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Titre : | Development of a Short-Form Stroke Impact Scale Using a Machine Learning Algorithm for Patients at the Subacute Stage (2022) |
Auteurs : | Shih-Chieh Lee ; Inga Wang ; Gong-Hong Lin ; Pei-Chi Li ; Ya-Chen Lee ; Chia-Yeh Chou ; Chien-Yu Huang ; Ching-Lin Hsieh |
Type de document : | Article |
Dans : | American journal of occupational therapy (Vol. 76, n° 6, Novembre-Décembre 2022) |
Article en page(s) : | p. 1-8 |
Note générale : | 10.5014/ajot.2022.049136 |
Langues: | Anglais |
Descripteurs : |
HE Vinci Accident vasculaire cérébral (AVC) ; Algorithmes ; Apprentissage machine ; Complications ; Echelle d'évaluation |
Résumé : | 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 (MLSIS). Design: We developed the MLSIS 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 MLSIS. Setting: Hospitals. Participants: We extracted complete data for 1,010 patients from an existing data set. Results: Twenty-eight items were selected for the MLSIS. High average R2s (.90.96) and small average residuals (mean absolute errors and root-mean-square errors = 0.492.84) indicate good comparability. High correlations (rs =.95.98) between the eight domain scores of the MLSIS and the SIS 3.0 indicate sufficient concurrent validity. Similar interdomain correlations between the two measures indicate satisfactory convergent validity. Conclusions and Relevance: The MLSIS 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 MLSIS may be an efficient alternative to the SIS 3.0. What This Article Adds: The MLSIS, 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. This article details the development of a short-form Stroke Impact Scale Version 3.0 (SIS 3.0) using a machine learning algorithm (MLSIS) and concludes that the measure has good potential to be efficient and informative for clinical settings and provides scores that are valid and comparable to those of the original measure. |
Disponible en ligne : | Oui |
En ligne : | https://search-ebscohost-com.ezproxy.vinci.be/login.aspx?direct=true&db=ccm&AN=161715142&lang=fr&site=ehost-live |