Utilizing Sequential Information of General Lab-test Results and Diagnoses History for Differential Diagnosis of Dementia
This addresses early and differential diagnosis of dementia for patients in clinical settings, but is incremental as it applies existing deep learning methods to medical data.
The study tackled the problem of early Alzheimer's Disease diagnosis by using sequential general lab-test results and diagnoses history, improving diagnostic accuracy with a scalable and cost-effective approach.
Early diagnosis of Alzheimer's Disease (AD) faces multiple data-related challenges, including high variability in patient data, limited access to specialized diagnostic tests, and overreliance on single-type indicators. These challenges are exacerbated by the progressive nature of AD, where subtle pathophysiological changes often precede clinical symptoms by decades. To address these limitations, this study proposes a novel approach that takes advantage of routinely collected general laboratory test histories for the early detection and differential diagnosis of AD. By modeling lab test sequences as "sentences", we apply word embedding techniques to capture latent relationships between tests and employ deep time series models, including long-short-term memory (LSTM) and Transformer networks, to model temporal patterns in patient records. Experimental results demonstrate that our approach improves diagnostic accuracy and enables scalable and costeffective AD screening in diverse clinical settings.