BACKGROUND
Although mental illness is a leading cause of death, social stigma makes people hesitant to receive timely medical treatment. Online diagnosis systems can increase the accessibility of mental health care services. However, explaining illness detection results has been challenging in these systems because existing online diagnosis systems rely mainly on users’ self-assessments based on structured questionnaires.
OBJECTIVE
We proposed a system that provides detailed explanations (i.e., symptom evidence and solutions) about detected mental illnesses by analyzing users’ free-text inputs.
METHODS
Our system consists of three modules: a detection module, symptom extraction module, and solution suggestion module. The detection module analyzes the input text that the user describes, and accurately classifies the mental disorders that the user is likely to have. The system then extracts and delivers symptom evidence and solutions to explain the detection results better. A within-subject study was conducted to examine the effectiveness of the proposed system. Qualitative and quantitative data were collected using 36 questions, including seven open-ended questions, from 30 participants.
RESULTS
Analysis of the quantitative results demonstrated that additional symptom and solution information, along with the classification results, helped increase the explainability and usability of the online mental illness detection system. A total of 83.33% of the users preferred the system, which provided various explanations, and the free-text entry method helped them think more about themselves than answering structured questions.
CONCLUSIONS
The implementation of our proposed system revealed that (i) the system achieved fairly accurate performance in mental illness detection, (ii) providing explanations could improve user experience, and (iii) most users were satisfied with the system, while a few showed some concerns.