title: Computer-assisted initial diagnosis of rare diseases.
abstract: 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.
authors.affiliation: Departament de Cienciès Mèdiques Bàsiques, Universitat de Lleida, Lleida, Catalunya, Spain; IRBLleida, Lleida, Catalunya, Spain.
authors.forename: Rui
authors.initials: R
authors.lastname: Alves
authors.affiliation: Departament d'Informàtica i Enginyeria Industrial, Universitat de Lleida , Lleida , Catalunya , Spain.
authors.forename: Marc
authors.initials: M
authors.lastname: Piñol
authors.affiliation: Departament d'Informàtica i Enginyeria Industrial, Universitat de Lleida, Lleida, Catalunya, Spain; INSPIRES, Lleida, Catalunya, Spain.
authors.forename: Jordi
authors.initials: J
authors.lastname: Vilaplana
authors.affiliation: Departament d'Informàtica i Enginyeria Industrial, Universitat de Lleida, Lleida, Catalunya, Spain; INSPIRES, Lleida, Catalunya, Spain.
authors.forename: Ivan
authors.initials: I
authors.lastname: Teixidó
authors.affiliation: Departament de Cienciès Mèdiques Bàsiques, Universitat de Lleida, Lleida, Catalunya, Spain; IRBLleida, Lleida, Catalunya, Spain.
authors.forename: Joaquim
authors.initials: J
authors.lastname: Cruz
authors.affiliation: Departament de Cienciès Mèdiques Bàsiques, Universitat de Lleida, Lleida, Catalunya, Spain; IRBLleida, Lleida, Catalunya, Spain; Departament d'Informàtica i Enginyeria Industrial, Universitat de Lleida, Lleida, Catalunya, Spain; INSPIRES, Lleida, Catalunya, Spain.
authors.forename: Jorge
authors.initials: J
authors.lastname: Comas
authors.affiliation: Departament de Cienciès Mèdiques Bàsiques, Universitat de Lleida, Lleida, Catalunya, Spain; IRBLleida, Lleida, Catalunya, Spain.
authors.forename: Ester
authors.initials: E
authors.lastname: Vilaprinyo
authors.affiliation: Departament de Cienciès Mèdiques Bàsiques, Universitat de Lleida, Lleida, Catalunya, Spain; IRBLleida, Lleida, Catalunya, Spain.
authors.forename: Albert
authors.initials: A
authors.lastname: Sorribas
authors.affiliation: Departament d'Informàtica i Enginyeria Industrial, Universitat de Lleida, Lleida, Catalunya, Spain; INSPIRES, Lleida, Catalunya, Spain.
authors.forename: Francesc
authors.initials: F
authors.lastname: Solsona
keywords: Computer assisted diagnosis; Family doctors; Rare diseases; User-friendly webserver; eHealth