The shortage of clinical workforce presents significant challenges in mental healthcare, limiting access to formal diagnostics and services. We aim to tackle this shortage by integrating a customized large language model (LLM) into the workflow, thus promoting equity in mental healthcare for the general population. Although LLMs have showcased their capability in clinical decision-making, their adaptation to severe conditions like Post-traumatic Stress Disorder (PTSD) remains largely unexplored. Therefore, we collect 411 clinician-administered diagnostic interviews and devise a novel approach to obtain high-quality data. Moreover, we build a comprehensive framework to automate PTSD diagnostic assessments based on interview contents by leveraging two state-of-the-art LLMs, GPT-4 and Llama-2, with potential for broader clinical diagnoses. Our results illustrate strong promise for LLMs, tested on our dataset, to aid clinicians in diagnostic validation. To the best of our knowledge, this is the first AI system that fully automates assessments for mental illness based on clinician-administered interviews.

Social media has become a popular platform where people share information about personal healthcare conditions, diagnostic histories, and medical plans. Analyzing posts on social media depicting such realistic information can help improve quality and clinical decision-making; however, the lack of structured resources in this genre limits us to build robust NLP models for meaningful analysis. This paper presents a new corpus annotating relations among many types of conditions, treatments, and their attributes illustrated in social media posts by patients and caregivers. For experiments, a transformer encoder is pretrained on 1M raw posts and used to train several document-level relation extraction models using our corpus. Our best-performing model achieves the F1 scores of 70.9 and 51.7 for Entity Recognition and Relation Extraction, respectively. These results are encouraging as it is the first neural model extracting complex relations of this kind on social media data.

Entity recognition provides semantic access to ancient materials in the Digital Humanities: it exposes people and places of interest in texts that cannot be read exhaustively, facilitates linking resources and can provide a window into text contents, even for texts with no translations. In this paper we present entity recognition for Coptic, the language of Hellenistic era Egypt. We evaluate NLP approaches to the task and lay out difficulties in applying them to a low-resource, morphologically complex language. We present solutions for named and non-named nested entity recognition and semi-automatic entity linking to Wikipedia, relying on robust dependency parsing, feature-based CRF models, and hand-crafted knowledge base resources, enabling high accuracy NER with orders of magnitude less data than those used for high resource languages. The results suggest avenues for research on other languages in similar settings.