Abstract
For many years, mental health has been hidden behind a veil of shame and prejudice. In 2017, studies claimed that 10.7% of the global population suffered from mental health disorders. Recently, people started seeking relaxing treatment through technology, which enhanced and expanded mental health care, especially during the COVID-19 pandemic, where the use of mental health forums, websites, and applications has increased by 95%. However, these solutions still have many limits, as existing mental health technologies are not meant for everyone. In this work, an up-to-date literature review on state-of-the-art of mental health and healthcare solutions is provided. Then, we focus on Arab-speaking patients and propose an intelligent tool for mental health intent recognition. The proposed system uses the concepts of intent recognition to make mental health diagnoses based on a bidirectional encoder representations from transformers (BERT) model and the International Neuropsychiatric Interview (MINI). Experiments are conducted using a dataset collected at the Military Hospital of Tunis in Tunisia. Results show excellent performance of the proposed system (the accuracy is over 92%, the precision, recall, and F1 scores are over 94%) in mental health patient diagnosis for five aspects (depression, suicidality, panic disorder, social phobia, and adjustment disorder). In addition, the tool was tested and evaluated by medical staff at the Military Hospital of Tunis, who found it very interesting to help decision-making and prioritizing patient appointment scheduling, especially with a high number of treated patients every day.
Highlights
This section introduces the mental health issues and presents model and the International Neuropsychiatric Interview (MINI) and general concepts about artificial intelligence (AI) in mental health diagnosis and natural language processing (NLP) and bidirectional encoder representations from transformers (BERT) models.2.1
We propose a mental health diagnosis application for Arabic-speaking patients using both MINI and the supervised machine learning BERT model to equip the psychiatry department of the Military Hospital of Instruction of Tunis with a rapid and intelligent tool handling the high number of patients treated every day
Once the input is successful, the BERT model will output an embedding vector of 768 in each of the tokens. These vectors can be used as an input for different NLP applications, such as the classification where we focus our attention on the embedding vector output from the special CLS token
Summary
This section introduces the mental health issues and presents MINI and general concepts about AI in mental health diagnosis and NLP and BERT models.2.1. Every person feels disturbed, anxious, or even downhearted, leading to significant mental health issues. According to the ICD-11 [7] as a reference for mental health illnesses, the identification of a mental health illness is often related to mental problems disturbing thinking ability, relation with others, and day-to-day behaviors. Most of these studies focused mainly on schizophrenia, stress disorder, depression, bipolar disorder, obsessive-compulsive disorder, and others [8]. Mental issues are global problems and equal opportunity issues. They affect the young and the old, male or female, every race, ethnic group, and different education and income levels
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