Assessing mental health disorders and determining treatment can be difficult for a number of reasons, including access to healthcare providers. Assessments and treatments may not be continuous and can be limited by the unpredictable nature of psychiatric symptoms. Machine-learning models using data collected in a clinical setting can improve diagnosis and treatment. Studies have used speech, text, and facial expression analysis to identify depression. Still, more research is needed to address challenges such as the need for multimodality machine-learning models for clinical use. We conducted a review of studies from the past decade that utilized speech, text, and facial expression analysis to detect depression, as defined by the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guideline. We provide information on the number of participants, techniques used to assess clinical outcomes, speech-eliciting tasks, machine-learning algorithms, metrics, and other important discoveries for each study. A total of 544 studies were examined, 264 of which satisfied the inclusion criteria. A database has been created containing the query results and a summary of how different features are used to detect depression. While machine learning shows its potential to enhance mental health disorder evaluations, some obstacles must be overcome, especially the requirement for more transparent machine-learning models for clinical purposes. Considering the variety of datasets, feature extraction techniques, and metrics used in this field, guidelines have been provided to collect data and train machine-learning models to guarantee reproducibility and generalizability across different contexts.

Depression is increasingly impacting individuals both physically and psychologically worldwide. It has become a global major public health problem and attracts attention from various research fields. Traditionally, the diagnosis of depression is formulated through semi-structured interviews and supplementary questionnaires, which makes the diagnosis heavily relies on physicians' experience and is subject to bias. However, since the pathogenic mechanism of depression is still under investigation, which is difficult for physicians to diagnose and treat, especially in the early clinical stage. In this article, we propose an attention-based multimodality speech and text representation for depression prediction. Our model is trained to estimate the depression severity of participants using the Distress Analysis Interview Corpus-Wizard of Oz (DAIC-WOZ) dataset. Results show that both audio and text models perform well on the depression severity estimation task, with best sequence level F1 score of 0.9870 and patient level F1 score of 0.9074 for the audio model, as well as sequence level F1 score of 0.9709 and patient level F1 score of 0.9245 for the text model. Results are similar for the multimodality fused model, with the highest F1 score of 0.9580 on the patient level depression detection task. Experiments show statistically significant improvements over previous works

Post-traumatic stress disorder (PTSD) is frequently underdiagnosed due to its clinical and biological heterogeneity. Worldwide, many people face barriers to accessing accurate and timely diagnoses. Machine learning (ML) techniques have been utilized for early assessments and outcome prediction to address these challenges. This paper aims to conduct a systematic review to investigate if ML is a promising approach for PTSD diagnosis. In this review, statistical methods were employed to synthesize the outcomes of the included research and provide guidance on critical considerations for ML task implementation. These included (a) selection of the most appropriate ML model for the available dataset, (b) identification of optimal ML features based on the chosen diagnostic method, (c) determination of appropriate sample size based on the distribution of the data, and (d) implementation of suitable validation tools to assess the performance of the selected ML models. We screened 3186 studies and included 41 articles based on eligibility criteria in the final synthesis. Here we report that the analysis of the included studies highlights the potential of artificial intelligence (AI) in PTSD diagnosis. However, implementing AI-based diagnostic systems in real clinical settings requires addressing several limitations, including appropriate regulation, ethical considerations, and protection of patient privacy.

The global prevalence of mental health disorders is increasing, leading to a significant economic burden estimated in trillions of dollars. In automated mental health diagnosis, the scarcity and imbalance of clinical data pose considerable challenges for researchers, limiting the effectiveness of machine learning algorithms. To cope with this issue, this paper aims to introduce a novel clinical transcript data augmentation framework by leveraging large language models (CALLM). The framework follows a “patient-doctor role-playing” intuition to generate realistic synthetic data. In addition, our study introduces a unique “Textbook-Assignment-Application” (T-A-A) partitioning approach to offer a systematic means of crafting synthetic clinical interview datasets. Concurrently, we have also developed a “Response-Reason” prompt engineering paradigm to generate highly authentic and diagnostically valuable transcripts. By leveraging a fine-tuned DistilBERT model on the E-DAIC PTSD dataset, we achieved a balanced accuracy of 0.77, an F1-score of 0.70, and an AUC of 0.78 during test set evaluations, which showcase robust adaptability in both Zero-Shot Learning (ZSL) and Few-Shot Learning (FSL) scenarios. We further compare the CALLM framework with other data augmentation methods and PTSD diagnostic works and demonstrates consistent improvements. Compared to conventional data collection methods, our synthetic dataset not only demonstrates superior performance but also incurs less than 1% of the associated costs.

Depression clinical interview corpora are essential for advancing automated depression diagnosis. While previous studies have used written speech material in controlled settings, these materials do not accurately represent spontaneous conversational speech. Additionally, self-reported measures of depression are subject to bias, making the data unreliable for training models for real-world scenarios. This study introduces a new corpus of depression clinical interviews collected directly from a psychiatric hospital, containing 113 recordings with 52 healthy and 61 depressive patients. The subjects were examined using the Montgomery-Asberg Depression Rating Scale (MADRS) in Chinese. Their final diagnosis was based on medical evaluations through a clinical interview conducted by a psychiatry specialist. All interviews were audio-recorded and transcribed verbatim, and annotated by experienced physicians. This dataset is a valuable resource for automated depression detection research and is expected to advance the field of psychology. Baseline models for detecting and predicting depression presence and level were built, and descriptive statistics of audio and text features were calculated. The decision-making process of the model was also investigated and illustrated. To the best of our knowledge, this is the first study to collect a depression clinical interview corpus in Chinese and train machine learning models to diagnose depression patients.