The challenges of using machine learning models in psychiatric research and clinical practice

TY - JOUR

T1 - The challenges of using machine learning models in psychiatric research and clinical practice

AU - Ostojic, Dijana

AU - Lalousis, Paris Alexandros

AU - Donohoe, Gary

AU - Morris, Derek W.

N1 - Publisher Copyright: © 2024

PY - 2024/11

Y1 - 2024/11

N2 - To understand the complex nature of heterogeneous psychiatric disorders, scientists and clinicians are required to employ a wide range of clinical, endophenotypic, neuroimaging, genomic, and environmental data to understand the biological mechanisms of psychiatric illness before this knowledge is applied into clinical setting. Machine learning (ML) is an automated process that can detect patterns from large multidimensional datasets and can supersede conventional statistical methods as it can detect both linear and non-linear relationships. Due to this advantage, ML has potential to enhance our understanding, improve diagnosis, prognosis and treatment of psychiatric disorders. The current review provides an in-depth examination of, and offers practical guidance for, the challenges encountered in the application of ML models in psychiatric research and clinical practice. These challenges include the curse of dimensionality, data quality, the ‘black box’ problem, hyperparameter tuning, external validation, class imbalance, and data representativeness. These challenges are particularly critical in the context of psychiatry as it is expected that researchers will encounter them during the stages of ML model development and deployment. We detail practical solutions and best practices to effectively mitigate the outlined challenges. These recommendations have the potential to improve reliability and interpretability of ML models in psychiatry.

AB - To understand the complex nature of heterogeneous psychiatric disorders, scientists and clinicians are required to employ a wide range of clinical, endophenotypic, neuroimaging, genomic, and environmental data to understand the biological mechanisms of psychiatric illness before this knowledge is applied into clinical setting. Machine learning (ML) is an automated process that can detect patterns from large multidimensional datasets and can supersede conventional statistical methods as it can detect both linear and non-linear relationships. Due to this advantage, ML has potential to enhance our understanding, improve diagnosis, prognosis and treatment of psychiatric disorders. The current review provides an in-depth examination of, and offers practical guidance for, the challenges encountered in the application of ML models in psychiatric research and clinical practice. These challenges include the curse of dimensionality, data quality, the ‘black box’ problem, hyperparameter tuning, external validation, class imbalance, and data representativeness. These challenges are particularly critical in the context of psychiatry as it is expected that researchers will encounter them during the stages of ML model development and deployment. We detail practical solutions and best practices to effectively mitigate the outlined challenges. These recommendations have the potential to improve reliability and interpretability of ML models in psychiatry.

KW - Challenges

KW - Machine learning

KW - Psychiatry

UR - http://www.scopus.com/inward/record.url?scp=85202971553&partnerID=8YFLogxK

U2 - 10.1016/j.euroneuro.2024.08.005

DO - 10.1016/j.euroneuro.2024.08.005

M3 - Review article

C2 - 39232341

AN - SCOPUS:85202971553

SN - 0924-977X

VL - 88

SP - 53

EP - 65

JO - European Neuropsychopharmacology

JF - European Neuropsychopharmacology

ER -