In this study,we present a Transformer-based encoder model to predict Alzheimer’s Disease(AD)progression from longitudinal multi-modal patient data.Our model,Longitudinal Survival Model for AD(LSM-AD),leverages rich ...In this study,we present a Transformer-based encoder model to predict Alzheimer’s Disease(AD)progression from longitudinal multi-modal patient data.Our model,Longitudinal Survival Model for AD(LSM-AD),leverages rich temporal patterns present in sequences of patient visits,integrating multi-modal data,such as cognitive assessments and Magnetic Resonance Imaging(MRI)biomarkers to compute accurate diagnostic predictions.We conduct an empirical evaluation across two patient groups—Cognitively Normal(CN)individuals and those with Mild Cognitive Impairment(MCI)—tracking their progression for up to five follow-up years.Our results indicate that incorporating longer patient histories can yield superior performance compared to relying solely on a single visit,emphasizing the importance of historical context in improving predictive accuracy.Additionally,we show that the choice of the prediction head,training loss function and method for handling input missingness can significantly impact the quality of predictions.Notably,LSM-AD can improve Area Under the Receiver Operating Characteristic(AUROC)curve by up to 15%over previous state-of-the-art,when MRI biomarkers serve as the sole longitudinal feature.Our findings reinforce the value of multi-modal longitudinal data in evaluating patients,demonstrating its potential to improve early detection and monitoring of AD progression.Our code is available at https://github.com/batuhankmkaraman/LSM-AD.展开更多
基金funded by National Institutes of Health of USA(NIH)(Nos.R01AG053949 and R01MH130899)National Science Foundation(NSF)CAREER of USA(No.1748377).
文摘In this study,we present a Transformer-based encoder model to predict Alzheimer’s Disease(AD)progression from longitudinal multi-modal patient data.Our model,Longitudinal Survival Model for AD(LSM-AD),leverages rich temporal patterns present in sequences of patient visits,integrating multi-modal data,such as cognitive assessments and Magnetic Resonance Imaging(MRI)biomarkers to compute accurate diagnostic predictions.We conduct an empirical evaluation across two patient groups—Cognitively Normal(CN)individuals and those with Mild Cognitive Impairment(MCI)—tracking their progression for up to five follow-up years.Our results indicate that incorporating longer patient histories can yield superior performance compared to relying solely on a single visit,emphasizing the importance of historical context in improving predictive accuracy.Additionally,we show that the choice of the prediction head,training loss function and method for handling input missingness can significantly impact the quality of predictions.Notably,LSM-AD can improve Area Under the Receiver Operating Characteristic(AUROC)curve by up to 15%over previous state-of-the-art,when MRI biomarkers serve as the sole longitudinal feature.Our findings reinforce the value of multi-modal longitudinal data in evaluating patients,demonstrating its potential to improve early detection and monitoring of AD progression.Our code is available at https://github.com/batuhankmkaraman/LSM-AD.
