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Resource-stratified machine learning framework for cognitive status classification and mild cognitive impairment to dementia progression prediction.

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Τίτλος: Resource-stratified machine learning framework for cognitive status classification and mild cognitive impairment to dementia progression prediction.
Συγγραφείς: Yang J; Department of Electrical & Computer Engineering, Division of Systems Engineering, Department of Biomedical Engineering, Boston University, 8 St. Mary's St, Boston, MA, 02215, USA., Ding H; Department of Anatomy & Neurobiology and Framingham Heart Study, Chobanian & Avedisian School of Medicine, Boston University, 72 E Concord St, Boston, MA, 02118, USA., Amini S; Department of Electrical & Computer Engineering, Division of Systems Engineering, Department of Biomedical Engineering, Boston University, 8 St. Mary's St, Boston, MA, 02215, USA., Hao B; Department of Electrical & Computer Engineering, Division of Systems Engineering, Department of Biomedical Engineering, Boston University, 8 St. Mary's St, Boston, MA, 02215, USA., Karjadi C; Department of Anatomy & Neurobiology and Framingham Heart Study, Chobanian & Avedisian School of Medicine, Boston University, 72 E Concord St, Boston, MA, 02118, USA., Au R; Department of Anatomy & Neurobiology and Framingham Heart Study, Chobanian & Avedisian School of Medicine, Boston University, 72 E Concord St, Boston, MA, 02118, USA.; Department of Neurology and Medicine, Chobanian & Avedisian School of Medicine, Boston University, 72 E Concord St, Boston, MA, 02118, USA.; Department of Epidemiology, School of Public Health, Boston University, 72 E Concord St, Boston, MA, 02118, USA., Paschalidis IC; Department of Electrical & Computer Engineering, Division of Systems Engineering, Department of Biomedical Engineering, Boston University, 8 St. Mary's St, Boston, MA, 02215, USA. yannisp@bu.edu.; Faculty of Computing & Data Sciences, Boston University, 665 Commonwealth Ave, Boston, MA, 02215, USA. yannisp@bu.edu.
Πηγή: Alzheimer's research & therapy [Alzheimers Res Ther] 2026 Mar 07; Vol. 18 (1). Date of Electronic Publication: 2026 Mar 07.
Τύπος έκδοσης: Journal Article; Research Support, N.I.H., Extramural; Research Support, U.S. Gov't, Non-P.H.S.
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: BioMed Central Ltd Country of Publication: England NLM ID: 101511643 Publication Model: Electronic Cited Medium: Internet ISSN: 1758-9193 (Electronic) NLM ISO Abbreviation: Alzheimers Res Ther Subsets: MEDLINE
Imprint Name(s): Original Publication: [London] : BioMed Central Ltd.
Ιατρικοί όροι (MeSH): Cognitive Dysfunction*/diagnosis , Cognitive Dysfunction*/classification , Dementia*/diagnosis , Dementia*/classification , Disease Progression* , Machine Learning*, Humans ; Female ; Male ; Aged ; Predictive Learning Models ; Neuropsychological Tests ; Classification Algorithms ; Prognosis ; Aged, 80 and over ; Boosting Machine Learning Algorithms ; Prediction Algorithms
Περίληψη: BACKGROUND: Widespread access to diagnosis of cognitive decline remains inadequate. Assessment tools rely on neuroimaging, biofluid markers, or lengthy neuropsychological batteries. This reliance limits their use in primary care and low-resource settings and contributes to healthcare disparities. We developed and validated a three-level, resource-stratified machine learning framework to provide scalable dementia screening and prognostic risk stratification across diverse healthcare settings. METHODS: We used data from 31,081 participants in the National Alzheimer’s Coordinating Center. We trained Gradient Boosted Tree models for multi-class detection (Cognitively Intact/Mild Cognitive Impairment [MCI]/Dementia) and 10-year risk stratification (MCI-to-dementia progression). The framework tiered inputs by resource intensity. The minimal-resource Level 1 included demographic and basic functional data. The moderate-resource Level 2 added standard cognitive tests. The high-resource Level 3 added comprehensive neuropsychological batteries. RESULTS: The Level 3 model achieved high classification performance (AUC: 93.98%), and the Level 1 model achieved comparable performance (AUC: 91.53%). For MCI-to-dementia progression, the framework showed strong prognostic performance. The Level 3 and Level 1 models achieved AUCs of 85.90% and 81.90% for predicting progression over a 2-year window, respectively. Key predictors remained consistent across all resource levels, such as difficulty managing finances and self-reported cognitive decline, and sociodemographic factors, including education level and Black race. CONCLUSIONS: The tiered system offers a scalable and accessible approach for dementia detection and prognostic stratification. It helps non-specialists conduct initial evaluations and identify high-risk individuals with minimal data. High-risk patients may then be triaged for specialized assessment. This resource-stratified framework offers a strategy to expand diagnostic capacity and reduce care inequities, especially in low-resource settings.
Competing Interests: Declarations. Ethics approval and consent to participate: The data used in this study were obtained from the NACC database. All participants provided informed consent at their respective ADRCs prior to data collection and submission to NACC. As secondary users of de-identified data from the NACC database, consent was not necessary for the use of human subject data in this study. The use of NACC UDS data for this research was conducted in accordance with NACC data use agreements. Consent for publication: All authors have reviewed the manuscript and consented to its publication. Competing interests: The authors declare no competing interests.
References: Int J Equity Health. 2025 Mar 17;24(1):74. (PMID: 40091013)
J Alzheimers Dis. 2022;87(4):1627-1636. (PMID: 35491782)
BMJ Glob Health. 2022 Apr;7(4):. (PMID: 35379735)
Alzheimers Dement. 2023 Feb;19(2):477-486. (PMID: 35451562)
Clin Interv Aging. 2007;2(4):631-6. (PMID: 18225464)
Alzheimers Dement. 2013 Nov;9(6):657-65. (PMID: 23332672)
Alzheimers Dement. 2023 Sep;19(9):4046-4060. (PMID: 37204054)
Alzheimers Dement. 2018 Mar;14(3):263-270. (PMID: 29055814)
Alzheimers Dement. 2024 Aug;20(8):5551-5560. (PMID: 38934297)
Alzheimers Dement (N Y). 2024 Apr 24;10(2):e12465. (PMID: 38659717)
Int J Mol Sci. 2022 Nov 12;23(22):. (PMID: 36430432)
J Alzheimers Dis. 2021;83(2):581-589. (PMID: 34334396)
JAMA Intern Med. 2024 May 1;184(5):563-572. (PMID: 38436963)
BMC Med. 2025 Feb 21;23(1):98. (PMID: 39984949)
Alzheimers Dement. 2024 Jun;20(6):4290-4314. (PMID: 38696263)
Alzheimers Dement (N Y). 2022 Mar 24;8(1):e12271. (PMID: 35356741)
Alzheimers Dement. 2024 Feb;20(2):769-782. (PMID: 37776210)
Neuroimage. 2014 Nov 1;101:569-82. (PMID: 25042445)
Alzheimers Res Ther. 2022 Mar 29;14(1):45. (PMID: 35351193)
Life Sci. 2022 Oct 1;306:120861. (PMID: 35932841)
J Med Syst. 2023 Feb 1;47(1):17. (PMID: 36720727)
Alzheimers Dement. 2023 Dec;19(12):5885-5904. (PMID: 37563912)
Lancet. 2015 Jun 6;385(9984):2255-63. (PMID: 25771249)
JAMA. 2019 Oct 22;322(16):1589-1599. (PMID: 31638686)
Alzheimer Dis Assoc Disord. 2009 Oct-Dec;23(4):306-14. (PMID: 19568149)
J Prev Alzheimers Dis. 2023;10(2):301-313. (PMID: 36946457)
Biomed Mater Devices. 2023 Apr 10;:1-17. (PMID: 37363136)
Neuroimage Clin. 2014 Jan 04;4:461-72. (PMID: 24634832)
Alzheimers Dement. 2024 May;20(5):3708-3821. (PMID: 38689398)
BMC Fam Pract. 2010 Jul 01;11:52. (PMID: 20594302)
EClinicalMedicine. 2024 Jul 12;74:102725. (PMID: 39764178)
Alzheimers Res Ther. 2024 Jun 7;16(1):122. (PMID: 38849944)
Alzheimer Dis Assoc Disord. 2018 Oct-Dec;32(4):351-358. (PMID: 30376508)
Lancet. 2024 Aug 10;404(10452):572-628. (PMID: 39096926)
J Alzheimers Dis. 2021;81(2):729-742. (PMID: 33814438)
Transl Neurodegener. 2025 Jan 27;14(1):6. (PMID: 39865265)
Alzheimers Dement. 2025 Jun;21(6):e14333. (PMID: 39713942)
Front Neurosci. 2021 Feb 19;15:584641. (PMID: 33746695)
Nat Commun. 2022 Jun 20;13(1):3404. (PMID: 35725739)
Alzheimers Dement. 2024 Aug;20(8):5262-5270. (PMID: 38924662)
Alzheimers Dement (Amst). 2025 Mar 12;17(1):e70074. (PMID: 40078377)
Nat Med. 2024 Oct;30(10):2977-2989. (PMID: 38965435)
Alzheimers Dement (N Y). 2021 Jul 27;7(1):e12200. (PMID: 34337138)
J Alzheimers Dis. 2020;75(1):277-288. (PMID: 32250302)
J Alzheimers Dis. 2022;87(1):83-100. (PMID: 35342092)
BMC Fam Pract. 2021 Aug 11;22(1):166. (PMID: 34380424)
Front Aging Neurosci. 2022 Jun 02;14:876202. (PMID: 35721012)
Clin Gerontol. 2023 Mar-Apr;46(2):133-137. (PMID: 35672953)
Biomedicines. 2019 Dec 09;7(4):. (PMID: 31835422)
Nat Rev Neurol. 2018 Nov;14(11):653-666. (PMID: 30291317)
Alzheimers Dement (Amst). 2023 Oct 29;15(4):e12494. (PMID: 37908438)
Curr Med Imaging. 2023;19(8):855-864. (PMID: 36177618)
J Gen Intern Med. 2007 Nov;22(11):1487-92. (PMID: 17823840)
Grant Information: UL54 TR00413 United States GF NIH HHS; R03 AG095992 United States AG NIA NIH HHS; AG062109 United States AG NIA NIH HHS; DEB-2433726 Office of Inspector General; CCF-2200052 National Science Foundation, United States; R01 AG062109 United States AG NIA NIH HHS; U19 AG068753 United States AG NIA NIH HHS; R01 AG072654 United States AG NIA NIH HHS; R01 AG083735 United States AG NIA NIH HHS; P30 AG072959 United States AG NIA NIH HHS; P30 AG072958 United States AG NIA NIH HHS; P20 AG068082 United States AG NIA NIH HHS; P20 AG068077 United States AG NIA NIH HHS; P20 AG068053 United States AG NIA NIH HHS; P20 AG068024 United States AG NIA NIH HHS; P30 AG066546 United States AG NIA NIH HHS; P30 AG072931 United States AG NIA NIH HHS; P30 AG072947 United States AG NIA NIH HHS; P30 AG066515 United States AG NIA NIH HHS; P30 AG066508 United States AG NIA NIH HHS; P30 AG066506 United States AG NIA NIH HHS; P30 AG072973 United States AG NIA NIH HHS; P30 AG072946 United States AG NIA NIH HHS; P30 AG066511 United States AG NIA NIH HHS; P30 AG062422 United States AG NIA NIH HHS; P30 AG062677 United States AG NIA NIH HHS; P30 AG066519 United States AG NIA NIH HHS; P30 AG072977 United States AG NIA NIH HHS; U24 AG072122 United States AG NIA NIH HHS; P30 AG062429 United States AG NIA NIH HHS; P30 AG066468 United States AG NIA NIH HHS; P30 AG062421 United States AG NIA NIH HHS; P30 AG066509 United States AG NIA NIH HHS; P30 AG066514 United States AG NIA NIH HHS; P30 AG066530 United States AG NIA NIH HHS; P30 AG066507 United States AG NIA NIH HHS; P30 AG066444 United States AG NIA NIH HHS; P30 AG066518 United States AG NIA NIH HHS; P30 AG066512 United States AG NIA NIH HHS; P30 AG066462 United States AG NIA NIH HHS; P30 AG072979 United States AG NIA NIH HHS; P30 AG072972 United States AG NIA NIH HHS
Contributed Indexing: Keywords: Cognitive Assessment; Healthcare Disparities; MCI-to-Dementia Progression; Machine Learning; National Alzheimer’s Coordinating Center Data; Resource-Stratified Screening
Entry Date(s): Date Created: 20260307 Date Completed: 20260628 Latest Revision: 20260813
Update Code: 20260813
PubMed Central ID: PMC13081641
DOI: 10.1186/s13195-026-02006-7
PMID: 41794745
Βάση Δεδομένων: MEDLINE
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  Group: Ti
  Data: Resource-stratified machine learning framework for cognitive status classification and mild cognitive impairment to dementia progression prediction.
– Name: Author
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  Data: <searchLink fieldCode="AU" term="%22Yang+J%22">Yang J</searchLink>; Department of Electrical & Computer Engineering, Division of Systems Engineering, Department of Biomedical Engineering, Boston University, 8 St. Mary's St, Boston, MA, 02215, USA.<br /><searchLink fieldCode="AU" term="%22Ding+H%22">Ding H</searchLink>; Department of Anatomy & Neurobiology and Framingham Heart Study, Chobanian & Avedisian School of Medicine, Boston University, 72 E Concord St, Boston, MA, 02118, USA.<br /><searchLink fieldCode="AU" term="%22Amini+S%22">Amini S</searchLink>; Department of Electrical & Computer Engineering, Division of Systems Engineering, Department of Biomedical Engineering, Boston University, 8 St. Mary's St, Boston, MA, 02215, USA.<br /><searchLink fieldCode="AU" term="%22Hao+B%22">Hao B</searchLink>; Department of Electrical & Computer Engineering, Division of Systems Engineering, Department of Biomedical Engineering, Boston University, 8 St. Mary's St, Boston, MA, 02215, USA.<br /><searchLink fieldCode="AU" term="%22Karjadi+C%22">Karjadi C</searchLink>; Department of Anatomy & Neurobiology and Framingham Heart Study, Chobanian & Avedisian School of Medicine, Boston University, 72 E Concord St, Boston, MA, 02118, USA.<br /><searchLink fieldCode="AU" term="%22Au+R%22">Au R</searchLink>; Department of Anatomy & Neurobiology and Framingham Heart Study, Chobanian & Avedisian School of Medicine, Boston University, 72 E Concord St, Boston, MA, 02118, USA.; Department of Neurology and Medicine, Chobanian & Avedisian School of Medicine, Boston University, 72 E Concord St, Boston, MA, 02118, USA.; Department of Epidemiology, School of Public Health, Boston University, 72 E Concord St, Boston, MA, 02118, USA.<br /><searchLink fieldCode="AU" term="%22Paschalidis+IC%22">Paschalidis IC</searchLink>; Department of Electrical & Computer Engineering, Division of Systems Engineering, Department of Biomedical Engineering, Boston University, 8 St. Mary's St, Boston, MA, 02215, USA. yannisp@bu.edu.; Faculty of Computing & Data Sciences, Boston University, 665 Commonwealth Ave, Boston, MA, 02215, USA. yannisp@bu.edu.
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  Data: <searchLink fieldCode="JN" term="%22101511643%22">Alzheimer's research & therapy</searchLink> [Alzheimers Res Ther] 2026 Mar 07; Vol. 18 (1). <i>Date of Electronic Publication: </i>2026 Mar 07.
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  Data: Journal Article; Research Support, N.I.H., Extramural; Research Support, U.S. Gov't, Non-P.H.S.
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  Label: Language
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  Data: English
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22BioMed+Central+Ltd%22">BioMed Central Ltd </searchLink><i>Country of Publication: </i>England <i>NLM ID: </i>101511643 <i>Publication Model: </i>Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1758-9193 (Electronic) <i>NLM ISO Abbreviation: </i>Alzheimers Res Ther <i>Subsets: </i>MEDLINE
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  Data: <i>Original Publication</i>: [London] : BioMed Central Ltd.
– Name: SubjectMESH
  Label: MeSH Terms
  Group: Su
  Data: <searchLink fieldCode="MM" term="%22Cognitive+Dysfunction%22">Cognitive Dysfunction*</searchLink>/<searchLink fieldCode="MM" term="%22Cognitive+Dysfunction+diagnosis%22">diagnosis</searchLink> <br /><searchLink fieldCode="MM" term="%22Cognitive+Dysfunction%22">Cognitive Dysfunction*</searchLink>/<searchLink fieldCode="MM" term="%22Cognitive+Dysfunction+classification%22">classification</searchLink> <br /><searchLink fieldCode="MM" term="%22Dementia%22">Dementia*</searchLink>/<searchLink fieldCode="MM" term="%22Dementia+diagnosis%22">diagnosis</searchLink> <br /><searchLink fieldCode="MM" term="%22Dementia%22">Dementia*</searchLink>/<searchLink fieldCode="MM" term="%22Dementia+classification%22">classification</searchLink> <br /><searchLink fieldCode="MM" term="%22Disease+Progression%22">Disease Progression*</searchLink> <br /><searchLink fieldCode="MM" term="%22Machine+Learning%22">Machine Learning*</searchLink><br /><searchLink fieldCode="MH" term="%22Humans%22">Humans</searchLink> ; <searchLink fieldCode="MH" term="%22Female%22">Female</searchLink> ; <searchLink fieldCode="MH" term="%22Male%22">Male</searchLink> ; <searchLink fieldCode="MH" term="%22Aged%22">Aged</searchLink> ; <searchLink fieldCode="MH" term="%22Predictive+Learning+Models%22">Predictive Learning Models</searchLink> ; <searchLink fieldCode="MH" term="%22Neuropsychological+Tests%22">Neuropsychological Tests</searchLink> ; <searchLink fieldCode="MH" term="%22Classification+Algorithms%22">Classification Algorithms</searchLink> ; <searchLink fieldCode="MH" term="%22Prognosis%22">Prognosis</searchLink> ; <searchLink fieldCode="MH" term="%22Aged%2C+80+and+over%22">Aged, 80 and over</searchLink> ; <searchLink fieldCode="MH" term="%22Boosting+Machine+Learning+Algorithms%22">Boosting Machine Learning Algorithms</searchLink> ; <searchLink fieldCode="MH" term="%22Prediction+Algorithms%22">Prediction Algorithms</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: BACKGROUND: Widespread access to diagnosis of cognitive decline remains inadequate. Assessment tools rely on neuroimaging, biofluid markers, or lengthy neuropsychological batteries. This reliance limits their use in primary care and low-resource settings and contributes to healthcare disparities. We developed and validated a three-level, resource-stratified machine learning framework to provide scalable dementia screening and prognostic risk stratification across diverse healthcare settings. METHODS: We used data from 31,081 participants in the National Alzheimer’s Coordinating Center. We trained Gradient Boosted Tree models for multi-class detection (Cognitively Intact/Mild Cognitive Impairment [MCI]/Dementia) and 10-year risk stratification (MCI-to-dementia progression). The framework tiered inputs by resource intensity. The minimal-resource Level 1 included demographic and basic functional data. The moderate-resource Level 2 added standard cognitive tests. The high-resource Level 3 added comprehensive neuropsychological batteries. RESULTS: The Level 3 model achieved high classification performance (AUC: 93.98%), and the Level 1 model achieved comparable performance (AUC: 91.53%). For MCI-to-dementia progression, the framework showed strong prognostic performance. The Level 3 and Level 1 models achieved AUCs of 85.90% and 81.90% for predicting progression over a 2-year window, respectively. Key predictors remained consistent across all resource levels, such as difficulty managing finances and self-reported cognitive decline, and sociodemographic factors, including education level and Black race. CONCLUSIONS: The tiered system offers a scalable and accessible approach for dementia detection and prognostic stratification. It helps non-specialists conduct initial evaluations and identify high-risk individuals with minimal data. High-risk patients may then be triaged for specialized assessment. This resource-stratified framework offers a strategy to expand diagnostic capacity and reduce care inequities, especially in low-resource settings.
– Name: Abstract
  Label: Competing Interests
  Group: Ab
  Data: Declarations. Ethics approval and consent to participate: The data used in this study were obtained from the NACC database. All participants provided informed consent at their respective ADRCs prior to data collection and submission to NACC. As secondary users of de-identified data from the NACC database, consent was not necessary for the use of human subject data in this study. The use of NACC UDS data for this research was conducted in accordance with NACC data use agreements. Consent for publication: All authors have reviewed the manuscript and consented to its publication. Competing interests: The authors declare no competing interests.
– Name: Ref
  Label: References
  Group: RefInfo
  Data: Int J Equity Health. 2025 Mar 17;24(1):74. (PMID: <searchLink fieldCode="PM" term="%2240091013%22">40091013)</searchLink><br />J Alzheimers Dis. 2022;87(4):1627-1636. (PMID: <searchLink fieldCode="PM" term="%2235491782%22">35491782)</searchLink><br />BMJ Glob Health. 2022 Apr;7(4):. (PMID: <searchLink fieldCode="PM" term="%2235379735%22">35379735)</searchLink><br />Alzheimers Dement. 2023 Feb;19(2):477-486. (PMID: <searchLink fieldCode="PM" term="%2235451562%22">35451562)</searchLink><br />Clin Interv Aging. 2007;2(4):631-6. (PMID: <searchLink fieldCode="PM" term="%2218225464%22">18225464)</searchLink><br />Alzheimers Dement. 2013 Nov;9(6):657-65. (PMID: <searchLink fieldCode="PM" term="%2223332672%22">23332672)</searchLink><br />Alzheimers Dement. 2023 Sep;19(9):4046-4060. (PMID: <searchLink fieldCode="PM" term="%2237204054%22">37204054)</searchLink><br />Alzheimers Dement. 2018 Mar;14(3):263-270. (PMID: <searchLink fieldCode="PM" term="%2229055814%22">29055814)</searchLink><br />Alzheimers Dement. 2024 Aug;20(8):5551-5560. (PMID: <searchLink fieldCode="PM" term="%2238934297%22">38934297)</searchLink><br />Alzheimers Dement (N Y). 2024 Apr 24;10(2):e12465. (PMID: <searchLink fieldCode="PM" term="%2238659717%22">38659717)</searchLink><br />Int J Mol Sci. 2022 Nov 12;23(22):. (PMID: <searchLink fieldCode="PM" term="%2236430432%22">36430432)</searchLink><br />J Alzheimers Dis. 2021;83(2):581-589. (PMID: <searchLink fieldCode="PM" term="%2234334396%22">34334396)</searchLink><br />JAMA Intern Med. 2024 May 1;184(5):563-572. (PMID: <searchLink fieldCode="PM" term="%2238436963%22">38436963)</searchLink><br />BMC Med. 2025 Feb 21;23(1):98. (PMID: <searchLink fieldCode="PM" term="%2239984949%22">39984949)</searchLink><br />Alzheimers Dement. 2024 Jun;20(6):4290-4314. (PMID: <searchLink fieldCode="PM" term="%2238696263%22">38696263)</searchLink><br />Alzheimers Dement (N Y). 2022 Mar 24;8(1):e12271. (PMID: <searchLink fieldCode="PM" term="%2235356741%22">35356741)</searchLink><br />Alzheimers Dement. 2024 Feb;20(2):769-782. (PMID: <searchLink fieldCode="PM" term="%2237776210%22">37776210)</searchLink><br />Neuroimage. 2014 Nov 1;101:569-82. (PMID: <searchLink fieldCode="PM" term="%2225042445%22">25042445)</searchLink><br />Alzheimers Res Ther. 2022 Mar 29;14(1):45. (PMID: <searchLink fieldCode="PM" term="%2235351193%22">35351193)</searchLink><br />Life Sci. 2022 Oct 1;306:120861. (PMID: <searchLink fieldCode="PM" term="%2235932841%22">35932841)</searchLink><br />J Med Syst. 2023 Feb 1;47(1):17. (PMID: <searchLink fieldCode="PM" term="%2236720727%22">36720727)</searchLink><br />Alzheimers Dement. 2023 Dec;19(12):5885-5904. (PMID: <searchLink fieldCode="PM" term="%2237563912%22">37563912)</searchLink><br />Lancet. 2015 Jun 6;385(9984):2255-63. (PMID: <searchLink fieldCode="PM" term="%2225771249%22">25771249)</searchLink><br />JAMA. 2019 Oct 22;322(16):1589-1599. (PMID: <searchLink fieldCode="PM" term="%2231638686%22">31638686)</searchLink><br />Alzheimer Dis Assoc Disord. 2009 Oct-Dec;23(4):306-14. (PMID: <searchLink fieldCode="PM" term="%2219568149%22">19568149)</searchLink><br />J Prev Alzheimers Dis. 2023;10(2):301-313. (PMID: <searchLink fieldCode="PM" term="%2236946457%22">36946457)</searchLink><br />Biomed Mater Devices. 2023 Apr 10;:1-17. (PMID: <searchLink fieldCode="PM" term="%2237363136%22">37363136)</searchLink><br />Neuroimage Clin. 2014 Jan 04;4:461-72. (PMID: <searchLink fieldCode="PM" term="%2224634832%22">24634832)</searchLink><br />Alzheimers Dement. 2024 May;20(5):3708-3821. (PMID: <searchLink fieldCode="PM" term="%2238689398%22">38689398)</searchLink><br />BMC Fam Pract. 2010 Jul 01;11:52. (PMID: <searchLink fieldCode="PM" term="%2220594302%22">20594302)</searchLink><br />EClinicalMedicine. 2024 Jul 12;74:102725. (PMID: <searchLink fieldCode="PM" term="%2239764178%22">39764178)</searchLink><br />Alzheimers Res Ther. 2024 Jun 7;16(1):122. (PMID: <searchLink fieldCode="PM" term="%2238849944%22">38849944)</searchLink><br />Alzheimer Dis Assoc Disord. 2018 Oct-Dec;32(4):351-358. (PMID: <searchLink fieldCode="PM" term="%2230376508%22">30376508)</searchLink><br />Lancet. 2024 Aug 10;404(10452):572-628. (PMID: <searchLink fieldCode="PM" term="%2239096926%22">39096926)</searchLink><br />J Alzheimers Dis. 2021;81(2):729-742. (PMID: <searchLink fieldCode="PM" term="%2233814438%22">33814438)</searchLink><br />Transl Neurodegener. 2025 Jan 27;14(1):6. (PMID: <searchLink fieldCode="PM" term="%2239865265%22">39865265)</searchLink><br />Alzheimers Dement. 2025 Jun;21(6):e14333. (PMID: <searchLink fieldCode="PM" term="%2239713942%22">39713942)</searchLink><br />Front Neurosci. 2021 Feb 19;15:584641. (PMID: <searchLink fieldCode="PM" term="%2233746695%22">33746695)</searchLink><br />Nat Commun. 2022 Jun 20;13(1):3404. (PMID: <searchLink fieldCode="PM" term="%2235725739%22">35725739)</searchLink><br />Alzheimers Dement. 2024 Aug;20(8):5262-5270. (PMID: <searchLink fieldCode="PM" term="%2238924662%22">38924662)</searchLink><br />Alzheimers Dement (Amst). 2025 Mar 12;17(1):e70074. (PMID: <searchLink fieldCode="PM" term="%2240078377%22">40078377)</searchLink><br />Nat Med. 2024 Oct;30(10):2977-2989. (PMID: <searchLink fieldCode="PM" term="%2238965435%22">38965435)</searchLink><br />Alzheimers Dement (N Y). 2021 Jul 27;7(1):e12200. (PMID: <searchLink fieldCode="PM" term="%2234337138%22">34337138)</searchLink><br />J Alzheimers Dis. 2020;75(1):277-288. (PMID: <searchLink fieldCode="PM" term="%2232250302%22">32250302)</searchLink><br />J Alzheimers Dis. 2022;87(1):83-100. (PMID: <searchLink fieldCode="PM" term="%2235342092%22">35342092)</searchLink><br />BMC Fam Pract. 2021 Aug 11;22(1):166. (PMID: <searchLink fieldCode="PM" term="%2234380424%22">34380424)</searchLink><br />Front Aging Neurosci. 2022 Jun 02;14:876202. (PMID: <searchLink fieldCode="PM" term="%2235721012%22">35721012)</searchLink><br />Clin Gerontol. 2023 Mar-Apr;46(2):133-137. (PMID: <searchLink fieldCode="PM" term="%2235672953%22">35672953)</searchLink><br />Biomedicines. 2019 Dec 09;7(4):. (PMID: <searchLink fieldCode="PM" term="%2231835422%22">31835422)</searchLink><br />Nat Rev Neurol. 2018 Nov;14(11):653-666. (PMID: <searchLink fieldCode="PM" term="%2230291317%22">30291317)</searchLink><br />Alzheimers Dement (Amst). 2023 Oct 29;15(4):e12494. (PMID: <searchLink fieldCode="PM" term="%2237908438%22">37908438)</searchLink><br />Curr Med Imaging. 2023;19(8):855-864. (PMID: <searchLink fieldCode="PM" term="%2236177618%22">36177618)</searchLink><br />J Gen Intern Med. 2007 Nov;22(11):1487-92. (PMID: <searchLink fieldCode="PM" term="%2217823840%22">17823840)</searchLink>
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  Data: UL54 TR00413 United States GF NIH HHS; R03 AG095992 United States AG NIA NIH HHS; AG062109 United States AG NIA NIH HHS; DEB-2433726 Office of Inspector General; CCF-2200052 National Science Foundation, United States; R01 AG062109 United States AG NIA NIH HHS; U19 AG068753 United States AG NIA NIH HHS; R01 AG072654 United States AG NIA NIH HHS; R01 AG083735 United States AG NIA NIH HHS; P30 AG072959 United States AG NIA NIH HHS; P30 AG072958 United States AG NIA NIH HHS; P20 AG068082 United States AG NIA NIH HHS; P20 AG068077 United States AG NIA NIH HHS; P20 AG068053 United States AG NIA NIH HHS; P20 AG068024 United States AG NIA NIH HHS; P30 AG066546 United States AG NIA NIH HHS; P30 AG072931 United States AG NIA NIH HHS; P30 AG072947 United States AG NIA NIH HHS; P30 AG066515 United States AG NIA NIH HHS; P30 AG066508 United States AG NIA NIH HHS; P30 AG066506 United States AG NIA NIH HHS; P30 AG072973 United States AG NIA NIH HHS; P30 AG072946 United States AG NIA NIH HHS; P30 AG066511 United States AG NIA NIH HHS; P30 AG062422 United States AG NIA NIH HHS; P30 AG062677 United States AG NIA NIH HHS; P30 AG066519 United States AG NIA NIH HHS; P30 AG072977 United States AG NIA NIH HHS; U24 AG072122 United States AG NIA NIH HHS; P30 AG062429 United States AG NIA NIH HHS; P30 AG066468 United States AG NIA NIH HHS; P30 AG062421 United States AG NIA NIH HHS; P30 AG066509 United States AG NIA NIH HHS; P30 AG066514 United States AG NIA NIH HHS; P30 AG066530 United States AG NIA NIH HHS; P30 AG066507 United States AG NIA NIH HHS; P30 AG066444 United States AG NIA NIH HHS; P30 AG066518 United States AG NIA NIH HHS; P30 AG066512 United States AG NIA NIH HHS; P30 AG066462 United States AG NIA NIH HHS; P30 AG072979 United States AG NIA NIH HHS; P30 AG072972 United States AG NIA NIH HHS
– Name: SubjectMinor
  Label: Contributed Indexing
  Group:
  Data: <i>Keywords: </i>Cognitive Assessment; Healthcare Disparities; MCI-to-Dementia Progression; Machine Learning; National Alzheimer’s Coordinating Center Data; Resource-Stratified Screening
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  Group: Date
  Data: <i>Date Created: </i>20260307 <i>Date Completed: </i>20260628 <i>Latest Revision: </i>20260813
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  Data: 20260813
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  Data: PMC13081641
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1186/s13195-026-02006-7
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  Data: 41794745
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=cmedm&AN=41794745
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1186/s13195-026-02006-7
    Languages:
      – Code: eng
        Text: English
    Subjects:
      – SubjectFull: Humans
        Type: general
      – SubjectFull: Female
        Type: general
      – SubjectFull: Male
        Type: general
      – SubjectFull: Aged
        Type: general
      – SubjectFull: Predictive Learning Models
        Type: general
      – SubjectFull: Neuropsychological Tests
        Type: general
      – SubjectFull: Classification Algorithms
        Type: general
      – SubjectFull: Prognosis
        Type: general
      – SubjectFull: Aged, 80 and over
        Type: general
      – SubjectFull: Boosting Machine Learning Algorithms
        Type: general
      – SubjectFull: Prediction Algorithms
        Type: general
      – SubjectFull: Cognitive Dysfunction diagnosis
        Type: general
      – SubjectFull: Cognitive Dysfunction classification
        Type: general
      – SubjectFull: Dementia diagnosis
        Type: general
      – SubjectFull: Dementia classification
        Type: general
      – SubjectFull: Disease Progression
        Type: general
      – SubjectFull: Machine Learning
        Type: general
    Titles:
      – TitleFull: Resource-stratified machine learning framework for cognitive status classification and mild cognitive impairment to dementia progression prediction.
        Type: main
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            NameFull: Yang J
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            NameFull: Paschalidis IC
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          Dates:
            – D: 07
              M: 03
              Text: 2026 Mar 07
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-electronic
              Value: 1758-9193
          Numbering:
            – Type: volume
              Value: 18
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: Alzheimer's research & therapy
              Type: main
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