Automated Diagnosis Through Ontologies and Logical Descriptions

Colomo‐Palacios, Ricardo; García-Crespo, Ángel; Gómez-Berbís, Juan Miguel; Rodríguez‐González, Alejandro; Gayo, José Emilio Labra; Alor-Hernández, Giner

doi:10.4018/jdsst.2011010102

Cited by 6 publications

(3 citation statements)

References 39 publications

(43 reference statements)

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“…Nevertheless, few systems address the problem of multilevel diagnosis. Of those that do, the ADONIS approach [26] presents a partial solution to the multilevel diagnosis problem based on the use of description logics, with interesting results but several limitations of the diagnosis process such as not being able to perform normal diagnosis because of the modifications done to perform multilevel diagnosis. The SEDELO approach [27] is similar to ADONIS in that it offers solutions based on description logics, but puts a special emphasis on measurement of the temporal efficiency, as well as addressing the aforementioned limitations of ADONIS.…”

Section: State Of the Artmentioning

confidence: 99%

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Analysis of a Multilevel Diagnosis Decision Support System and Its Implications: A Case Study

Rodríguez-González

Torres-Niño

Mayer

et al. 2012

Computational and Mathematical Methods in Medicine

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Medical diagnosis can be performed in an automatic way with the use of computer-based systems or algorithms. Such systems are usually called diagnostic decision support systems (DDSSs) or medical diagnosis systems (MDSs). An evaluation of the performance of a DDSS called ML-DDSS has been performed in this paper. The methodology is based on clinical case resolution performed by physicians which is then used to evaluate the behavior of ML-DDSS. This methodology allows the calculation of values for several well-known metrics such as precision, recall, accuracy, specificity, and Matthews correlation coefficient (MCC). Analysis of the behavior of ML-DDSS reveals interesting results about the behavior of the system and of the physicians who took part in the evaluation process. Global results show how the ML-DDSS system would have significant utility if used in medical practice. The MCC metric reveals an improvement of about 30% in comparison with the experts, and with respect to sensitivity the system returns better results than the experts.

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Section: State Of the Artmentioning

confidence: 99%

“…While other data elements such as clinical history are certainly relevant to the diagnosis model, these three items—findings (symptoms and signs), tests, and drugs—are considered particularly relevant for performing accurate diagnosis [33]. The internal behavior of the system has been previously explained and discussed [27, 33, 34]. …”

Section: Ml-ddssmentioning

confidence: 99%

Analysis of a Multilevel Diagnosis Decision Support System and Its Implications: A Case Study

Rodríguez-González

Torres-Niño

Mayer

et al. 2012

Computational and Mathematical Methods in Medicine

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“…Examples of the interaction of semantic technologies and MDSS can be found in the recent literature (e.g. García-Crespo et al, 2010;Rodríguez-González et al, 2011b;Splendiani et al, 2011).…”

Section: Related Workmentioning

confidence: 99%

Towards an ontology for psychological disorders

Rodríguez-González

Rodríguez

Casado‐Lumbreras

et al. 2012

IJMSO

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Psychological diagnosis is the process by which mental health professionals determine if problems that affect a person meet all specific criteria for a psychological disorder. In the last years decision support systems (DSS) have helped practitioners in the field of psychological clinical diagnosis with notable results. Given that ontologies are created, among other goals, to allow different sorts of formal reasoning, they are seen as valid artifacts to support a new generation of DSS in mental health professionals. Although in recent years some initiatives have emerged in order to build realist ontologies of mental diseases, this field presents a remarkable heterogeneity of data and two different clinical classification systems that is why authors present an ontology with the aim of reusing existing works and serving as the key element of a clinical DSS in the field of mental disorders.

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SeDeLo: Using Semantics and Description Logics to Support Aided Clinical Diagnosis

et al. 2011

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Automated medical diagnosis systems based on knowledge-oriented descriptions have gained momentum with the emergence of semantic descriptions. The objective of this paper is to propose a normalized design that solves some of the problems which have been detected by authors in previous tools. The authors bring together two different technologies to develop a new clinical decision support system: description logics aimed at developing inference systems to improve decision support for the prevention, treatment and management of illness and semantic technologies. Because of its new design, the system is capable of obtaining improved diagnostics compared with previous efforts. However, this evaluation is more focused in the computational performance, giving as result that description logics is a good solution with small data sets. In this paper, we provide a well-structured ontology for automated diagnosis in the medical field and a three-fold formalization based on Description Logics with the use of Semantic Web technologies.

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Automated Diagnosis Through Ontologies and Logical Descriptions

Cited by 6 publications

References 39 publications

Analysis of a Multilevel Diagnosis Decision Support System and Its Implications: A Case Study

Analysis of a Multilevel Diagnosis Decision Support System and Its Implications: A Case Study

Towards an ontology for psychological disorders

SeDeLo: Using Semantics and Description Logics to Support Aided Clinical Diagnosis

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