SYNRARE is 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.
Abstract
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.
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The early and accurate detection and diagnosis of diseases play a crucial role in impeding disease progression and guiding appropriate treatment selection. Complex diseases such as autoimmune diseases (ADs), often present nonspecific symptoms that can overlap also with other conditions, leading to misdiagnosis. Medical software, known as clinical decision support systems (CDSS), is used by clinicians to categorize patients based on specific criteria. However, existing CDSS implementations, generally developed within individual hospitals, are often limited to specific data types, and reflect questionnaires that do not take advantage of machine learning (ML) methods for detection and prediction of complex patterns.
To address this gap, we developed Personalis as a proof-of-concept medical software. Personalis integrates various modalities of medical datasets and applies ML models to target hospital datasets and disease-target specific prediction tasks within a unified software environment. The platform has been run with real-world data to investigate the potential of this proof-of-concept medical software in providing early clinical support for the personalized prediction of specific autoimmune diseases in individual patients. These intermediate results have been implemented to redesign the front-end to enhance user-experience, and serve the conveyed clinical needs and expectations.
Personalis is a proof-of-concept medical software that demonstrated the feasibility of integrating various data modalities and machine learning methods to support clinicians in the diagnosis, treatment or management of individual patients with autoimmune diseases. By leveraging ML methods, Personalis provided prediction with a certain accuracy of a specific autoimmune disease for an individual patient, highlighting factors that contributed to the prediction results. Explainable machine learning made the models’ decisions understandable to humans, and the resulting factors were further used to provide additional clinical insights beyond the disease prediction accuracy metric. The platform was designed considering user-experience and human factors to enable actionable clinical practical adoption. Its results may provide hints on the prognosis assessment by selecting a specific patient record.
Personalis provides a proof-of-concept medical software for integrating heterogeneous clinical data with configurable ML-based prediction and interpretation workflows for autoimmune diseases. It offers a mechanistic overview of the parameters that mostly influence the prediction of the machine learning models, therefore providing interpretable results and mechanistic insights essential to model transparency. The potential of the medical software Personalis is be extended to several diseases, and support in cases of challenging differential diagnoses.
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