Summary of Llm Questionnaire Completion For Automatic Psychiatric Assessment, by Gony Rosenman et al.
LLM Questionnaire Completion for Automatic Psychiatric Assessment
by Gony Rosenman, Lior Wolf, Talma Hendler
First submitted to arxiv on: 9 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers utilize a Large Language Model (LLM) to transform free-form psychological interviews into standardized questionnaires covering various psychiatric and personality areas. The LLM is trained to respond as if it were the interviewee, producing answers that are then coded as features used for predicting depression and PTSD symptoms using a Random Forest regressor. Compared to several baselines, this approach demonstrates improved diagnostic accuracy, offering a new framework for analyzing unstructured psychological interviews and bridging the gap between narrative-driven and data-driven mental health assessment approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses artificial intelligence to help doctors better understand people’s thoughts and feelings. Researchers trained a computer model to pretend it was the person being interviewed, so it could answer questions in a way that makes sense. They used this model to turn free-form interviews into standardized questionnaires that can be used to diagnose things like depression and PTSD. The results showed that this approach is more accurate than other ways of doing things, which could help doctors better understand people’s mental health. |
Keywords
» Artificial intelligence » Large language model » Random forest
