A Case Study of a Machine-Learning Approach in Differential Diagnosis of Schizophrenia: The Predictive Capacity of WAIS-IV / 신경정신의학

OBJECTIVES: Machine learning (ML) encompasses a body of statistical approaches that can detect complex interaction patterns from multi-dimensional data. ML is gradually being adopted in medical science, for example, in treatment response prediction and diagnostic classification. Cognitive impairment is a prominent feature of schizophrenia, but is not routinely used in differential diagnosis. In this study, we investigated the predictive capacity of the Wechsler Adult Intelligence Scale IV (WAIS-IV) in differentiating schizophrenia from non-psychotic illnesses using the ML methodology. The purpose of this study was to illustrate the possibility of using ML as an aid in differential diagnosis. METHODS: The WAIS-IV test data for 434 psychiatric patients were curated from archived medical records. Using the final diagnoses based on DSM-IV as the target and the WAIS-IV scores as predictor variables, predictive diagnostic models were built using 1) linear 2) non-linear/non-parametric ML algorithms. The accuracy obtained was compared to that of the baseline model built without the WAIS-IV information. RESULTS: The performances of the various ML models were compared. The accuracy of the baseline model was 71.5%, but the best non-linear model showed an accuracy of 84.6%, which was significantly higher than that of non-informative random guessing (p=0.002). Overall, the models using the non-linear algorithms showed better accuracy than the linear ones. CONCLUSION: The high performance of the developed models demonstrated the predictive capacity of the WAIS-IV and justified the application of ML in psychiatric diagnosis. However, the practical application of ML models may need refinement and larger-scale data collection.

Efficacy of a Day-Center Treatment Program for Children with Developmental Disorders

OBJECTIVES: This study was conducted in order to evaluate the effectiveness of a day-center treatment program to promote development of children with pervasive development disorder (PDD) and pervasive development disorder/mental retardation (PDD/MR). METHODS: Twenty five children (14 in the PDD group and 11 in the PDD/MR group) participated in a day-center treatment program. They had been enrolled in the whole program for 2-3 years. Their performance was evaluated according to the Preschool Language Scale (PRES), Social Maturity Scale (SMS), and Korean version of the Childhood Autism Rating Scale (CARS). They were grouped by diagnosis at the beginning of the program and the treatment effect was compared. RESULTS: Children who participated in the day-center treatment program showed a significant increase in their PRES and SMS scores and a decrease in their CARS scores. CONCLUSIONS: A day-center treatment program is effective for development of children with PDD and PDD/MR.