Choosing the right treatment: combining clinicians’ expert knowledge with data-driven predictions

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 disorder for treatment. Results: By incorporating expert knowledge, the model demonstrated enhanced performance compared to a strictly data-driven approach. Specifically, it achieved an AUC score of 0.85 vs 0.80 on the test data. Furthermore, we evaluated some cases where the predictions of the model did not match the actual treatment. The symptom questionnaires indicated that these participants likely had comorbid disorders, with the actual treatment being proposed by the model with the second highest probability. Conclusions: In 90.1% of cases, the hybrid model ranked the actual disorder treated as either the highest (67.3%) or second-highest (22.8%) on the test data. This emphasizes that instead of suggesting a single disorder to be treated, the model can offer the probabilities for multiple disorders. This allows individuals seeking treatment or their therapists to incorporate this information as an additional data-driven factor when collectively deciding on which treatment to prioritize.