Profile

New User

New to Graphy Publications? Create an account to get started today.

Registered Users

Have an account? Sign in now.

How to Cite | Author Information | Publication History | Funding Information
International Journal of Nursing & Clinical Practices Volume 9 (2022), Article ID 9:IJNCP-366, 4 Pages
https://doi.org/10.15344/2394-4978/2022/366
Original Article
A Novel Method to Predict Cognitive and Physical Function, Muscle Weight and Quality of Life in Japanese Elderly Using Deep Learning

Noboru Hasegawa1*, Seiji Tsuchiya2, Yoshihito Tsubouchi3, Takako Yamada4, Nobuko Shimizu5, Mayumi Kato6, and Miyako Mochizuki7


1 Graduate School of Nursing, Doshisha Women’s College, Kodo, Kyotanabe, Kyoto 610-0395, Japan
2 Doshisha University, 1-3 Tatara Miyakodani, Kyotanabe-shi, Kyoto 610-0394, Japan
3 Naragakuen University, 3-15-1 Nakatomigaoka, Nara 631-8524, Japan
4 Bukkyo University, 7 Higashitoganoo-cho, Nishinokyo, Nakagyo-ku, Kyoto 604-8418, Japan
5 Toyama Prefectural University, 2-2-78 Nishinagae, Toyama 930-0975, Japan
6 Aichi Medical College for Physical and Occupational Therapy, 519 Ichiba, Kiyosu-City, Aichi 452-0931, Japan
7 Kyoto Bunkyo Junior College, 80 Senzoku, Makishima-cho, Uji, Kyoto 611-0041, Japan
Prof. Noboru Hasegawa, Graduate School of Nursing, Doshisha Women’s College, Kodo, Kyotanabe, Kyoto 610-0395, Japan, Tel: +81- 774-65-8855; E-mail: nhasegaw@dwc.doshisha.ac.jp
14 December 2022; 18 December 2022; 20 December 2022
Hasegawa N, Tsuchiya S, Tsubouchi Y, Yamada T, Shimizu N, et al. (2022) A Novel Method to Predict Cognitive and Physical Function, Muscle Weight and Quality of Life in Japanese Elderly Using Deep Learning. Int J Nurs Clin Pract 9: 366. doi: https://doi.org/10.15344/2394-4978/2022/366
This work was supported by KAKENHI (grant number 22K11220).
© 2022 Hasegawa et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

References

  1. Ferri CP, Prince M, Brayne C, Brodaty H, Fratiglioni L, Ganguli M, et al. (2005) Global prevalence of dementia: a Delphi consensus study. Lancet 366: 2112-7. View
  2. Prince M, Bryce R, Albanese E, Wimo A, Ribeiro W et al. (2013) The global prevalence of dementia: a systematic review and metaanalysis. Alzheimer's & Dementia 9: 63-75. View
  3. LeCun Y, Bengio Y, Hinton G (2015) Deep Learning. Nature 521: 436-444. View
  4. Saravi B, Zink A, Ülkümen S, Couillard-Despres S, Hassel F et al. (2022) Performance of artificial intelligence-based algorithms to predict prolonged length of stay after lumbar decompression surgery. J Clin Med 11: 4050- 4063. View
  5. De Ramón Fernández A, Ruiz Fernández D, Prieto Sánchez MT (2022) Prediction of the mode of delivery using artificial intelligence algorithms. Comput Methods Programs Biomed 219: 106740-106746. View
  6. Rostami M, Oussalah M (2022) A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest. Inform Med Unlocked 30: 100941-100963. View
  7. Jia Y, Chang CH, Hughes TF, Jacobsen E, Wang S. et al. (2020) Predictors of dementia in the oldest old: A novel machine learning approach. Alzheimer Dis Assoc Disord 34: 325-332. View
  8. Folstein MF, Folstein SE, McHugh PR (1975) “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12:189-198. View
  9. Nasredding ZS, Phillips N., Bédirian V, Charbonneau S, Whitehead V, et al. (2005) The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc 53: 695-699. View
  10. Harmin EK, Gustafsson G, Jaracz K (2012) Quality of life among the elderly with locomotor disabilities in Sweden and Poland in the 1990s. Qual Life Res 21:281–289. View
  11. Kriegeskorte N, Golan T (2019) Neural network models and deep learning. Curr Biol 29: R231-R236. View
  12. Chang WT, Wu CH, Hsu LW, Chen PW, Yu JR et al. (2017) Serum vitamin D, intact parathyroid hormone, and Fetuin A concentrations were associated with geriatric sarcopenia and cardiac hypertrophy. Sci Rep 7: 40996. View
  13. Kupisz-Urbanska M, Płudowski P Marcinowska-Suchowierska E (2021) Vitamin D deficiency in older patients-Problems of sarcopenia, drug interactions, management in deficiency. Nutr 13: 1247-1257. View
  14. Papathanasiou IV, Rammogianni A, Papagiannis D, Malli F, Mantzaris DC et al. (2021) Frailty and quality of life among community-dwelling older adults. Cureus 13: 13049-13057. View
  15. Hasegawa N, Mochizuki M, Kato M, Shimizu N, Yamada T (2018) Vitamin D3 supplementation improved physical performance in healthy older adults in Japan: A pilot study. Health 10: 1200-1209. View
Best viewed in Mozilla Firefox, Google Chrome, Safari, Above IE 9.0 version
Privacy Policy | Copyright & Permissions
Copyright © 2013-2026 Graphy Publications, All Rights Reserved.