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Computer-assisted initial diagnosis of rare diseases.

Rui Alves1, Marc Piñol2, Jordi Vilaplana3

  • 1Departament de Cienciès Mèdiques Bàsiques, Universitat de Lleida, Lleida, Catalunya, Spain; IRBLleida, Lleida, Catalunya, Spain.

Peerj
|August 23, 2016
PubMed
Summary

This study introduces Rare Disease Discovery, a computational tool aiding clinicians in diagnosing rare diseases based on symptoms. The system demonstrates high precision and sensitivity, proving robust even with incomplete patient data.

Keywords:
Computer assisted diagnosisFamily doctorsRare diseasesUser-friendly webservereHealth

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Area of Science:

  • Medical Informatics
  • Genetics
  • Computational Biology

Background:

  • Rare diseases, often genetic, pose diagnostic challenges due to low prevalence.
  • Phenotypic symptom-based diagnosis can be difficult for clinicians unfamiliar with specific rare conditions.

Purpose of the Study:

  • To develop a computational approach for symptom-based rare disease diagnosis.
  • To implement this approach in a user-friendly web prototype called Rare Disease Discovery.
  • To evaluate the diagnostic performance of the developed system.

Main Methods:

  • Utilized the ORPHANET dataset linking rare diseases and symptoms.
  • Developed an expert system for predicting rare diseases from patient symptoms.
  • Validated the system on 187 retrospectively diagnosed rare disease patients.
  • Conducted large-scale Monte Carlo simulations to assess performance under various conditions, including absent/unrelated symptoms.

Main Results:

  • The Rare Disease Discovery system achieved high diagnostic precision (≥80%) and sensitivity (≥99%).
  • The system demonstrated robustness against absent and unrelated symptoms.
  • The computational engine provides a fast and reliable method for initial differential diagnosis.

Conclusions:

  • Rare Disease Discovery offers a valuable tool for assisted differential diagnosis of rare diseases.
  • The system is accessible via a web interface and its code/database are publicly available.
  • The tool can significantly aid clinicians in identifying rare diseases earlier and more accurately.