Authors - Yijun Shao, Ying Yin, Debby Tsuang, Phillip Ma, Edward Zamrini, Ali Ahmed, Charles Faselis, Katherine Wilson, Karl Brown, Qing Zeng-Treitler Abstract - This study involved the development and evaluation of a novel Deep Neural Network (DNN) model for Alzheimer's disease and related dementias (ADRD) phenotyping. The model was initially trained on a large cohort of 100,000 cases and controls and subsequently fine-tuned using a smaller, expert reviewed dataset of 1,200 individuals. The final fine-tuned model achieved an Area Under the Receiver Operating Characteristic curve (AUC) of 0.832. For further validation, the model's predictive capability was assessed in a separate randomly selected patient cohort comprising individuals without an ADRD diagnosis from 2009 to 2018. The survival analysis shows that patients with higher predicted ADRD risk scores exhibited a significantly increased incidence of developing ADRD after their index date within five years.
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