Predicting Patients’ Satisfaction With Mental Health Drug Treatment Using Their Reviews: Unified Interchangeable Model Fusion Approach.

Publication date: Dec 05, 2023

After the COVID-19 pandemic, the conflict between limited mental health care resources and the rapidly growing number of patients has become more pronounced. It is necessary for psychologists to borrow artificial intelligence (AI)-based methods to analyze patients’ satisfaction with drug treatment for those undergoing mental illness treatment. Our goal was to construct highly accurate and transferable models for predicting the satisfaction of patients with mental illness with medication by analyzing their own experiences and comments related to medication intake. We extracted 41,851 reviews in 20 categories of disorders related to mental illnesses from a large public data set of 161,297 reviews in 16,950 illness categories. To discover a more optimal structure of the natural language processing models, we proposed the Unified Interchangeable Model Fusion to decompose the state-of-the-art Bidirectional Encoder Representations from Transformers (BERT), support vector machine, and random forest (RF) models into 2 modules, the encoder and the classifier, and then reconstruct fused “encoder+classifer” models to accurately evaluate patients’ satisfaction. The fused models were divided into 2 categories in terms of model structures, traditional machine learning-based models and neural network-based models. A new loss function was proposed for those neural network-based models to overcome overfitting and data imbalance. Finally, we fine-tuned the fused models and evaluated their performance comprehensively in terms of F-score, accuracy, _705 coefficient, and training time using 10-fold cross-validation. Through extensive experiments, the transformer bidirectional encoder+RF model outperformed the state-of-the-art BERT, MentalBERT, and other fused models. It became the optimal model for predicting the patients’ satisfaction with drug treatment. It achieved an average graded F-score of 0. 872, an accuracy of 0. 873, and a _705 coefficient of 0. 806. This model is suitable for high-standard users with sufficient computing resources. Alternatively, it turned out that the word-embedding encoder+RF model showed relatively good performance with an average graded F-score of 0. 801, an accuracy of 0. 812, and a _705 coefficient of 0. 695 but with much less training time. It can be deployed in environments with limited computing resources. We analyzed the performance of support vector machine, RF, BERT, MentalBERT, and all fused models and identified the optimal models for different clinical scenarios. The findings can serve as evidence to support that the natural language processing methods can effectively assist psychologists in evaluating the satisfaction of patients with drug treatment programs and provide precise and standardized solutions. The Unified Interchangeable Model Fusion provides a different perspective on building AI models in mental health and has the potential to fuse the strengths of different components of the models into a single model, which may contribute to the development of AI in mental health.

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Concepts Keywords
Health AI
Psychologists artificial intelligence
Rapidly data imbalance
Transformers deep learning
machine learning
mental disorder
model fusion
natural language processing
psychotherapy effectiveness


Type Source Name
disease MESH COVID-19 pandemic
disease MESH mental illness
disease VO data set
disease VO time
drug DRUGBANK Trestolone
disease VO effectiveness
disease VO organization
disease MESH lifestyles
disease MESH depressive disorders
disease VO efficient
disease IDO process
disease VO inefficient
disease VO effective
disease VO efficiency
disease VO frequency
disease VO document
disease MESH suicide
disease MESH attention deficit hyperactivity disorder
drug DRUGBANK Fish oil
drug DRUGBANK Venlafaxine
disease VO stomach
drug DRUGBANK Tropicamide
disease MESH sweating
disease VO mouth
disease VO dose
disease MESH insomnia
disease MESH bipolar disorder
disease MESH panic disorder
disease MESH psychosis
disease MESH schizophrenia
disease MESH major depressive disorder
disease MESH obsessive compulsive disorder
disease MESH anxiety disorder
disease MESH autism spectrum disorder
disease MESH paranoid disorder
disease MESH social anxiety disorder
disease MESH postpartum depression
disease MESH dissociative identity disorder
disease MESH intermittent explosive disorder
disease IDO algorithm
drug DRUGBANK Alpha-1-proteinase inhibitor
disease MESH panic
disease IDO production
drug DRUGBANK Flunarizine
drug DRUGBANK Resiniferatoxin
drug DRUGBANK Isoxaflutole
disease IDO intervention
disease VO storage
disease VO protocol
disease VO Gap
disease VO Apa
drug DRUGBANK Guanosine
disease VO volume
drug DRUGBANK Coenzyme M
drug DRUGBANK Gold
drug DRUGBANK Huperzine B
disease IDO object

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