Predicting Alzheimer’s Diseases and Related Dementias in 3‐year timeframe with AI Foundation Model on Electronic Health Records

Background As disease‐modifying interventions advance, there is a critical need to detect Alzheimer’s disease and related dementias (ADRD) at the earlier, pre‐symptomatic stages. Transformer is a powerful model used to understand high‐dimensional data like images and languages. In this study, we propose a transformer‐based algorithm for predicting mild cognitive impairment (MCI) and ADRD 12 to 36 months in advance based on electronic health records (EHR). Method Our study analyzed EHR from NYU Langone between Jan 1 2014 and Jun 29 2022. The patient records were analyzed with sliding windows (Figure 1b): For every index date with at least one‐year lead‐time to onset, records from one preceding year were used as features, and the MCI/ADRD onset within 1‐3 years after the index date served as outcome labels. Patients with MCI/ADRD records in the feature and gap window are excluded to avoid data leakage. Our prediction framework included two stages ‐ pretraining and finetuning (Figure 1a, c, d). First, we pretrained a foundation Transformer‐based model (EHR‐BERT) using the full cohort, which enabled the model to understand the semantic meanings of variables in EHR. Then we finetuned the pretrained model with the MCI/ADRD outcome labels to identify the potential MCI/ADRD patients. Final predictive performance was evaluated on a fully held‐out validation cohort. Result Pretraining used EHRs from 1.98M patients; 947K records from 366K patients over 60, without preexisting MCI/ADRD, were used for finetuning in the downstream prediction task. The demographics are shown in Table 1. We evaluated the prediction performance on a held‐out validation set, consisting of 118,516 records from 46,082 patients. The model obtained AUROC at 0.761 [0.760, 0.762] for prediction in 0‐3 years and 0.740 [0.739, 0.742] in 1‐3 years. Figure 2 shows that EHR‐BERT performed better than existing algorithms in PPVs at the same sensitivity level. Conclusion In this study, our transformer‐based AI foundation model, trained on large‐scale electronic health records, demonstrated strong capability in predicting MCI/ADRD up to three years in advance. This algorithm will allow us to advance the current recruitment of dementia screening.