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• W4309662531 abstract "Abstract Importance: Several short forms of the Stroke Impact Scale Version 3.0 (SIS 3.0) have been proposed in order to decrease its administration time of about 20 min. However, none of the short-form scores are comparable to those of the original measure. Objective: To develop a short-form SIS 3.0 using a machine learning algorithm (ML–SIS). Design: We developed the ML–SIS in three stages. First, we calculated the frequencies of items having the highest contribution to predicting the original domain scores across 50 deep neural networks. Second, we iteratively selected the items showing the highest frequency until the coefficient of determination (R2) of each domain was ≥.90. Third, we examined the comparability and concurrent and convergent validity of the ML–SIS. Setting: Hospitals. Participants: We extracted complete data for 1,010 patients from an existing data set. Results: Twenty-eight items were selected for the ML–SIS. High average R2s (.90–.96) and small average residuals (mean absolute errors and root-mean-square errors = 0.49–2.84) indicate good comparability. High correlations (rs = .95–.98) between the eight domain scores of the ML–SIS and the SIS 3.0 indicate sufficient concurrent validity. Similar interdomain correlations between the two measures indicate satisfactory convergent validity. 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." @default.
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• W4309662531 date "2022-11-01" @default.
• W4309662531 modified "2023-09-26" @default.
• W4309662531 title "Development of a Short-Form Stroke Impact Scale Using a Machine Learning Algorithm for Patients at the Subacute Stage" @default.
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• W4309662531 doi "https://doi.org/10.5014/ajot.2022.049136" @default.
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