A Case Study of a Machine-Learning Approach in Differential Diagnosis of Schizophrenia: The Predictive Capacity of WAIS-IV / 신경정신의학
Journal of Korean Neuropsychiatric Association
;
: 103-110, 2017.
Article
in Korean
| WPRIM
| ID: wpr-178698
ABSTRACT
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.
Full text:
Available
Index:
WPRIM (Western Pacific)
Main subject:
Schizophrenia
/
Medical Records
/
Data Collection
/
Classification
/
Cognition Disorders
/
Nonlinear Dynamics
/
Diagnostic and Statistical Manual of Mental Disorders
/
Diagnosis
/
Diagnosis, Differential
/
Machine Learning
Type of study:
Diagnostic study
/
Prognostic study
Limits:
Adult
/
Humans
Language:
Korean
Journal:
Journal of Korean Neuropsychiatric Association
Year:
2017
Type:
Article
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