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Summarizing Phenotype Evolution Patterns from Report Cases

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Abstract

The need to represent and manage time is implicit in several reasoning processes in medicine. However, this is predominantly obvious in the field of many neurodegenerative disorders, which are characterized by insidious onsets, progressive courses and variable combinations of clinical manifestations in each patient. Therefore, the availability of tools providing high level descriptions of the evolution of phenotype manifestations from patient data is crucial to promote early disease recognition and optimize the diagnostic process. Although many case reports published in the literature do not provide exhaustive temporal information except only key time references, such as disease onset, diagnosis or monitoring time, automatically comparing cases described by temporal clinical manifestation sequences can provide valuable knowledge about the data evolution. In this paper, we demonstrate the usefulness of representing patient case reports of a neurodegenerative disorder as a set of temporal clinical manifestations semantically annotated with a domain phenotype ontology and registered with a time-stamped value. Novel techniques are presented to query and match sets of different manifestation sequences from multiple patient cases, with the aim of automatically inferring phenotype evolution patterns of generic patients for clinical studies. The method was applied to 25 patient report cases from a Spanish study of the domain of cerebrotendinous xanthomatosis. Five evolution patterns were automatically generated to analyze the patient data. The results were evaluated against 49 relevant conclusions drawn from the study, with a precision of 93 % and a recall of 70 %.

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Acknowledgments

The work presented in this paper has been developed in the funded national project Gestión de Terminologías Médicas para Arquetipos (TIN2009-14159-C05-05) by the Ministerio de Educación y Ciencia.

Conflict of interest

The authors declare that they have no conflict of interest.

Author information

Authors and Affiliations

  1. Department of Electronics and Computer Science, University of Santiago de Compostela, 15782, Santiago de Compostela, Spain

    María Taboada & Verónica Álvarez

  2. Department of Applied Physics, University of Santiago de Compostela, Santiago de Compostela, Spain

    Diego Martínez

  3. Section of Neurology, Hospital del Sureste, Arganda del Rey, Madrid, Spain

    Belén Pilo

  4. Institut für Medizinische Genetik und Humangenetik, Charité - Universitätsmedizin Berlin, Berlin, Germany

    Peter N. Robinson

  5. Fundación Pública Galega de Medicina Xenómica, Santiago de Compostela. Center for Biomedical Research on Rare Diseases (CIBERER), Institute of Health Carlos III, Madrid, Spain

    María J. Sobrido

Authors
  1. María Taboada
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  2. Verónica Álvarez
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  3. Diego Martínez
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  4. Belén Pilo
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  5. Peter N. Robinson
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  6. María J. Sobrido
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Corresponding author

Correspondence to María Taboada.

Appendix A

Appendix A

Processing algorithm to generate a set of time points from patient cases.

Function obtainTP (SC, keyevents) returns TP

figure a

Online resources

The online resources and all information relevant to the paper is provided in following link: http://www.usc.es/keam/TempCTX/, which contains

Online resources 1 The graphical representation of the 5 evolution patterns automatically generated by the system.

Online resources 2 The 5 evolution patterns in detailed format.

Several diagrams showing the main hierarchies of the CTX phenotype management ontology.

- A description of the implemented application.

Implementation note

We used Protégé-OWL 3.4.8, Java 1.7.0, Pellet 3.4.8, the collection of Java APIs provided by the SWRL Tab, and Jess.

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Taboada, M., Álvarez, V., Martínez, D. et al. Summarizing Phenotype Evolution Patterns from Report Cases. J Med Syst 36 (Suppl 1), 25–36 (2012). https://doi.org/10.1007/s10916-012-9887-2

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  • DOI: https://doi.org/10.1007/s10916-012-9887-2

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