Based Detection, Linguistic Biomarkers, Machine Learning, Explainable AI, Cognitive Decline Monitoring Share and Cite: de Filippis, R. and Al Foysal, A. (2025) Early Alzheimer’s Disease Detection from ...
Williams, A. and Louis, L. (2026) Cumulative Link Modeling of Ordinal Outcomes in the National Health Interview Survey Data: Application to Depressive Symptom Severity. Journal of Data Analysis and ...
Objective: This study compared a conventional logistic regression model with machine learning (ML) models using demographic and clinical data to predict outcomes at 2 and 6 months of treatment for MDR ...
Abstract: The excessive use of antibiotics has contributed to the growing resistance of various microbial pathogens, prompting the exploration of alternatives such as antimicrobial peptides (AMPs).
Abstract: The absence of low-frequency (LF) components in seismic records leads to nonuniqueness in inversion, making the building of a reasonable LF model crucial for achieving high-precision ...
Background: Despite substantial progress in biomarker research, Parkinson’s disease (PD) still lacks widely validated, easily deployable diagnostic tests for reliable early-stage detection, ...
Background: Diabetic foot ulcer (DFU) is a common and serious complication in patients with diabetes, which affects the quality of life greatly as well as brings high risk for mortality.
Gary Marcus, professor emeritus at NYU, explains the differences between large language models and "world models" — and why he thinks the latter are key to achieving artificial general intelligence.
When suboptimal results were observed, we generated additional samples of the misclassified ones to give them more attention. We reserved 184 posts for internal testing and 74 for external validation.
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