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Stroke patients' utilisation of extrinsic feedback from computer-based technology in the home: a multiple case study realistic evaluation
Conclusions: Findings suggest that the theory-driven mechanisms underpinning the utilisation of feedback from computer-based technology for home-based upper-limb post-stroke rehabilitation are dependent on key elements of computer feedback and the personal and environmental context. The identification of these elements may therefore inform the development of technology; therapy education and the subsequent adoption of technology and a self-management paradigm; long-term self-managed rehabilitation; and importantly, improvements in the physical and psychosocial aspects of recovery.
Source: BMC Medical Informatics and Decision Making - Latest articles - June 5, 2014 Category: Information Technology Authors: Jack ParkerSusan MawsonGail MountainNasrin NasrHuiru Zheng Source Type: research

Stroke patients¿ utilisation of extrinsic feedback from computer-based technology in the home: a multiple case study realistic evaluation
Conclusions: Findings suggest that the theory-driven mechanisms underpinning the utilisation of feedback from computer-based technology for home-based upper-limb post-stroke rehabilitation are dependent on key elements of computer feedback and the personal and environmental context. The identification of these elements may therefore inform the development of technology; therapy education and the subsequent adoption of technology and a self-management paradigm; long-term self-managed rehabilitation; and importantly, improvements in the physical and psychosocial aspects of recovery.
Source: BMC Medical Informatics and Decision Making - Latest articles - June 5, 2014 Category: Information Technology Authors: Jack ParkerSusan MawsonGail MountainNasrin NasrHuiru Zheng Source Type: research

Using machine learning models to improve stroke risk level classification methods of China national stroke screening
With the character of high incidence, high prevalence and high mortality, stroke has brought a heavy burden to families and society in China. In 2009, the Ministry of Health of China launched the China nationa...
Source: BMC Medical Informatics and Decision Making - December 10, 2019 Category: Information Technology Authors: Xuemeng Li, Di Bian, Jinghui Yu, Mei Li and Dongsheng Zhao Tags: Research article Source Type: research

Assessing stroke severity using electronic health record data: a machine learning approach
Stroke severity is an important predictor of patient outcomes and is commonly measured with the National Institutes of Health Stroke Scale (NIHSS) scores. Because these scores are often recorded as free text i...
Source: BMC Medical Informatics and Decision Making - January 8, 2020 Category: Information Technology Authors: Emily Kogan, Kathryn Twyman, Jesse Heap, Dejan Milentijevic, Jennifer H. Lin and Mark Alberts Tags: Research article Source Type: research

Machine learning in a real-world PFO study: analysis of data from multi-centers in China
The association of patent foreman ovale (PFO) and cryptogenic stroke has been studied for years. Although device closure overall decreases the risk for recurrent stroke, treatment effects varied across differe...
Source: BMC Medical Informatics and Decision Making - November 24, 2022 Category: Information Technology Authors: Dongling Luo, Ziyang Yang, Gangcheng Zhang, Qunshan Shen, Hongwei Zhang, Junxing Lai, Hui Hu, Jianxin He, Shulin Wu and Caojin Zhang Tags: Research Source Type: research