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Título:
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Computer-assisted initial diagnosis of rare diseases
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Autor/a:
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Alves, Rui; Piñol, Marc; Vilaplana Mayoral, Jordi; Teixidó Torrelles, Ivan; Cruz, Joaquim; Comas, Jorge; Vilaprinyo Terré, Ester; Sorribas Tello, Albert; Solsona Tehàs, Francesc
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Notas:
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Introduction. Most documented rare diseases have genetic origin. Because of their low individual frequency, an initial diagnosis based on phenotypic symptoms is not always easy, as practitioners might never have been exposed to patients suffering from the relevant disease. It is thus important to develop tools that facilitate symptom-based initial diagnosis of rare diseases by clinicians. In this work we aimed at developing a computational approach to aid in that initial diagnosis. We also aimed at implementing this approach in a user friendly web prototype. We call this tool Rare Disease Discovery. Finally, we also aimed at testing the performance of the prototype.
Methods. Rare Disease Discovery uses the publicly available ORPHANET data set of association between rare diseases and their symptoms to automatically predict the most likely rare diseases based on a patient’s symptoms. We apply the method to retrospectively diagnose a cohort of 187 rare disease patients with confirmed diagnosis. Subsequently we test the precision, sensitivity, and global performance of the system under different scenarios by running large scale Monte Carlo simulations. All settings account for situations where absent and/or unrelated symptoms are considered in the diagnosis.
Results. We find that this expert system has high diagnostic precision (≥80%) and sensitivity (≥99%), and is robust to both absent and unrelated symptoms.
Discussion. The Rare Disease Discovery prediction engine appears to provide a fast and robust method for initial assisted differential diagnosis of rare diseases. We coupled this engine with a user-friendly web interface and it can be freely accessed at http://disease-discovery.udl.cat/. The code and most current database for the whole project can be downloaded from https://github.com/Wrrzag/DiseaseDiscovery/tree/no_classifiers.
This work was partially supported by the MEyC under contracts TIN2014-53234-C2- 2-R, TIN2011-28689-C02-02 and BFU2010-17704 and by Universitat de Lleida and Departament de Ciències Mèdiques Bàsiques with bridge grants to RA. The authors are members of the research groups 2014-SGR163 and 2014-SGR243, funded by the Generalitat de Catalunya. |
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Materia(s):
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-Computer assisted diagnosis
-Rare diseases
-eHealth
-Family doctors
-User-friendly webserver |
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Derechos:
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cc-by, (c) Alves et al., 2016
http://creativecommons.org/licenses/by/3.0/es/
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Tipo de documento:
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article
publishedVersion |
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Editor:
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PeerJ
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Compartir:
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