发表机构
University of Southern Denmark（南丹麦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对罕见病诊断因症状相似易延迟且机器学习算法应用受法律隐私限制的问题，提出基于Synthea框架的图形用户界面SYNRARE，可生成与常见疾病患者有可定义差异的合成电子健康记录，用于机器学习算法基准测试。
AI 中文摘要
动机：由于症状与常见疾病变体相似，罕见病（RD）诊断常常延迟。应用于电子健康记录的机器学习算法有望加速诊断，但法律和隐私问题构成重大障碍。合成数据生成是获取电子健康记录的替代方法，可用于任何机器学习算法的基准测试和开发。然而，现有的合成数据生成算法往往缺乏对生成与大多数患者有可定义程度差异的患者子集以模拟RD患者的支持。结果：我们提出了SYNRARE，这是一个基于Synthea框架的图形用户界面，能更轻松地修改和生成RD患者的合成电子健康记录，这些记录与常见疾病患者仅有可定义程度的差异，从而能在可控技术条件下对算法进行基准测试和测试。SYNRARE使研究人员能够在任何场景下快速对其机器学习算法进行基准测试。可用性和实现：SYNRARE可通过此https URL获取，包括详细的安装说明。
英文摘要
Motivation: Rare disease (RD) diagnosis is frequently delayed due to the similarities in symptoms to common disease variants. Machine Learning Algorithms applied to Electronic Health Records show promise for accelerating the diagnosis; however, legal and privacy concerns pose significant barriers. To address these issues, Synthetic Data Generation is an alternative method for obtaining Electronic Health Records and can be applied with any Machine Learning algorithm for benchmarking and development purposes. Despite the availability of Synthetic Data Generation algorithms, support for generating a subset of patients that differ in a definable degree from the majority to simulate patients with RD is often lacking. Results: We present SYNRARE, a graphical user interface based on the Synthea framework that enables easier modification and generation of synthetic Electronic Health Records of RD patients, which differ only to a definable degree from patients with common diseases, thereby enabling the benchmarking and testing of algorithms under controlled technical conditions. SYNRARE enables researchers to rapidly benchmark their Machine Learning algorithms across any scenario. Availability and implementation: SYNRARE, including detailed instructions for installing, is available at https://gitlab.sdu.dk/screen4care/synrare.
CommentsIntended for submission to the Application Notes Bioinformatics Journal
