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paperarXivTrust 82 · PrimaryPublished 6d agoLive · 3d ago

SYNRARE: Synthetic Rare Disease EHR Generation for ML Benchmarking

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 generatin

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PossiblePossibly related (embedding) · 58%From virtual experiments to biomedical insight with synthetic data →
PossiblePossibly related (embedding) · 53%Using AI to help physicians diagnose rare genetic diseases affecting children →
PossiblePossibly related (embedding) · 52%rasinmuhammed/misata →
PossiblePossibly related (embedding) · 50%Machine learning-based prediction of E. coli infection in hospitalized patients using a no-code analytical framework - Nature →
PossiblePossibly related (embedding) · 48%Introducing GeneBench-Pro →
LinkedLinked via arxiv author · 85%Nicolai Dinh Khang Truong →
“SYNRARE: Synthetic Rare Disease EHR Generation for ML Benchmarking”
LinkedLinked via arxiv author · 85%Richard Röttger →
“SYNRARE: Synthetic Rare Disease EHR Generation for ML Benchmarking”
PossiblePossibly related (embedding) · 55%AI Model Predicts 348 Diseases from Electronic Health Record, Genetics - Inside Precision Medicine →

Covers

newsFrom virtual experiments to biomedical insight with synthetic data newsUsing AI to help physicians diagnose rare genetic diseases affecting children newsMachine learning-based prediction of E. coli infection in hospitalized patients using a no-code analytical framework - Nature newsIntroducing GeneBench-Pro

Implements

reporasinmuhammed/misata

authored (incoming)

personNicolai Dinh Khang Truong personRichard Röttger

Covers (incoming)

newsAI Model Predicts 348 Diseases from Electronic Health Record, Genetics - Inside Precision Medicine newsMachine Learning Simplifies Accurate LDL Cholesterol Calculation - Inside Precision Medicine newsMachine Learning Method Enables Easier and Wider Use of Martin-Hopkins Equation to Accurately Assess LDL Cholesterol Risk - Johns Hopkins Medicine newsData-efficient machine learning approach for predicting asthma attack risk - Nature newsPatient-Reported Outcomes, Machine Learning May Predict Breast Cancer Recurrence Earlier - Pharmacy Times

Related across the graph

newsMachine Learning Method Enables Easier and Wider Use of Martin-Hopkins Equation to Accurately Assess LDL Cholesterol Risk - Johns Hopkins Medicine newsData-efficient machine learning approach for predicting asthma attack risk - Nature personNicolai Dinh Khang Truong newsMachine learning-based prediction of E. coli infection in hospitalized patients using a no-code analytical framework - Nature newsFrom virtual experiments to biomedical insight with synthetic data personRichard Röttger newsUsing AI to help physicians diagnose rare genetic diseases affecting children newsAI Model Predicts 348 Diseases from Electronic Health Record, Genetics - Inside Precision Medicine reporasinmuhammed/misata newsPatient-Reported Outcomes, Machine Learning May Predict Breast Cancer Recurrence Earlier - Pharmacy Times newsIntroducing GeneBench-Pro newsMachine Learning Simplifies Accurate LDL Cholesterol Calculation - Inside Precision Medicine

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