[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82335-en":3,"doc-seo-82335-105":29,"detail-sidebar-cat-0-en-105":89},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},82335,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","SYNRARE: Synthetic Rare Disease EHR Generation for ML Benchmarking","SYNRARE addresses delayed rare-disease diagnosis by enabling machine-learning benchmarking on controlled synthetic EHR cohorts when real data access is limited by legal and privacy constraints. The approach uses synthetic data generation to create patients that differ from common-disease patients by a definable degree, supporting balanced evaluation under realistic technical conditions. A graphical user interface built on the Synthea framework simplifies modification and generation of synthetic EHRs for rare-disease ML testing.","Journal Title Here, YEAR, pp. 1–4 DOI added during production Published: Date added during production  \nPaper  \nSYNRARE: Synthetic Rare Disease EHR Generation for  \nML Benchmarking  \nNicolai Dinh Khang Truong1 and Richard R¨ottger1,∗  \n1 Department of Mathematics and Computer Science, University of Southern Denmark, Campusvej 55, 5230, Odense, Denmark  \n∗ Corresponding author. Department of Mathematics and Computer Science, University of Southern Denmark, Campusvej 55, 5230, Odense, Denmark. E-mail:  \n[roettger@imada.sdu.dk](roettger@imada.sdu.dk)  \nAbstract  \nMotivation: 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.  \nResults: 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.  \nAvailability and implementation: SYNRARE, including detailed instructions for installing, is available at  \n[https://gitlab.sdu.dk/screen4care/synrare](https://gitlab.sdu.dk/screen4care/synrare).  \nIntroduction  \nRare diseases are defined as conditions affecting 1 in 2,000 patients in the European Union (Valdez et al., 2016; Health, 2024) . Due to the low prevalence of these diseases, their symptomatological similarity to common diseases, and clinical unfamiliarity, patients undergo a diagnostic odyssey, taking several years to receive the final diagnosis (Valdez et al., 2016; Health, 2024; Dubief et al., 2024) . Machine Learning on Electronic Health Records (EHRs) can accelerate the diagnosis of patients with rare diseases and therefore help shorten the path to diagnosis. Recent examples demonstrate that applying Machine Learning (ML) algorithms alone to EHRs can identify rare diseases, thereby providing clinicians with the information necessary to perform targeted interventions (Huda et al., 2021; Cohen et al. , 2020; Wilson et al., 2023) . However, rare diseases generally posea challenge to ML approaches: We are normally presented with highly imbalanced datasets, resulting in increased false positives due to Bayes’ theorem. Tailored and specialized ML approaches have to be developed and tested in controlled environments to demonstrate their capabilities for the application in those cases. However, due to privacy and ethical concerns (Fecho et al. , 2022), access to extensive real-world data is limited and consequently poses a significant roadblock for research on the limitations and suitability of novel ML methods for EHR data of patients with rare diseases. Synthetic data helps overcome these barriers, allowing researchers  \nto conduct rapid benchmarking and evolution of the capabilities of their ML models in a controlled environment. For example, a research group can test the ability of a classifier or outlier detection method for the early detection of rare diseases and train and evaluate it on synthetic patient data under realistic circumstances before undergoing the diligent access procedure to finally access real-world data.  \nWe present SYNRARE, a GUI that leverages the Synthea framework (","cbCaiqf6fMk99Dy4","https://ap.wps.com/l/cbCaiqf6fMk99Dy4","pdf",614404,1,4,"English","en",105,"# Abstract\n# Motivation and Problem\n# Results: SYNRARE Overview\n# Availability and Implementation\n# Introduction\n## Background on Rare Diseases and EHRs\n## Machine Learning Challenges and Data Access Barriers\n## Synthetic Data as a Solution","[{\"question\":\"Why does rare disease diagnosis often take a long time, and how does the work relate to this problem?\",\"answer\":\"Rare disease diagnosis is frequently delayed because symptoms resemble those of more common disease variants. The paper links this challenge to machine-learning methods on EHRs and aims to support their development through controlled synthetic benchmarking data.\"},{\"question\":\"What is SYNRARE, and what does it enable for machine learning evaluation?\",\"answer\":\"SYNRARE is a GUI built on the Synthea framework that generates synthetic EHRs for rare disease patients. It enables benchmarking and testing of ML algorithms under controlled conditions by creating cohorts with a definable level of dissimilarity from majority-class patients.\"},{\"question\":\"How does SYNRARE handle the limitations caused by privacy and restricted access to real-world EHR data?\",\"answer\":\"The approach uses fully synthetic, rule-based patient data generated without relying on real EHR data. This helps researchers quickly run technical evaluations without undergoing the restrictive process required to access extensive real-world datasets.\"}]",1784179727,10,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":84,"head_meta":86,"extra_data":88,"updated_unix":27},"synrare-synthetic-rare-disease-ehr-generation-for-ml-benchmarking","",{"@graph":35,"@context":83},[36,52,66],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":21},"https://docshare.wps.com/document/synrare-synthetic-rare-disease-ehr-generation-for-ml-benchmarking/82335/",{"url":51,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"inLanguage":23,"description":14,"dateModified":60,"datePublished":60,"encodingFormat":59,"isAccessibleForFree":61,"interactionStatistic":62},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":40,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-07-16",true,{"@type":63,"interactionType":64,"userInteractionCount":4},"InteractionCounter",{"@type":65},"ViewAction",{"@type":67,"mainEntity":68},"FAQPage",[69,75,79],{"name":70,"@type":71,"acceptedAnswer":72},"Why does rare disease diagnosis often take a long time, and how does the work relate to this problem?","Question",{"text":73,"@type":74},"Rare disease diagnosis is frequently delayed because symptoms resemble those of more common disease variants. The paper links this challenge to machine-learning methods on EHRs and aims to support their development through controlled synthetic benchmarking data.","Answer",{"name":76,"@type":71,"acceptedAnswer":77},"What is SYNRARE, and what does it enable for machine learning evaluation?",{"text":78,"@type":74},"SYNRARE is a GUI built on the Synthea framework that generates synthetic EHRs for rare disease patients. It enables benchmarking and testing of ML algorithms under controlled conditions by creating cohorts with a definable level of dissimilarity from majority-class patients.",{"name":80,"@type":71,"acceptedAnswer":81},"How does SYNRARE handle the limitations caused by privacy and restricted access to real-world EHR data?",{"text":82,"@type":74},"The approach uses fully synthetic, rule-based patient data generated without relying on real EHR data. 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