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Using Artificial Intelligence in Predicting Ischemic Stroke Events After Percutaneous Coronary Intervention
CONCLUSIONS: The RF model accurately predicts short- and long-term risk of IS and outperforms logistic regression analysis in patients undergoing PCI. Patients with periprocedural stroke may benefit from aggressive management to reduce the future risk of IS.PMID:37410747
Source: The Journal of Invasive Cardiology - July 6, 2023 Category: Cardiology Authors: Chieh-Ju Chao Pradyumna Agasthi Timothy Barry Chia-Chun Chiang Panwen Wang Hasan Ashraf Farouk Mookadam Amith R Seri Nithin Venepally Mohamed Allam Sai Harika Pujari Anil Sriramoju Mohamed Sleem Said Alsidawi Mackram Eleid Nirat Beohar Floyd D Fortuin Eri Source Type: research

IJERPH, Vol. 20, Pages 4123: Predicting Arm Nonuse in Individuals with Good Arm Motor Function after Stroke Rehabilitation: A Machine Learning Study
Keh-Chung Lin Many stroke survivors demonstrate arm nonuse despite good arm motor function. This retrospective secondary analysis aims to identify predictors of arm nonusers with good arm motor function after stroke rehabilitation. A total of 78 participants were categorized into 2 groups using the Fugl-Meyer Assessment Upper Extremity Scale (FMA-UE) and the Motor Activity Log Amount of Use (MAL-AOU). Group 1 comprised participants with good motor function (FMA-UE ≥ 31) and low daily upper limb use (MAL-AOU ≤ 2.5), and group 2 comprised all other participants. Feature selection analysis was perf...
Source: International Journal of Environmental Research and Public Health - February 25, 2023 Category: Environmental Health Authors: Yu-Wen Chen Yi-Chun Li Chien-Yu Huang Chia-Jung Lin Chia-Jui Tien Wen-Shiang Chen Chia-Ling Chen Keh-Chung Lin Tags: Article Source Type: research

Predicting patient-reported outcome of activities of daily living in stroke rehabilitation: a machine learning study
Machine Learning is increasingly used to predict rehabilitation outcomes in stroke in the context of precision rehabilitation and patient-centered care. However, predictors for patient-centered outcome measure...
Source: Journal of NeuroEngineering and Rehabilitation - February 23, 2023 Category: Rehabilitation Authors: Yu-Wen Chen, Keh-chung Lin, Yi-chun Li and Chia-Jung Lin Tags: Research Source Type: research

Development of a Short-Form Stroke Impact Scale Using a Machine Learning Algorithm for Patients at the Subacute Stage
CONCLUSIONS AND RELEVANCE: The ML-SIS uses about half of the items in the SIS 3.0, has an estimated administration time of 10 min, and provides valid scores comparable to those of the original measure. Thus, the ML-SIS may be an efficient alternative to the SIS 3.0. What This Article Adds: The ML-SIS, a short form of the SIS 3.0 developed using a machine learning algorithm, shows good potential to be an efficient and informative measure for clinical settings, providing scores that are valid and comparable to those of the original measure.PMID:36410404 | DOI:10.5014/ajot.2022.049136
Source: The American Journal of Occupational Therapy - November 21, 2022 Category: Occupational Health Authors: Shih-Chieh Lee Inga Wang Gong-Hong Lin Pei-Chi Li Ya-Chen Lee Chia-Yeh Chou Chien-Yu Huang Ching-Lin Hsieh Source Type: research

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