Advancing psychosocial disability and psychosocial rehabilitation research through large language models and computational text mining

Abstract

Psychosocial rehabilitation and psychosocial disability research have been a longstanding topic in healthcare, demanding continuous exploration and analysis to enhance patient and clinical outcomes. As the prevalence of psychosocial disability research continues to attract scholarly attention, many scientific articles are being published in the literature. These publications offer profound insights into diagnostics, preventative measures, treatment strategies, and epidemiological factors. Computational text mining as a subfield of artificial intelligence (AI) can make a big difference in accurately analyzing the current extensive collection of scientific articles on time, assisting individual scientists in understanding psychosocial disabilities better, and improving how we care for people with these challenges. Leveraging the vast repository of scientific literature available on PubMed, this study employs advanced text mining strategies, including word embeddings and large language models (LLMs) to extract valuable insights, automatically catalyzing research in mental health. It aims to significantly enhance the scientific community’s knowledge by creating an extensive textual dataset and advanced computational text mining strategies to explore current trends in psychosocial rehabilitation and psychosocial disability research.

Keywords

• psychosocial disability research
• psychosocial rehabilitation
• computational text mining
• large language models (LLMs)