Advantages of Machine Learning in Forensic Psychiatric Research—Uncovering the Complexities of Aggressive Behavior in Schizophrenia

Steffen Lau,Lena A Hofmann,Johannes Kirchebner

doi:10.3390/app12020819

Steffen Lau, Lena A Hofmann + Show 1 more

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https://doi.org/10.3390/app12020819

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Abstract

Linear statistical methods may not be suited to the understanding of psychiatric phenomena such as aggression due to their complexity and multifactorial origins. Here, the application of machine learning (ML) algorithms offers the possibility of analyzing a large number of influencing factors and their interactions. This study aimed to explore inpatient aggression in offender patients with schizophrenia spectrum disorders (SSDs) using a suitable ML model on a dataset of 370 patients. With a balanced accuracy of 77.6% and an AUC of 0.87, support vector machines (SVM) outperformed all the other ML algorithms. Negative behavior toward other patients, the breaking of ward rules, the PANSS score at admission as well as poor impulse control and impulsivity emerged as the most predictive variables in distinguishing aggressive from non-aggressive patients. The present study serves as an example of the practical use of ML in forensic psychiatric research regarding the complex interplay between the factors contributing to aggressive behavior in SSD. Through its application, it could be shown that mental illness and the antisocial behavior associated with it outweighed other predictors. The fact that SSD is also highly associated with antisocial behavior emphasizes the importance of early detection and sufficient treatment.

Highlights

Academic Editors: Kyungtae Kang, With the rapid technological progress of the past few years, artificial intelligence (AI) is increasingly being put to use in medical research
This results in the following certain limitations: (I) mainly linear relationships can be determined, and, with null hypothesis significance tests (NHSTs), it is not even possible to examine the relationships between the variables themselves; (II) in order to avoid alpha error accumulation, only a limited number of variables can be analyzed; and (III) the research question must be precisely defined and rather constrained, as it can only be determined whether a hypothesis is true or not
Our findings expand the current research on factors influencing aggression within forensic inpatient treatment in offender patients with spectrum disorders (SSDs)

Summary

Introduction

Academic Editors: Kyungtae Kang, With the rapid technological progress of the past few years, artificial intelligence (AI) is increasingly being put to use in medical research. Often equated with human-like robots by the general public, AI is any system that adapts its performance based on its perception of the environment This includes advanced statistics such as machine learning (ML), which allows a variety of variables and their relationship to one another to be analyzed through complex mathematical algorithms, as well as the quantification of the quality of a statistical model [1,2,3,4]. The genesis of psychiatric diseases and pathological behavioral disorders is by no means a linear process influenced by only single, independent factors This is especially true for the generally under-researched field of forensic psychiatry, where the interplay of psychopathology, offending, and aggression has yet to be comprehensively understood.

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