Leveraging Large Language Models for Automated Summarization of Mental Health Assessments

Core Concepts

Developing an automated system to generate concise summaries from mental state examinations can help alleviate the burden on mental health professionals, especially in regions with limited access to such services.

Abstract

The paper explores the use of large language models (LLMs) for generating concise summaries from mental state examinations (MSEs). The authors first developed a 12-item MSE questionnaire and collected data from 300 participants. They then evaluated the performance of four pre-trained LLMs (BART-base, BART-large-CNN, T5-large, and BART-large-xsum-samsum) with and without fine-tuning on the collected dataset.

The results show that fine-tuning the LLMs, even with limited training data, significantly improves the quality of the generated summaries. The best-performing fine-tuned model, BART-large-CNN, achieved ROUGE-1 and ROUGE-L scores of 0.810 and 0.764, respectively, outperforming the pre-trained models and existing work on medical dialogue summarization.

The authors also assessed the generalizability of the BART-large-CNN model by evaluating it on a publicly available dataset, with promising results. The study highlights the potential of leveraging LLMs to develop scalable, automated systems for conducting initial mental health assessments and generating summaries, which could help alleviate the burden on mental health professionals, especially in regions with limited access to such services.

Stats

The average length of the dialogue conversation with and without the questionnaire is 3662 and 2054 characters, respectively.

Quotes

"Improving mental health support in developing countries is a pressing need. One potential solution is the development of scalable, automated systems to conduct diagnostic screenings, which could help alleviate the burden on mental health professionals."
"Developing an automated system for initial assessment and summary generation would be pivotal in simulating an AI-driven junior doctor. The system would conduct MSEs and generate concise summaries of the MSE for the attending senior doctor."

Key Insights Distilled From

Fine-tuning Large Language Models for Automated Diagnostic Screening Summaries

by Manjeet Yada... at arxiv.org 04-01-2024

https://arxiv.org/pdf/2403.20145.pdf

Deeper Inquiries

How can the proposed automated system be further improved to better capture the nuances of mental health assessments, such as non-verbal cues and contextual information?

To enhance the automated system's ability to capture the nuances of mental health assessments, incorporating technologies like sentiment analysis and emotion recognition can be beneficial. These technologies can help analyze non-verbal cues such as tone of voice, facial expressions, and body language, providing valuable insights into the patient's emotional state. Additionally, integrating natural language processing techniques to understand contextual information and subtle nuances in language can further improve the system's accuracy in interpreting mental health assessments. By combining these advanced technologies, the system can better understand the complexities of human communication and provide more comprehensive assessments.

What are the potential ethical and privacy concerns associated with the use of such automated systems in mental health assessments, and how can they be addressed?

The use of automated systems in mental health assessments raises several ethical and privacy concerns. One major concern is the confidentiality and security of sensitive patient data. Ensuring data encryption, secure storage practices, and strict access controls can help mitigate privacy risks. Another concern is the potential for algorithmic bias, where the automated system may inadvertently discriminate against certain demographics or perpetuate existing biases in mental health diagnosis. Regular audits, transparency in algorithm design, and diverse training data can help address bias issues. Additionally, obtaining informed consent from patients, providing clear information on data usage, and offering opt-out options can uphold ethical standards in using automated systems for mental health assessments.

How can the insights from this work on automated summarization of mental health assessments be applied to other domains, such as physical health or education, to improve access to professional services?

The insights gained from automated summarization of mental health assessments can be applied to other domains to enhance access to professional services. In the domain of physical health, similar automated systems can be developed to summarize patient-doctor conversations, medical reports, and treatment plans, facilitating quicker decision-making and improving patient care. In the field of education, automated summarization can be utilized to generate concise summaries of student assessments, feedback from teachers, and educational resources, aiding in personalized learning and academic support. By leveraging the advancements in automated summarization techniques, professionals in various domains can streamline information processing, enhance communication, and ultimately improve the delivery of services to individuals in need.