Leverage Real-world Longitudinal Data in Large Clinical Research Networks for Alzheimer's Disease and Related Dementia (ADRD)
Duan, R.; Chen, Z.; Tong, J.; Luo, C.; Lyu, T.; Tao, C.; Maraganore, D.; Bian, J.; Chen, Y.
Show abstract
With vast amounts of patients medical information, electronic health records (EHRs) are becoming one of the most important data sources in biomedical and health care research. Effectively integrating data from multiple clinical sites can help provide more generalized real-world evidence that is clinically meaningful. To analyze the clinical data from multiple sites, distributed algorithms are developed to protect patient privacy without sharing individual-level medical information. In this paper, we applied the One-shot Distributed Algorithm for Cox proportional hazard model (ODAC) to the longitudinal data from the OneFlorida Clinical Research Consortium to demonstrate the feasibility of implementing the distributed algorithms in large research networks. We studied the associations between the clinical risk factors and Alzheimers disease and related dementia (ADRD) onsets to advance clinical research on our understanding of the complex risk factors of ADRD and ultimately improve the care of ADRD patients.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Practical Strategies for Extreme Missing Data Imputation in Dementia Diagnosis 92%
- GREMI: an Explainable Multi-omics Integration Framework for Enhanced Disease Prediction and Module Identification 92%
- BertNDA: a Model Based on Graph-Bert and Multi-scale Information Fusion for ncRNA-disease Association Prediction 91%
Similar papers in this journal
- Identification of functionally connected multi-omic biomarkers for Alzheimer’s Disease using modularity-constrained Lasso 92%
- Regional medical inter-institutional cooperation in medical provider network constructed using patient claims data from Japan 92%
- Interpretable multivariate survival models: Improving predictions for conversion from mild cognitive impairment to Alzheimers disease (AD) via data fusion and machine learning 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.