Choosing the right treatment - combining clinicians' expert knowledge with data-driven predictions.

ABSTRACT:

Context

This study proposes a Bayesian network model to aid mental health specialists making data-driven decisions on suitable treatments. The aim is to create a probabilistic machine learning model to assist psychologists in selecting the most suitable treatment for individuals for four potential mental disorders: Depression, Panic Disorder, Social Phobia, or Specific Phobia.

Methods

This study utilized a dataset from 1,094 individuals in Denmark containing socio-demographic details and mental health information. A Bayesian network was initially employed in a purely data-driven approach and was later refined with expert knowledge, referred to as a hybrid model. The model outputted probabilities for each disorder, with the highest probability indicating the most suitable dis

SUBMITTER: Maekawa E 

PROVIDER: S-EPMC11406075 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature