Summary of Generalization in Healthcare Ai: Evaluation Of a Clinical Large Language Model, by Salman Rahman et al.
Generalization in Healthcare AI: Evaluation of a Clinical Large Language Model
by Salman Rahman, Lavender Yao Jiang, Saadia Gabriel, Yindalon Aphinyanaphongs, Eric Karl Oermann, Rumi Chunara
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Computers and Society (cs.CY); 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 This paper explores the potential of large language models (LLMs) in healthcare by analyzing the performance of ClinicLLM, an LLM trained on clinical notes from a hospital. The study focuses on 30-day all-cause readmission prediction and evaluates the model’s ability to generalize across different hospitals and patient populations. Results show that the model performs poorly in hospitals with limited samples, among patients with certain characteristics such as government insurance, older age, or high comorbidity levels. To understand these limitations, the authors investigate various factors including sample size, note content, patient demographics, and health system aspects. They find that these factors are important for generalization and propose local fine-tuning as a strategy to improve performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how big language models can be used in healthcare. It looks at a specific model called ClinicLLM and tries to predict when patients will be readmitted to the hospital within 30 days. The study found that this model didn’t work well in hospitals with small sample sizes, or for certain groups of people like older adults or those with multiple health problems. To figure out why, researchers looked at things like how much data was used to train the model, what kind of information was included in the notes, and other details about patients and hospitals. They found that these factors are important for making predictions work well across different groups of people. |
Keywords
* Artificial intelligence * Fine tuning * Generalization
