Leveraging multi-site electronic health data for characterization of subtypes: a pilot study of dementia in the N3C Clinical Tenant

Objectives To provide a foundational methodology for differentiating comorbidity patterns in subphenotypes through investigation of a multi-site dementia patient dataset.Materials and Methods Employing the National Clinical Cohort Collaborative Tenant Pilot (N3C Clinical) dataset, our approach integrates machine learning algorithms-logistic regression and eXtreme Gradient Boosting (XGBoost)-with a diagnostic hierarchical model for nuanced classification of dementia subtypes based on comorbidities and gender. The methodology is enhanced by multi-site EHR data, implementing a hybrid sampling strategy combining 65% Synthetic Minority Over-sampling Technique (SMOTE), 35% Random Under-Sampling (RUS), and Tomek Links for class imbalance. The hierarchical model further refines the analysis, allowing for layered understanding of disease patterns.Results The study identified significant comorbidity patterns associated with diagnosis of Alzheimer's, Vascular, and Lewy Body dementia subtypes. The classification models achieved accuracies up to 69% for Alzheimer's/Vascular dementia and highlighted challenges in distinguishing Dementia with Lewy Bodies. The hierarchical model elucidates the complexity of diagnosing Dementia with Lewy Bodies and reveals the potential impact of regional clinical practices on dementia classification.Conclusion Our methodology underscores the importance of leveraging multi-site datasets and tailored sampling techniques for dementia research. This framework holds promise for extending to other disease subtypes, offering a pathway to more nuanced and generalizable insights into dementia and its complex interplay with comorbid conditions.Discussion This study underscores the critical role of multi-site data analyzes in understanding the relationship between comorbidities and disease subtypes. By utilizing diverse healthcare data, we emphasize the need to consider site-specific differences in clinical practices and patient demographics. Despite challenges like class imbalance and variability in EHR data, our findings highlight the essential contribution of multi-site data to developing accurate and generalizable models for disease classification. This study aims to enhance our understanding and classification of dementia subtypes using data from multiple healthcare sites. Dementia includes forms like Alzheimer's, Vascular, and Lewy Body dementia, each with unique health conditions. Researchers analyzed data from 9 US sites using a multi-stage approach with machine learning techniques, specifically logistic regression and eXtreme Gradient Boosting (XGBoost).The methodology involved 3 steps. First, the dataset was refined to focus on well-represented dementia subtypes. Next, advanced techniques balanced the data for fair representation. Finally, machine learning models classified the dementia types based on comorbidities and gender differences, achieving up to 70% accuracy for Alzheimer's and Vascular dementia, but finding Lewy Body dementia more challenging. A hierarchical model was used to address site-specific variations, revealing disparities among sites and improving generalization across populations.This study highlights the complexity of diagnosing dementia subtypes and the limitations of single-site studies, which often suffer from biases. By leveraging data from multiple sites, the research underscores the importance of multi-site dataset analysis for better generalization. This approach enhances understanding of dementia and provides a framework applicable to other diseases.