Developing a machine learning-based short form of the positive and negative syndrome scale

Gong Hong Lin, Jen Hsuan Liu, Shih Chieh Lee, Bo Jian Wu, Shu Qi Li, Hsien Jane Chiu, San Ping Wang, Ching Lin Hsieh

Research output: Contribution to journalArticlepeer-review

Abstract

Background and hypothesis: The Positive and Negative Syndrome Scale (PANSS) consists of 30 items and takes up to 50 minutes to administer and score. Therefore, this study aimed to develop and validate a machine learning-based short form of the PANSS (PANSS-MLSF) that reproduces the PANSS scores. Moreover, the PANSS-MLSF estimated the removed-item scores. Study design: The PANSS-MLSF was developed using an artificial neural network, and the removed-item scores were estimated using the eXtreme Gradient Boosting classifier algorithm. The reliability of the PANSS-MLSF was examined using Cronbach's alpha. The concurrent validity was examined by the association (Pearson's r) between the PANSS-MLSF and the PANSS. The convergent validity was examined by the association (Pearson's r) between the PANSS-MLSF and the Clinical Global Impression-Severity, Mini-Mental State Examination, and Lawton Instrumental Activities of Daily Living Scale. The agreement of the estimated removed-item scores with their original scores was examined using Cohen's kappa. Study results: Our analysis included data from 573 patients with moderate severity. The two versions of the PANSS-MLSF comprised 15 items and 9 items were proposed. The PANSS-MLSF scores were similar to the PANSS scores (mean squared error=2.6–24.4 points). The reliability, concurrent validity, and convergent validity of the PANSS-MLSF were good. Moderate to good agreement between the estimated removed-item scores and the original item scores was found in 60% of the removed items. Conclusion: The PANSS-MLSF offers a viable way to reduce PANSS administration time, maintain score comparability, uphold reliability and validity, and even estimate scores for the removed items.

Original languageEnglish
Article number103965
JournalAsian Journal of Psychiatry
Volume94
DOIs
Publication statusPublished - Apr 2024

Keywords

  • artificial intelligence
  • machine learning
  • schizophrenia
  • short forms

ASJC Scopus subject areas

  • General Psychology
  • Psychiatry and Mental health

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