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Dementia related cognitive impairment (CI) is a neurodegenerative disorder,affecting over 55 million people worldwide and growing rapidly at the rate ofone new case every 3 seconds. 75% cases go undiagnosed globally with up to 90%in low-and-middle-income countries, leading to an estimated annual worldwidecost of USD 1.3 trillion, forecasted to reach 2.8 trillion by 2030. With nocure, a recurring failure of clinical trials, and a lack of early diagnosis,the mortality rate is 100%. Information in electronic health records (EHR) canprovide vital clues for early detection of CI, but a manual review by expertsis tedious and error prone. Several computational methods have been proposed,however, they lack an enhanced understanding of the linguistic context incomplex language structures of EHR. Therefore, I propose a novel and moreaccurate framework, NeuraHealth, to identify patients who had no earlierdiagnosis. In NeuraHealth, using patient EHR from Mass General Brigham BioBank,I fine-tuned a bi-directional attention-based deep learning natural languageprocessing model to classify sequences. The sequence predictions were used togenerate structured features as input for a patient level regularized logisticregression model. This two-step framework creates high dimensionality,outperforming all existing state-of-the-art computational methods as well asclinical methods. Further, I integrate the models into a real-world product, aweb app, to create an automated EHR screening pipeline for scalable andhigh-speed discovery of undetected CI in EHR, making early diagnosis viable inmedical facilities and in regions with scarce health services.

NeuraHealth: An Automated Screening Pipeline to Detect Undiagnosed  Cognitive Impairment in Electronic Health Records with Deep Learning and  Natural Language Processing