Use of a multilayer perceptron to create a prediction model for dressing independence in a small sample at a single facility-Reference-Cited by-同舟云学术

Use of a multilayer perceptron to create a prediction model for dressing independence in a small sample at a single facility

Published:2019 Issue:1 Volume:31 Page:69-74
ISSN:0915-5287
Container-title:Journal of Physical Therapy Science
language:en
Short-container-title:J Phys Ther Sci

Author:

Fujita Takaaki¹,Sato Atsushi²,Narita Akira³,Sone Toshimasa¹,Iokawa Kazuaki⁴,Tsuchiya Kenji⁵,Yamane Kazuhiro⁶,Yamamoto Yuichi⁶,Ohira Yoko⁶,Otsuki Koji⁶

Affiliation:

1. Department of Rehabilitation, Faculty of Health Sciences, Tohoku Fukushi University: 1-8-1 Kunimi, Aoba-ku, Sendai-shi, Miyagi 981-8522, Japan

2. Department of Rehabilitation, Care Center Moriyama, Japan

3. Tohoku Medical Megabank Organization, Tohoku University, Japan

4. Preparing Section for New Faculty of Medical Science, Fukushima Medical University, Japan

5. Department of Rehabilitation Sciences, Gunma University Graduate School of Health Sciences, Japan

6. Department of Rehabilitation, Kita-Fukushima Medical Center, Japan

Publisher

Society of Physical Therapy Science

Subject

Physical Therapy, Sports Therapy and Rehabilitation

Link

https://www.jstage.jst.go.jp/article/jpts/31/1/31_jpts-2018-333/_pdf

Reference19 articles.

1. 1) Mozaffarian D, Benjamin EJ, Go AS, et al. American Heart Association Statistics Committee and Stroke Statistics Subcommittee: Heart disease and stroke statistics—2015 update: a report from the American Heart Association. Circulation, 2015, 131: e29–e322.

2. 2) Edmans JA, Towle D, Lincoln NB: The recovery of perceptual problems after stroke and the impact on daily life. Clin Rehabil, 1991, 5: 301–309.

3. 3) Veerbeek JM, Kwakkel G, van Wegen EE, et al.: Early prediction of outcome of activities of daily living after stroke: a systematic review. Stroke, 2011, 42: 1482–1488.

4. 4) Sato A, Fujita T, Ohashi Y, et al.: A prediction model for activities of daily living for stroke patients in a convalescent Rehabilitation Ward. J Allied Health Sci, 2016, 7: 1–6.

5. 5) Cheng CA: Hung-Wen Chiu: An artificial neural network model for the evaluation of carotid artery stenting prognosis using a national-wide database. Conf Proc IEEE Eng Med Biol Soc, 2017, 2566–2569.

Cited by 11 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Deep Learning Approach for Modeling the Power Consumption and Delay of Logic Circuits Employing GNRFET Technology;Electronics;2024-07-29

2. Anomaly Detection and Artificial Intelligence Identified the Pathogenic Role of Apoptosis and RELB Proto-Oncogene, NF-kB Subunit in Diffuse Large B-Cell Lymphoma;BioMedInformatics;2024-06-07

3. Predicting rectal cancer prognosis from histopathological images and clinical information using multi-modal deep learning;Frontiers in Oncology;2024-04-15

4. A Scoping Review of Prognosis Prediction Studies Focusing on Activity and Participation Among Patients with Stroke in Japan;Asian Journal of Occupational Therapy;2024

5. Analysis of influencing factors of college students' entrepreneurship based on decision tree model;International Journal of Computing Science and Mathematics;2023