task_categories:text-classification language:en license:apache-2.0 size_categories:1K<n<10K format:csv modality:text library:datasets library:dask library:mlcroissant library:polars region:us alcohol emrs ehrs clinical liver hepatology medical records narrative gpt4 hospital healthcare admission cohort
---
license: apache-2.0
task_categories:
- text-classification
language:
- en
tags:
- alcohol
- emrs
- ehrs
- clinical
- liver
- hepatology
- medical
- records
- narrative
- gpt4
- hospital
- healthcare
- admission
- cohort
---
**Contributions:** The dataset was created by Dr. Uri Kartoun (https://urikartoun.com/).

**Use Case:** [Leveraging Large Language Models for Enhanced Clinical Narrative Analysis: An Application in Alcohol Use Detection](https://www.linkedin.com/pulse/leveraging-large-language-models-enhanced-clinical-uri-w6qye/?trackingId=06CMNcZa95lObWau2Ha%2FEg%3D%3D)

**Dataset Summary:** This dataset contains 1,500 samples of expressions indicating alcohol use or its negation, generated from clinical narrative notes using OpenAI's ChatGPT 4 model. It's designed to support NLP applications that require the identification of alcohol use references in healthcare records.

**Text Classification:** The dataset can be used to train and test models to classify expressions as indicating alcohol use or not.

**Dataset Structure:** Blob (the expression extracted from a simulated clinical note) and label (a binary label indicating presence or absence of alcohol use).

**Data Splits:** Training set (1,000 samples) and held-out set (500 samples).

**Dataset Curators:** The dataset was curated by using OpenAI's ChatGPT 4 model to generate expressions based on synthetic prompts.

**Potential Biases:** As the data was generated by a language model, it may inherit biases from the training data used for the model.

**Other Potential Limitations:** The accuracy of the dataset in reflecting real-world clinical narratives depends on the input prompts and the model's performance, which might not always capture the nuances of human-generated clinical notes.