the translational program dedicated to RDF and through manual checking by physicians in actual healthcare networks. The construction of a common semantic model should only contain general concepts and relationships common to all TCM diseases so that this model can be reused and extended by certain mechanisms (such as ontology import) for a particular disease. This study provides preliminary insights into the ontological reconstruction of TCM clinical terminology. Although much effort needs to be exerted to obtain this objective, we believe that we are heading in the right direction.
Li Ma et al.
Acknowledgements This study was supported by the National High Technology Research and Development Program of China (863 Program, No. 2012AA02A609).
Compliance with ethics guidelines Li Ma, Baoyan Liu, Qi Xie, Shusong Mao, and Zhiwei Cui declare that they have no conflicts of interest. This article does not contain any studies with human or animal subjects performed by any of the authors.
References 1. Liu BY, Xie Q, Shi HX. The organization management strategy for building real-world clinical research technology platform. J Tradit Chin Med (Zhong Yi Za Zhi) 2013; 54(24): 2071–2075 (in Chinese) 2. Valls A, Gibert K, Sánchez D, Batet M. Using ontologies for structuring organizational knowledge in Home Care assistance. Int J Med Inform 2010; 79(5): 370–387
361 3. Delir Haghighi P, Burstein F, Zaslavsky A, Arbon P. Development and evaluation of ontology for intelligent decision support in medical emergency management for mass gatherings. Decis Support Syst 2013; 54(2): 1192–1204 4. Schulz S. Competing interpretations of disorder codes in SNOMED CT and ICD. AMIA Annual Symposium Proceedings. American Medical Informatics Association, 2012 5. Schulz S, Cornet R, Spackman K. Consolidating SNOMED CT’s ontological commitment. Appl Ontol 2011; 6(1): 1–11 6. Dieng-Kuntz R, Minier D, Růzicka M, Corby F, Corby O, Alamarguy L. Building and using a medical ontology for knowledge management and cooperative work in a health care network. Comput Biol Med 2006; 36(7–8): 871–892 7. Brank J, Grobelnik M, Mladenic D. A Survey of Ontology Evaluation Techniques in Proceedings of Conference on Data Mining and Data Warehouses (SiKDD 2005). Ljubljana, Slovenia, 2005 8. Delir Haghighi P, Burstein F, Zaslavsky A, Arbon P. Development and evaluation of ontology for intelligent decision support in medical emergency management for mass gatherings. Decis Support Syst 2013; 54(2): 1192–1204 9. Smith B, Scheuermann RH. Ontologies for clinical and translational research: introduction. J Biomed Inform 2011; 44(1): 3–7 10. Pisanelly D. Ontologies in Medicine. IOS Press, 2004
