Computer-assisted initial diagnosis of rare diseases.
Computer-assisted initial diagnosis of rare diseases.
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作者：
Alves R;Piñol M;Vilaplana J;Teixidó I;Cruz J;Comas J;Vilaprinyo E;Sorribas A;Solsona F
导论.大多数记录在案的罕见疾病都有遗传起源。由于其个体频率低，基于表型症状的初步诊断并不总是容易的，因为从业者可能从未接触过患有相关疾病的患者。因此，重要的是要开发工具，促进临床医生对罕见疾病的初步诊断。在这项工作中，我们的目标是开发一种计算方法来帮助初步诊断。我们还旨在实现这种方法在一个用户友好的Web原型。我们称之为罕见疾病发现工具。最后，对样机进行了性能测试.方法. Rare Disease Discovery使用公开可用的ORPHANET数据集，根据患者的症状自动预测最可能的罕见疾病。我们应用该方法回顾性诊断了187例确诊的罕见病患者队列。随后，我们测试的精度，灵敏度，和不同的情况下，通过运行大规模的Monte Carlo模拟系统的全局性能。所有设置都考虑了在诊断中考虑不存在和/或不相关症状的情况。结果我们发现，该专家系统具有较高的诊断精度（≥80%）和灵敏度（≥99%），并对缺席和无关症状具有鲁棒性。讨论罕见病发现预测引擎似乎为罕见病的初步辅助鉴别诊断提供了一种快速而强大的方法。我们将此引擎与用户友好的Web界面相结合，可以在。整个项目的代码和最新数据库可以从下载。
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 . The code and most current database for the whole project can be downloaded from .