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Advancing mental health diagnostics: AI-based method for depression detection in patient interviews
(2023)
In this paper, we present a novel artificial intelligence (AI) application for depression detection, using advanced transformer networks to analyse clinical interviews. By incorporating simulated data to enhance traditional datasets, we overcome limitations in data protection and privacy, consequently improving the model’s performance. Our methodology employs BERT-based models, GPT-3.5, and ChatGPT-4, demonstrating state-of-the-art results in detecting depression from linguistic patterns and contextual information that significantly outperform previous approaches. Utilising the DAIC-WOZ and Extended-DAIC datasets, our study showcases the potential of the proposed application in revolutionising mental health care through early depression detection and intervention. Empirical results from various experiments highlight the efficacy of our approach and its suitability for real-world implementation. Furthermore, we acknowledge the ethical, legal, and social implications of AI in mental health diagnostics. Ultimately, our study underscores the transformative potential of AI in mental health diagnostics, paving the way for innovative solutions that can facilitate early intervention and improve patient outcomes.
For collision and obstacle avoidance as well as trajectory planning, robots usually generate and use a simple 2D costmap without any semantic information about the detected obstacles. Thus a robot’s path planning will simply adhere to an arbitrarily large safety margin around obstacles. A more optimal approach is to adjust this safety margin according to the class of an obstacle. For class prediction, an image processing convolutional neural network can be trained. One of the problems in the development and training of any neural network is the creation of a training dataset. The first part of this work describes methods and free open source software, allowing a fast generation of annotated datasets. Our pipeline can be applied to various objects and environment settings and is extremely easy to use to anyone for synthesising training data from 3D source data. We create a fully synthetic industrial environment dataset with 10 k physically-based rendered images and annotations. Our dataset and sources are publicly available at https://github.com/LJMP/synthetic-industrial-dataset. Subsequently, we train a convolutional neural network with our dataset for costmap safety class prediction. We analyse different class combinations and show that learning the safety classes end-to-end directly with a small dataset, instead of using a class lookup table, improves the quantity and precision of the predictions.
While the potential of Artificial Intelligence (AI) - particularly Natural Language Processing (NLP) models - for detecting symptoms of depression from text has been vastly researched, only a few studies examine such potential for the detection of social anxiety symptoms. We investigated the ability of the large language model (LLM) GPT-4 to correctly infer social anxiety symptom strength from transcripts obtained from semi-structured interviews. N = 51 adult participants were recruited from a convenience sample of the German population. Participants filled in a self-report questionnaire on social anxiety symptoms (SPIN) prior to being interviewed on a secure online teleconference platform. Transcripts from these interviews were then evaluated by GPT-4. GPT-4 predictions were highly correlated (r = 0.79) with scores obtained on the social anxiety self-report measure. Following the cut-off conventions for this population, an F1 accuracy score of 0.84 could be obtained. Future research should examine whether these findings hold true in larger and more diverse datasets.
Jüngste Fortschritte in der Künstlichen Intelligenz (KI) und der Erweiterten Realität (englisch „extended reality“ [XR]) bieten Potenziale, die Diagnostik und Behandlung in der Psychotherapie zu verbessern. KI-gesteuerte Technologien ermöglichen die präzise Analyse großer Datensätze zur Erkennung von Mustern und zur genauen Vorhersage und bietet z. B. im Kontext der Diagnose von Depressionen vielversprechende Einsatzmöglichkeiten. Extended-Reality-(XR)-Technologien wie Virtual Reality (VR) und Augmented Reality (AR) bieten immersive und interaktive Umgebungen, die sowohl in therapeutischen Interventionen als auch in der Diagnostik genutzt werden können. Dieser Überblick hebt das Potenzial von KI und XR in der klinischen Psychologie hervor und beschreibt ihre Vorteile, darunter eine erhöhte Diagnosegenauigkeit und Standardisierung, frühzeitige Erkennung und verbesserte Effizienz. Es werden auch die Einschränkungen und Herausforderungen ihres Einsatzes in der klinisch-psychologischen Praxis behandelt. Darüber hinaus werden ethische Überlegungen und regulatorische Rahmenbedingungen diskutiert, wobei der Fokus auf den neuesten EU-Vorschriften zur KI und deren Auswirkungen auf die klinische Praxis liegt. Zukünftige Trends und Entwicklungen werden ebenfalls beleuchtet.
One of the most underdiagnosed medical conditions worldwide is depression. It has been demonstrated that the current classical procedures for early detection of depression are insufficient, which emphasizes the importance of seeking a more efficient approach to overcome this challenge. One of the most promising opportunities is arising in the field of Artificial Intelligence as AI-based models could have the capacity to offer a fast, widely accessible, unbiased and efficient method to address this problem. In this paper, we compared three natural language processing models, namely, BERT, GPT-3.5 and GPT-4 on three different datasets. Our findings show that different levels of efficacy are shown by fine-tuned BERT, GPT-3.5, and GPT-4 in identifying depression from textual data. By comparing the models on the metrics such as accuracy, precision, and recall, our results have shown that GPT-4 outperforms both BERT and GPT-3.5 models, even without previous fine-tuning, showcasing its enormous potential to be utilized for automated depression detection on textual data. In the paper, we present newly introduced datasets, fine-tuning and model testing processes, while also addressing limitations and discussing further considerations for future research.
Depression is a significant global health challenge. Still, many people suffering from depression remain undiagnosed. Furthermore, the assessment of depression can be subject to human bias. Natural Language Processing (NLP) models offer a promising solution. We investigated the potential of four NLP models (BERT, Llama2-13B, GPT-3.5, and GPT-4) for depression detection in clinical interviews. Participants (N = 82) underwent clinical interviews and completed a self-report depression questionnaire. NLP models inferred depression scores from interview transcripts. Questionnaire cut-off values for depression were used as a classifier for depression. GPT-4 showed the highest accuracy for depression classification (F1 score 0.73), while zero-shot GPT-3.5 initially performed with low accuracy (0.34), improved to 0.82 after fine-tuning, and achieved 0.68 with clustered data. GPT-4 estimates of symptom severity PHQ-8 score correlated strongly (r = 0.71) with true symptom severity. These findings demonstrate the potential of AI models for depression detection. However, further research is necessary before widespread deployment can be considered.
