109 related articles for article (PubMed ID: 38776891)
1. Prediction of Cognitive Impairment Risk among Older Adults: A Machine Learning-Based Comparative Study and Model Development.
Li J; Li J; Zhu H; Liu M; Li T; He Y; Xu Y; Huang F; Qin Q
Dement Geriatr Cogn Disord; 2024 May; ():1-11. PubMed ID: 38776891
[TBL] [Abstract][Full Text] [Related]
2. A Risk Prediction Model Based on Machine Learning for Cognitive Impairment Among Chinese Community-Dwelling Elderly People With Normal Cognition: Development and Validation Study.
Hu M; Shu X; Yu G; Wu X; Välimäki M; Feng H
J Med Internet Res; 2021 Feb; 23(2):e20298. PubMed ID: 33625369
[TBL] [Abstract][Full Text] [Related]
3. A Risk Prediction Model for Physical Restraints Among Older Chinese Adults in Long-term Care Facilities: Machine Learning Study.
Wang J; Chen H; Wang H; Liu W; Peng D; Zhao Q; Xiao M
J Med Internet Res; 2023 Apr; 25():e43815. PubMed ID: 37023416
[TBL] [Abstract][Full Text] [Related]
4. Identifying Predictive Risk Factors for Future Cognitive Impairment Among Chinese Older Adults: Longitudinal Prediction Study.
Sakal C; Li T; Li J; Li X
JMIR Aging; 2024 Mar; 7():e53240. PubMed ID: 38534042
[TBL] [Abstract][Full Text] [Related]
5. Prediction of positive pulmonary nodules based on machine learning algorithm combined with central carbon metabolism data.
Liu JJ; Shen WB; Qin QR; Li JW; Li X; Liu MY; Hu WL; Wu YY; Huang F
J Cancer Res Clin Oncol; 2024 Jan; 150(2):33. PubMed ID: 38270703
[TBL] [Abstract][Full Text] [Related]
6. Interpretable machine learning model for early prediction of delirium in elderly patients following intensive care unit admission: a derivation and validation study.
Tang D; Ma C; Xu Y
Front Med (Lausanne); 2024; 11():1399848. PubMed ID: 38828233
[TBL] [Abstract][Full Text] [Related]
7. Predictive model and risk analysis for peripheral vascular disease in type 2 diabetes mellitus patients using machine learning and shapley additive explanation.
Liu L; Bi B; Cao L; Gui M; Ju F
Front Endocrinol (Lausanne); 2024; 15():1320335. PubMed ID: 38481447
[TBL] [Abstract][Full Text] [Related]
8. Community screening for dementia among older adults in China: a machine learning-based strategy.
Zhang Y; Xu J; Zhang C; Zhang X; Yuan X; Ni W; Zhang H; Zheng Y; Zhao Z
BMC Public Health; 2024 May; 24(1):1206. PubMed ID: 38693495
[TBL] [Abstract][Full Text] [Related]
9. Machine learning for the prediction of cognitive impairment in older adults.
Li W; Zeng L; Yuan S; Shang Y; Zhuang W; Chen Z; Lyu J
Front Neurosci; 2023; 17():1158141. PubMed ID: 37179565
[TBL] [Abstract][Full Text] [Related]
10. Application value of the automated machine learning model based on modified CT index combined with serological indices in the early prediction of lung cancer.
Meng L; Zhu P; Xia K
Front Public Health; 2024; 12():1368217. PubMed ID: 38645446
[TBL] [Abstract][Full Text] [Related]
11. Disability risk prediction model based on machine learning among Chinese healthy older adults: results from the China Health and Retirement Longitudinal Study.
Han Y; Wang S
Front Public Health; 2023; 11():1271595. PubMed ID: 38026309
[TBL] [Abstract][Full Text] [Related]
12. Interpretable machine learning for 28-day all-cause in-hospital mortality prediction in critically ill patients with heart failure combined with hypertension: A retrospective cohort study based on medical information mart for intensive care database-IV and eICU databases.
Peng S; Huang J; Liu X; Deng J; Sun C; Tang J; Chen H; Cao W; Wang W; Duan X; Luo X; Peng S
Front Cardiovasc Med; 2022; 9():994359. PubMed ID: 36312291
[TBL] [Abstract][Full Text] [Related]
13. Interpretable machine learning model for early prediction of 28-day mortality in ICU patients with sepsis-induced coagulopathy: development and validation.
Zhou S; Lu Z; Liu Y; Wang M; Zhou W; Cui X; Zhang J; Xiao W; Hua T; Zhu H; Yang M
Eur J Med Res; 2024 Jan; 29(1):14. PubMed ID: 38172962
[TBL] [Abstract][Full Text] [Related]
14. Prediction of subjective cognitive decline after corpus callosum infarction by an interpretable machine learning-derived early warning strategy.
Xu Y; Sun X; Liu Y; Huang Y; Liang M; Sun R; Yin G; Song C; Ding Q; Du B; Bi X
Front Neurol; 2023; 14():1123607. PubMed ID: 37416313
[TBL] [Abstract][Full Text] [Related]
15. A risk prediction model based on machine learning for early cognitive impairment in hypertension: Development and validation study.
Zhong X; Yu J; Jiang F; Chen H; Wang Z; Teng J; Jiao H
Front Public Health; 2023; 11():1143019. PubMed ID: 36969637
[TBL] [Abstract][Full Text] [Related]
16. Prediction of conversion to dementia using interpretable machine learning in patients with amnestic mild cognitive impairment.
Chun MY; Park CJ; Kim J; Jeong JH; Jang H; Kim K; Seo SW
Front Aging Neurosci; 2022; 14():898940. PubMed ID: 35992586
[TBL] [Abstract][Full Text] [Related]
17. A machine learning-based prediction model pre-operatively for functional recovery after 1-year of hip fracture surgery in older people.
Lin C; Liang Z; Liu J; Sun W
Front Surg; 2023; 10():1160085. PubMed ID: 37351328
[TBL] [Abstract][Full Text] [Related]
18. Supervised learning applied to classifying fallers versus non-fallers among older adults with cancer.
Ramsdale E; Kunduru M; Smith L; Culakova E; Shen J; Meng S; Zand M; Anand A
J Geriatr Oncol; 2023 May; 14(4):101498. PubMed ID: 37084629
[TBL] [Abstract][Full Text] [Related]
19.
; ; . PubMed ID:
[No Abstract] [Full Text] [Related]
20.
; ; . PubMed ID:
[No Abstract] [Full Text] [Related]
[Next] [New Search]