Publications by authors named "S MUKERJI"
Background: Underdiagnosis of Alzheimer's disease and related dementias (ADRD) leads to lost opportunities for timely intervention, increased healthcare costs, and underestimation of the true burden of disease. To address this problem, we developed an AI algorithm, Decipher-AI (DEtection of Cognitive Impairment PHenotypes in EHR), to screen primary care patients for undiagnosed cognitive impairment (CI). We evaluated performance across sociodemographic groups using 3 years of EHR data before the first diagnosis or most recent visit.
View Article and Find Full Text PDFBackground: Underdiagnosis of Alzheimer's disease and related dementias (ADRD) leads to lost opportunities for timely intervention, increased healthcare costs, and underestimation of the true burden of disease. To address this problem, we developed an AI algorithm, Decipher-AI (DEtection of Cognitive Impairment PHenotypes in EHR), to screen primary care patients for undiagnosed cognitive impairment (CI). We evaluated performance across sociodemographic groups using 3 years of EHR data before the first diagnosis or most recent visit.
View Article and Find Full Text PDFBackground: This study examined the relationship between neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) and cognition in people living with HIV (PLWH) at baseline and over time.
Methods: Plasma and clinical data were available from PLWH aged ≥45 years with HIV RNA <200 copies/mL enrolled in the AIDS Clinical Trials Group HAILO cohort study. We measured plasma NfL and GFAP using a single molecule array platform.
Article Synopsis
- Unstructured and structured data in electronic health records (EHR) can provide valuable insights for research, but extracting this information can be challenging; researchers introduced an automated model to identify patients with Alzheimer's Disease, related dementias (ADRD), and mild cognitive impairment (MCI).
- The study involved a sample of 3,626 outpatient adults, using medical notes and diagnoses from chart reviews to develop a logistic regression model that predicts MCI/ADRD diagnoses with high performance metrics.
- The model demonstrated impressive accuracy (99.88%) and other metrics (like AUROC of 0.98), showing that automated EHR phenotyping could effectively facilitate large-scale research on MCI/ADRD.