Article

Leveraging Transformer Models to Predict Cognitive Impairment: Accuracy, Efficiency, and Interpretability

2024-11-27

Abstract excerpt

<title>Abstract</title> <p>Objective: This study aims to develop an enhanced Transformer model for predicting mild cognitive impairment (MCI) using data from the China Health and Retirement Longitudinal Study (CHARLS), focusing on handling mixed data types and improving predictive accuracy. <h4>Methods:</h4> The Transformer integrates categorical (integer-encoded) and continuous (floating-point) data, using multi...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 0fa487d9-2588-5448-9cfd-57a78e7c69ad
DOI: 10.21203/rs.3.rs-5397450/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Leveraging Transformer Models to Predict Cognitive Impairment: Accuracy, Efficiency, and InterpretabilityDOI 10.21203/rs.3.rs-5397450/v1Read

Generalizability, Interpretability, and Clinical Readiness of Deep Learning Methods for Alzheimer’s Disease: A Systematic Literature ReviewSemantic proximity Read

Artificial intelligence based detection of early cognitive impairment using language, speech, and demographic features: Model development and validationSemantic proximity Read

Machine Learning methods for predicting conversion from mild cognitive impairment to Alzheimer’s disease. A systematic review.Semantic proximity Read

Modeling Temporal Dependencies and Feature Interactions Reveal Novel Clinical and Molecular Insights into Alzheimer’s Disease ProgressionSemantic proximity Read

Risk Score Stratification of Alzheimer’s Disease and Mild Cognitive Impairment using Deep LearningSemantic proximity Read

Predicting Continuous Cognitive Decline: The Generalizability of a Multimodal Machine Learning Approach Including Structural MRI and Non-Brain DataSemantic proximity Read

AI-Powered Evaluation of Dementia Severity Based on Clinical Data and Visual Scoring Systems (MTA,ERICA, GCA) from MRISemantic proximity Read

Predicting 24-Month MCI-to-Alzheimer’s Conversion Using Routine Clinical Assessments Without Neuroimaging or Genetic TestingSemantic proximity Read

Artificial intelligence based detection of early cognitive impairment using language, speech, and demographic features: Model development and validationSemantic proximity Read

DSC-CNN: A Dual-Stream CNN with Cognitive Embedding Fusion for Early Alzheimer’s DiagnosisSemantic proximity Read

A Novel Ensemble-Based Machine Learning Algorithm To Predict The Conversion From Mild Cognitive Impairment To Alzheimer’s Disease Using Socio-demographic Characteristics, Clinical Information And Neuropsychological MeasuresSemantic proximity Read

Predicting Risk of Alzheimer’s Diseases and Related Dementias with AI Foundation Model on Electronic Health RecordsSemantic proximity Read