Development and validation of a machine learning‐based stroke suicidal ideation prediction model : A retrospective study
Year: 2022
Type: preprint
Study design: Observational Study
Abstract: Background: Currently, the identification of stroke patients with an increased suicide risk is mainly based on self‐report questionnaires, and this method suffers from a lack of objectivity. This study developed and validated a suicide ideation (SI) prediction model using clinical data and identified SI predictors.Method: Significant variables were selected through traditional statistical analysis based on retrospective data of 385 stroke patients; the data was collected from October 2012 to March 2014. The data was then applied to three boosting models (Xgboost, CatBoost, LGBM) to identify the comparative and best performing models.Results: Demographic variables that showed significant differences between the two groups were age, onset, type, socioeconomic, and education level. Additionally, functional variables also showed a significant difference with regard to ADL and emotion (p<0.05). The CatBoost model (0.900) showed higher performance than the other two models; and depression, anxiety, self-efficacy, and rehabilitation motivation (RM) were found to have high importance. Negative emotions such as depression and anxiety showed a positive relationship with SI and rehabilitation motivation and self-efficacy displayed an inverse relationship with SI.Conclusion: Machine learning-based SI models could augment SI prevention by helping rehabilitation and medical professionals identify high-risk stroke patients in need of SI prevention intervention.
Source: Research Square
Language: English
Subfield: Clinical Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
Award: G209226129
OpenAlex ID: W4280554162