This paper is in the following e-collection/theme issue:

Artificial Intelligence (1371) Machine Learning (1465) Dementia and Cognitive Decline (481) Clinical Information and Decision Making (1407)

Published on in Vol 23, No 2 (2021): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/20298, first published .
A Risk Prediction Model Based on Machine Learning for Cognitive Impairment Among Chinese Community-Dwelling Elderly People With Normal Cognition: Development and Validation Study

A Risk Prediction Model Based on Machine Learning for Cognitive Impairment Among Chinese Community-Dwelling Elderly People With Normal Cognition: Development and Validation Study

A Risk Prediction Model Based on Machine Learning for Cognitive Impairment Among Chinese Community-Dwelling Elderly People With Normal Cognition: Development and Validation Study

Authors of this article:

Mingyue Hu1 Author Orcid Image ;   Xinhui Shu2 Author Orcid Image ;   Gang Yu3 Author Orcid Image ;   Xinyin Wu4 Author Orcid Image ;   Maritta Välimäki1, 5 Author Orcid Image ;   Hui Feng1, 6, 7 Author Orcid Image
Article Authors Cited by (61) Tweetations (7) Metrics

Journals

  1. Xinran Z, Shumei Z, Xueying Z, Linan W, Ying G, Peng W, Yahong H, Longting M, Jing W. Construction of a predictive model for cognitive impairment risk in patients with advanced cancer. International Journal of Nursing Practice 2023;29(4) View
  2. Paul S, Maindarkar M, Saxena S, Saba L, Turk M, Kalra M, Krishnan P, Suri J. Bias Investigation in Artificial Intelligence Systems for Early Detection of Parkinson’s Disease: A Narrative Review. Diagnostics 2022;12(1):166 View
  3. Huang M, Gao X, Zhao R, Dong C, Gu Z, Gao J. Development and validation of a nomogram for predicting mild cognitive impairment in middle-aged and elderly people. Asian Journal of Psychiatry 2022;75:103224 View
  4. Macekova Z, Fazekas T, Krivosova M, Dragasek J, Zufkova V, Klimas J, Snopkova M. Identification of a Link between Suspected Metabolic Syndrome and Cognitive Impairment within Pharmaceutical Care in Adults over 75 Years of Age. Healthcare 2023;11(5):718 View
  5. Hu M, Gao Y, Kwok T, Shao Z, Xiao L, Feng H. Derivation and Validation of the Cognitive Impairment Prediction Model in Older Adults: A National Cohort Study. Frontiers in Aging Neuroscience 2022;14 View
  6. Wang S, Wang W, Li X, Liu Y, Wei J, Zheng J, Wang Y, Ye B, Zhao R, Huang Y, Peng S, Zheng Y, Zeng Y. Using machine learning algorithms for predicting cognitive impairment and identifying modifiable factors among Chinese elderly people. Frontiers in Aging Neuroscience 2022;14 View
  7. Tan W, Hargreaves C, Chen C, Hilal S. A Machine Learning Approach for Early Diagnosis of Cognitive Impairment Using Population-Based Data. Journal of Alzheimer's Disease 2023;91(1):449 View
  8. Pu L, Pan D, Wang H, He X, Zhang X, Yu Z, Hu N, Du Y, He S, Liu X, Li J. A predictive model for the risk of cognitive impairment in community middle-aged and older adults. Asian Journal of Psychiatry 2023;79:103380 View
  9. Szlejf C, Batista A, Bertola L, Lotufo P, Benseãor I, Chiavegatto Filho A, Suemoto C. Data-driven decision making for the screening of cognitive impairment in primary care: a machine learning approach using data from the ELSA-Brasil study. Brazilian Journal of Medical and Biological Research 2023;56 View
  10. Geethadevi G, Peel R, Bell J, Cross A, Hancock S, Ilomaki J, Tang T, Attia J, George J. Validity of three risk prediction models for dementia or cognitive impairment in Australia. Age and Ageing 2022;51(12) View
  11. Jiang Z, Cai Y, Liu S, Ye P, Yang Y, Lin G, Li S, Xu Y, Zheng Y, Bao Z, Nie S, Gu W. Decreased default mode network functional connectivity with visual processing regions as potential biomarkers for delayed neurocognitive recovery: A resting-state fMRI study and machine-learning analysis. Frontiers in Aging Neuroscience 2023;14 View
  12. Liu J, Cui K, Chen Q, Li Z, Fu J, Gong X, Xu H. Association of walking speed with cognitive function in Chinese older adults: A nationally representative cohort study. Frontiers in Aging Neuroscience 2022;14 View
  13. Xu Q, Zou K, Deng Z, Zhou J, Dang X, Zhu S, Liu L, Fang C. A Study of Dementia Prediction Models Based on Machine Learning with Survey Data of Community-Dwelling Elderly People in China. Journal of Alzheimer's Disease 2022;89(2):669 View
  14. Dolcet-Negre M, Imaz Aguayo L, García-de-Eulate R, Martí-Andrés G, Fernández-Matarrubia M, Domínguez P, Fernández-Seara M, Riverol M, Wang Z. Predicting Conversion from Subjective Cognitive Decline to Mild Cognitive Impairment and Alzheimer’s Disease Dementia Using Ensemble Machine Learning. Journal of Alzheimer's Disease 2023;93(1):125 View
  15. Wang J, Chen H, Wang H, Liu W, Peng D, Zhao Q, Xiao M. A Risk Prediction Model for Physical Restraints Among Older Chinese Adults in Long-term Care Facilities: Machine Learning Study. Journal of Medical Internet Research 2023;25:e43815 View
  16. Peng S, Zhou J, Xiong S, Liu X, Pei M, Wang Y, Wang X, Zhang P. Construction and validation of cognitive frailty risk prediction model for elderly patients with multimorbidity in Chinese community based on non-traditional factors. BMC Psychiatry 2023;23(1) View
  17. Zhang H, Ni R, Cao Y, Chen Y, Fang W, Hu W, Pan G. Interaction between home and community-based services and PM2.5 on cognition: A prospective cohort study of Chinese elderly. Environmental Research 2023;231:116048 View
  18. Leme D, de Oliveira C, Lipsitz L. Machine Learning Models to Predict Future Frailty in Community-Dwelling Middle-Aged and Older Adults: The ELSA Cohort Study. The Journals of Gerontology: Series A 2023;78(11):2176 View
  19. Jin S, Li C, Miao J, Sun J, Yang Z, Cao X, Sun K, Liu X, Ma L, Xu X, Liu Z. Sociodemographic Factors Predict Incident Mild Cognitive Impairment: A Brief Review and Empirical Study. Journal of the American Medical Directors Association 2023;24(12):1959 View
  20. Zhang H, Chen Y, Ni R, Cao Y, Fang W, Hu W, Pan G. Traffic-related air pollution, adherence to healthy lifestyles, and risk of cognitive impairment: A nationwide population-based study. Ecotoxicology and Environmental Safety 2023;262:115349 View
  21. Rodríguez-Sánchez I, Pérez-Rodríguez P. La revolución gerontotecnológica: integrando la inteligencia artificial para mejorar la vida de las personas mayores. Revista Española de Geriatría y Gerontología 2024;59(1):101409 View
  22. Bai A, Zhao M, Zhang T, Yang C, Yan J, Wang G, Zhang P, Xu W, Hu Y. Development and validation of a nomogram-assisted tool to predict potentially reversible cognitive frailty in Chinese community-living older adults. Aging Clinical and Experimental Research 2023;35(10):2145 View
  23. Wang Y, Hou R, Ni B, Jiang Y, Zhang Y. Development and validation of a prediction model based on machine learning algorithms for predicting the risk of heart failure in middle‐aged and older US people with prediabetes or diabetes. Clinical Cardiology 2023;46(10):1234 View
  24. Xie X, Li J, Zhong Y, Fang Z, Feng Y, Chen C, Zou J, Si Y. A risk prediction model based on machine learning for postoperative cognitive dysfunction in elderly patients with non-cardiac surgery. Aging Clinical and Experimental Research 2023;35(12):2951 View
  25. Zhao X, Li J, Xie X, Fang Z, Feng Y, Zhong Y, Chen C, Huang K, Ge C, Shi H, Si Y, Zou J. Online interpretable dynamic prediction models for postoperative delirium after cardiac surgery under cardiopulmonary bypass developed based on machine learning algorithms: A retrospective cohort study. Journal of Psychosomatic Research 2024;176:111553 View
  26. Zhu Y, Cheng J, Li Y, Pan D, Li H, Xu Y, Du Z, Lei M, Xiao S, Shen Q, Shi Z, Tang Y. Progression of cognitive dysfunction in NPC survivors with radiation-induced brain necrosis: A prospective cohort. Radiotherapy and Oncology 2024;190:110033 View
  27. Ávila-Jiménez J, Cantón-Habas V, Carrera-González M, Rich-Ruiz M, Ventura S. A deep learning model for Alzheimer’s disease diagnosis based on patient clinical records. Computers in Biology and Medicine 2024;169:107814 View
  28. Das A, Dhillon P. Application of machine learning in measurement of ageing and geriatric diseases: a systematic review. BMC Geriatrics 2023;23(1) View
  29. Brech G, da Silva V, Alonso A, Machado-Lima A, da Silva D, Micillo G, Bastos M, de Aquino R. Quality of life and socio-demographic factors associated with nutritional risk in Brazilian community-dwelling individuals aged 80 and over: cluster analysis and ensemble methods. Frontiers in Nutrition 2024;10 View
  30. Zhu J, Wu Y, Lin S, Duan S, Wang X, Fang Y. Identifying and predicting physical limitation and cognitive decline trajectory group of older adults in China: A data-driven machine learning analysis. Journal of Affective Disorders 2024;350:590 View
  31. Cai S, Zheng T, Wang K, Zhu H. Clinical study of different prediction models in predicting diabetic nephropathy in patients with type 2 diabetes mellitus. World Journal of Diabetes 2024;15(1):43 View
  32. Veneziani I, Marra A, Formica C, Grimaldi A, Marino S, Quartarone A, Maresca G. Applications of Artificial Intelligence in the Neuropsychological Assessment of Dementia: A Systematic Review. Journal of Personalized Medicine 2024;14(1):113 View
  33. Wang S, Guo P, Huang C, Zhang Y, Xiang B, Zeng J, Zhou F, Xie X, Guo Y, Yang M. The association between closed-eye unipedal standing and the risk of cognitive impairment in the elderly: a 7-year community-based cohort study in Wuhan, China. Frontiers in Aging Neuroscience 2024;16 View
  34. Huang J, Zeng X, Ning H, Peng R, Guo Y, Hu M, Feng H. Development and validation of prediction model for older adults with cognitive frailty. Aging Clinical and Experimental Research 2024;36(1) View
  35. Sakal C, Li T, Li J, Li X. Identifying Predictive Risk Factors for Future Cognitive Impairment Among Chinese Older Adults: Longitudinal Prediction Study. JMIR Aging 2024;7:e53240 View
  36. Zhang W, Wang J, Xie F, Wang X, Dong S, Luo N, Li F, Li Y. Development and validation of machine learning models to predict frailty risk for elderly. Journal of Advanced Nursing 2024;80(12):5064 View
  37. Chen L, Qiu R, Wang B, Liu J, Li X, Hou Z, Wu T, Cao H, Ji X, Zhang P, Zhang Y, Xue M, Qiu L, Wang L, Wei Y, Chen M. Investigating the association between inflammation mediated by mushroom consumption and mild cognitive impairment in Chinese older adults. Food & Function 2024;15(10):5343 View
  38. Ran W, Yu Q. Data-driven clustering approach to identify novel clusters of high cognitive impairment risk among Chinese community-dwelling elderly people with normal cognition: A national cohort study. Journal of Global Health 2024;14 View
  39. Zhang X, Fan H, Guo C, Li Y, Han X, Xu Y, Wang H, Zhang T. Establishment of a mild cognitive impairment risk model in middle-aged and older adults: a longitudinal study. Neurological Sciences 2024;45(9):4269 View
  40. Wang L, Xian X, Zhou M, Xu K, Cao S, Cheng J, Dai W, Zhang W, Ye M. Anti-Inflammatory Diet and Protein-Enriched Diet Can Reduce the Risk of Cognitive Impairment among Older Adults: A Nationwide Cross-Sectional Research. Nutrients 2024;16(9):1333 View
  41. Zhang Y, Xu J, Zhang C, Zhang X, Yuan X, Ni W, Zhang H, Zheng Y, Zhao Z. Community screening for dementia among older adults in China: a machine learning-based strategy. BMC Public Health 2024;24(1) View
  42. Shao Z, Huang J, Feng H, Hu M. Optimizing the physical activity intervention for older adults with mild cognitive impairment: a factorial randomized trial. Frontiers in Sports and Active Living 2024;6 View
  43. Cui X, Zheng X, Lu Y. Prediction Model for Cognitive Impairment among Disabled Older Adults: A Development and Validation Study. Healthcare 2024;12(10):1028 View
  44. Yu Q, Jiang X, Yan J, Yu H. Development and validation of a risk prediction model for mild cognitive impairment in elderly patients with type 2 diabetes mellitus. Geriatric Nursing 2024;58:119 View
  45. Li Y, Xin J, Fang S, Wang F, Jin Y, Wang L. Development and Validation of a Predictive Model for Early Identification of Cognitive Impairment Risk in Community-Based Hypertensive Patients. Journal of Applied Gerontology 2024;43(12):1867 View
  46. Huang A, Zhang D, Zhang L, Zhou Z. Predictors and consequences of visual trajectories in Chinese older population: A growth mixture model. Journal of Global Health 2024;14 View
  47. Cui Y, Choi M. Assessment of the Daily Living Activities of Older People (2004–2023): A Bibliometric and Visual Analysis. Healthcare 2024;12(12):1180 View
  48. Xu T, Zong T, Liu J, Zhang L, Ge H, Yang R, Liu Z. Correlation between hearing loss and mild cognitive impairment in the elderly population: Mendelian randomization and cross-sectional study. Frontiers in Aging Neuroscience 2024;16 View
  49. Wei L, Pan D, Wu S, Wang H, Wang J, Guo L, Gu Y. A glimpse into the future: revealing the key factors for survival in cognitively impaired patients. Frontiers in Aging Neuroscience 2024;16 View
  50. Li J, Li J, Zhu H, Liu M, Li T, He Y, Xu Y, Huang F, Qin Q. Prediction of Cognitive Impairment Risk among Older Adults: A Machine Learning-Based Comparative Study and Model Development. Dementia and Geriatric Cognitive Disorders 2024;53(4):169 View
  51. Zhang Y, Xie L, Wu R, Zhang C, Zhuang Q, Dai W, Zhou M, Li X. Predicting the Risk of Postoperative Delirium in Elderly Patients Undergoing Hip Arthroplasty: Development and Assessment of a Novel Nomogram. Journal of Investigative Surgery 2024;37(1) View
  52. Xiao Z, Zhou X, Zhao Q, Cao Y, Ding D. Significance of plasma p‐tau217 in predicting long‐term dementia risk in older community residents: Insights from machine learning approaches. Alzheimer's & Dementia 2024 View
  53. Herrera C, Gimenes F, Herrera J, Cavalli R. Development of Automated Triggers in Ambulatory Settings in Brazil: Protocol for a Machine Learning–Based Design Thinking Study. JMIR Research Protocols 2024;13:e55466 View
  54. Choi J, Lee H, Kim‐Godwin Y. Decoding machine learning in nursing research: A scoping review of effective algorithms. Journal of Nursing Scholarship 2024 View
  55. Zhao X, Liu D, Wang J. Association of Tai Chi and Square Dance with Cognitive Function in Chinese Older Adults. Healthcare 2024;12(18):1878 View
  56. Gao H, Schneider S, Hernandez R, Harris J, Maupin D, Junghaenel D, Kapteyn A, Stone A, Zelinski E, Meijer E, Lee P, Orriens B, Jin H. Early Identification of Cognitive Impairment in Community Environments Through Modeling Subtle Inconsistencies in Questionnaire Responses: Machine Learning Model Development and Validation. JMIR Formative Research 2024;8:e54335 View
  57. Gong C, Cai T, Wang Y, Xiong X, Zhou Y, Zhou T, Sun Q, Huang H. Development and Validation of a Nocturnal Hypoglycaemia Risk Model for Patients With Type 2 Diabetes Mellitus. Nursing Open 2024;11(10) View
  58. Feng M, Meng F, Jia Y, Wang Y, Ji G, Gao C, Luo J. Exploration of Risk Factors for Cardiovascular Disease in Patients with Rheumatoid Arthritis: A Retrospective Study. Inflammation 2024 View
  59. Hao Z, Zhang X, Wang Y. Evidence of the Long-Term Protective Effect of Moderate-Intensity Physical Activity on Cognitive Function in Middle-Aged and Elderly Individuals: A Predictive Analysis of Longitudinal Studies. Life 2024;14(10):1343 View
  60. Liang Z, Jin W, Huang L, Chen H. Body mass index, waist circumference, hip circumference, abdominal volume index, and cognitive function in older Chinese people: a nationwide study. BMC Geriatrics 2024;24(1) View
  61. Xie X, Huang L, Liu D, Cheng G, Hu F, Zhou J, Zhang J, Han G, Geng J, Liu X, Wang J, Zeng D, Liu J, Nie Q, Song D, Li S, Cai C, Cui Y, Xu L, Ou Y, Chen X, Zhou Y, Chen Y, Li J, Wei Z, Wu Q, Mei Y, Song S, Tan W, Zhao Q, Ding D, Zeng Y. Predicting progression to dementia using auditory verbal learning test in community-dwelling older adults based on machine learning. The American Journal of Geriatric Psychiatry 2024 View