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Screening for Early-Stage Alzheimer’s Disease Using Optimized Feature Sets and Machine Learning

https://doi.org/10.3233/jad-201377
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Outgoing claims: the papers this work cites, builds on, or argues with.

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BHA‐CS: A novel cognitive composite for Alzheimer's disease and related disorders
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Rey verbal learning test is a useful tool for differential diagnosis in the preclinical phase of Alzheimer's disease: comparison with mild cognitive impairment and normal aging
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A composite score for executive functioning, validated in Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants with baseline mild cognitive impairment
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Using Informant and Performance Screening Methods to Detect Mild Cognitive Impairment and Dementia
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Increasing Precision of Clinical Diagnosis of Alzheimer's Disease Using a Combined Algorithm Incorporating Clinical and Novel Biomarker Data
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Using artificial neural networks to select the parameters for the prognostic of mild cognitive impairment and dementia in elderly individuals
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The Quick Dementia Rating System (QDRS): A rapid dementia staging tool
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Psychosocial Interventions for Community-Dwelling People Following Diagnosis of Mild to Moderate Dementia: Findings of a Systematic Scoping Review
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Toward defining the preclinical stages of Alzheimer's disease: Recommendations from the National Institute on Aging‐Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease
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The diagnosis of dementia due to Alzheimer's disease: Recommendations from the National Institute on Aging‐Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease
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The diagnosis of mild cognitive impairment due to Alzheimer's disease: Recommendations from the National Institute on Aging‐Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease
inferredcites →https://doi.org/10.1016/j.jalz.2013.01.015openalex-snapshot
Implications of early treatment among Medicaid patients with Alzheimer's disease
inferredcites →https://doi.org/10.1016/j.jalz.2018.02.018openalex-snapshot
NIA‐AA Research Framework: Toward a biological definition of Alzheimer's disease
inferredcites →https://doi.org/10.1016/j.jalz.2019.01.010openalex-snapshot
2019 Alzheimer's disease facts and figures
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Artificial intelligence and neuropsychological measures: The case of Alzheimer’s disease
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Widespread distribution of tauopathy in preclinical Alzheimer's disease
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Fractionating verbal episodic memory in Alzheimer's disease
inferredcites →https://doi.org/10.1016/j.trci.2018.01.003openalex-snapshot
Measuring cognition and function in the preclinical stage of Alzheimer's disease
inferredcites →https://doi.org/10.1016/j.trci.2019.05.002openalex-snapshot
Using data science to diagnose and characterize heterogeneity of Alzheimer's disease
inferredcites →https://doi.org/10.1017/s0033291700022418openalex-snapshot
Performance of the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) as a screening test for dementia
inferredcites →https://doi.org/10.1023/a:1010933404324openalex-snapshot
Random Forests
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The antibody aducanumab reduces Aβ plaques in Alzheimer’s disease
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A Standardized Memory Scale for Clinical Use
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Neuropsychological test selection for cognitive impairment classification: A machine learning approach
inferredcites →https://doi.org/10.1093/ageing/afv020openalex-snapshot
Who has undiagnosed dementia? A cross-sectional analysis of participants of the Aging, Demographics and Memory Study
inferredcites →https://doi.org/10.1097/wad.0000000000000223openalex-snapshot
Version 3 of the Alzheimer Disease Centers’ Neuropsychological Test Battery in the Uniform Data Set (UDS)
inferredcites →https://doi.org/10.1111/j.1532-5415.2005.53221.xopenalex-snapshot
The Montreal Cognitive Assessment, MoCA: A Brief Screening Tool For Mild Cognitive Impairment
inferredcites →https://doi.org/10.1136/bmjopen-2016-011146openalex-snapshot
Prevalence and determinants of undetected dementia in the community: a systematic literature review and a meta-analysis
inferredcites →https://doi.org/10.1136/bmjopen-2016-013665openalex-snapshot
Incidence of bloodstream infections: a nationwide surveillance of acute care hospitals in Switzerland 2008–2014
inferredcites →https://doi.org/10.1155/2017/1850909openalex-snapshot
Optimizing Neuropsychological Assessments for Cognitive, Behavioral, and Functional Impairment Classification: A Machine Learning Study
inferredcites →https://doi.org/10.1159/000354370openalex-snapshot
Rate of Conversion from Prodromal Alzheimer's Disease to Alzheimer's Dementia: A Systematic Review of the Literature
inferredcites →https://doi.org/10.1186/s12911-018-0710-yopenalex-snapshot
Neuropsychological predictors of conversion from mild cognitive impairment to Alzheimer’s disease: a feature selection ensemble combining stability and predictability
inferredcites →https://doi.org/10.1186/s12911-019-0974-xopenalex-snapshot
Prediction of cognitive impairment via deep learning trained with multi-center neuropsychological test data
inferredcites →https://doi.org/10.1212/wnl.43.11.2412-aopenalex-snapshot
The Clinical Dementia Rating (CDR)
inferredcites →https://doi.org/10.1371/journal.pone.0210041openalex-snapshot
Biased efficacy estimates in phase-III dengue vaccine trials due to heterogeneous exposure and differential detectability of primary infections across trial arms
inferredcites →https://doi.org/10.1371/journal.pone.0211345openalex-snapshot
Job preferences for healthcare administration students in China: A discrete choice experiment
inferredcites →https://doi.org/10.1371/journal.pone.0219569openalex-snapshot
Validity of screening instruments for the detection of dementia and mild cognitive impairment in hospital inpatients: A systematic review of diagnostic accuracy studies
inferredcites →https://doi.org/10.1371/journal.pone.0235534openalex-snapshot
What older adults do with the results of dementia screening programs
inferredcites →https://doi.org/10.1371/journal.pone.0240422openalex-snapshot
Using a patient-reported outcome to improve detection of cognitive impairment and dementia: The patient version of the Quick Dementia Rating System (QDRS)
inferredcites →https://doi.org/10.1613/jair.953openalex-snapshot
SMOTE: Synthetic Minority Over-sampling Technique
inferredcites →https://doi.org/10.3233/adr-190126openalex-snapshot
Global Clinical Dementia Rating Score of 0.5 May Not Be an Accurate Criterion to Identify Individuals with Mild Cognitive Impairment
inferredcites →https://doi.org/10.3233/fi-2010-288openalex-snapshot
Boruta – A System for Feature Selection
inferredcites →https://doi.org/10.3233/jad-201377openalex-snapshot
Screening for Early-Stage Alzheimer’s Disease Using Optimized Feature Sets and Machine Learning
inferredcites →https://doi.org/10.3389/fnagi.2020.00001openalex-snapshot
Electrophile Signaling and Emerging Immuno- and Neuro-modulatory Electrophilic Pharmaceuticals
inferredcites →https://doi.org/10.3389/fnagi.2020.00077openalex-snapshot
Predicting Alzheimer’s Disease Conversion From Mild Cognitive Impairment Using an Extreme Learning Machine-Based Grading Method With Multimodal Data
inferredcites →https://doi.org/10.3389/fpubh.2017.00307openalex-snapshot
Sensitivity, Specificity, and Predictive Values: Foundations, Pliabilities, and Pitfalls in Research and Practice
inferredcites →https://doi.org/10.3390/app7070651openalex-snapshot
Early Diagnosis of Dementia from Clinical Data by Machine Learning Techniques
inferredcites →https://doi.org/10.4230/oasics.dx.2024.27openalex-snapshot
On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper)
inferredcites →https://doi.org/10.48550/arxiv.1705.07874openalex-snapshot
A Unified Approach to Interpreting Model Predictions
inferredcites →https://doi.org/10.5281/zenodo.4081703openalex-snapshot
Supplemental code for "Screening for early-stage Alzheimer's disease using optimized feature sets and machine learning"

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Generalized Robust Approach to Feature Selection
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Improving clinical efficiency in screening for cognitive impairment due to Alzheimer's
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Synthetic Data in the Detection of States of Cognitive Progression to Alzheimer’s Through Neuropsychological Assessments and Machine Learning Models
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Distal Symmetric Polyneuropathy Identification in Type 2 Diabetes Subjects: A Random Forest Approach
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Additional file 1 of XGBoost-SHAP-based interpretable diagnostic framework for alzheimer’s disease
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