Summary of The Point Of View Of a Sentiment: Towards Clinician Bias Detection in Psychiatric Notes, by Alissa A. Valentine et al.
The Point of View of a Sentiment: Towards Clinician Bias Detection in Psychiatric Notes
by Alissa A. Valentine, Lauren A. Lepow, Lili Chan, Alexander W. Charney, Isotta Landi
First submitted to arxiv on: 31 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A recent study focuses on identifying potentially harmful language usage in psychiatric clinical notes by analyzing the sentiment expressed in sentences describing patients based on the reader’s point of view. The researchers used pre-trained and large language models (PLMs and LLMs) to fine-tune three PLMs (RoBERTa, GatorTron, and GatorTron + Task Adaptation) and implement zero-shot and few-shot ICL approaches for three LLMs (GPT-3.5, Llama-3.1, and Mistral). The goal is to classify the sentiment of sentences according to the physician or non-physician point of view. The study analyzed 39 sentences from the Mount Sinai Health System containing psychiatric lexicon. Results showed that GPT-3.5 aligned best to the physician’s point of view and Mistral aligned best to the non-physician’s point of view. This research highlights the importance of recognizing the reader’s point of view for improving note writing processes, reducing bias in computational systems, and promoting more accurate downstream analyses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A study looked at how language used in psychiatric notes can affect healthcare outcomes. They found that words can be hurtful if patients or doctors read them. The researchers wanted to see if they could use special computer models to figure out if the language was negative or positive, depending on who was reading it (a doctor or a patient). They took 39 sentences from Mount Sinai Hospital and used three types of computer models to analyze them. The results showed that one model worked best for doctors’ perspectives and another for patients’. This study is important because it shows how we need to think about who is reading the language, not just what’s being said. |
Keywords
» Artificial intelligence » Few shot » Gpt » Llama » Zero shot
