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. Significant variables were selected through traditional statistical analysis based on retrospective data of 385 stroke patients; the data were collected from October 2012 to March 2014. The data were then applied to three boosting models (Xgboost, CatBoost, and LGBM) to identify the comparative and best performing models. 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 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. 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.

Computer-based neuropsychological assessments have many advantages over traditional neuropsychological assessments. However, limited data are available on the validity and reliability of computer-based assessments. The purpose of this study was to examine the reliability and validity of computer-based dementia assessment contents (Co-Wis). This study recruited 113 participants from Yeungnam University Medical Center in Daegu from June 2019 to December 2019 and received ethical approval. Participants were evaluated using standard and objective dementia cognitive test tools such as the Korean version of the Mini-Mental State Examination (K-MMSE), the Clinical Dementia Rating Scale (CDR), and the Standardized Seoul Neuropsychological Screening Battery-II (SNSB-II). To verify the effectiveness of Co-Wis, the concurrent validity, test–retest reliability (Pearson’s correlation coefficients), construct validity (Factor analysis), and signal detection analysis (ROC curve) were used. In most of the Co-Wis subtests, the concurrent validity and test–retest reliability showed statistically significant correlations (p < 0.05, p < 0.01). The factor analysis showed that Co-Wis assessed the most major cognitive areas (Tucker–Lewis Index (TLI) = 0.876, Comparative Fit Index (CFI) = 0.897, RMSEA = 0.88). Thus, Co-Wis appears clinically applicable and with high reliability and validity. In the future, we should develop tests to evaluate both standard data and big data-based machine learning.

본 연구는 전산화 인지재활치료의 필요성과 정신 건강 분야에서의 작업치료사의 참여와 역할에 대한 기초 자료를 제시하고 있으며, 한국보건의료연구원 및 PRISMA 지침을 기반으로 체계적 고찰과 메타 분석 방법을 사용하여 향후 통합된 정신건강 치료서비스를 제공하고 국가 정신 건강을 증진하는 것을 목적으로 한다. 체계적 고찰을 위한 문헌의 질 평가는 공동 저자를 포함하여 작업치료 석사 학위를 가진 10년 이상 임상 경험을 가진 2명의 작업치료사에 의해 Van Tulder 척도를 사용하여 평가되었으며 최종결과는 연구책임자 1명이 검토하였다. 메타분석은 Revman manager 5.3 프로그램을 이용하여 문헌 결과별로 데이터를 입력 및 분석하였으며 표와 그림을 통해 결과를 시각적으로 제시하였다. 본 연구 결과를 통해 전산화 인지재활치료를 적용하여 기존 인지재활치료 한계점 보완과 정신질환 환자 인지기능 개선 효과를 알 수 있었다. 따라서 본 연구의 의의는 정신건강의학 분야에서 전산화 인지재활치료의 효과를 증명하여 통합적인 의료서비스의 제공 및 작업치료사의 역할 확대에 대한 근거자료로써 의미가 있다.