TIMI数据集相关性矩阵
用于评估ChatGPT-4在急性非创伤性胸痛风险分层中表现的模拟患者数据及与TIMI/HEART评分的相关性矩阵。
基本信息
资源简介
TIMI数据集相关性矩阵
【数据集背景】
本数据集源站标题为《TIMI dataset correlation matrix.》。源站首次发布：2024-04-16。
【数据内容】
源站原始描述：Background ChatGPT-4 is a large language model with promising healthcare applications. However, its ability to analyze complex clinical data and provide consistent results is poorly known. Compared to validated tools, this study evaluated ChatGPT-4’s risk stratification of simulated patients with acute nontraumatic chest pain. Methods Three datasets of simulated case studies were created: one based on the TIMI score variables, another on HEART score variables, and a third comprising 44 randomize…
【数据结构与技术规格】
文件数 1 个，合计 5.63 KB。Table 1.xls：application/vnd.ms-excel，5.63 KB。数据集 DOI：10.1371/journal.pone.0301854.t001。
【主题与分类】
源站学科分类：Biotechnology、Biological Sciences not elsewhere classified、Mathematical Sciences not elsewhere classified、Cancer、Infectious Diseases、Virology。主题标签：provide consistent results、identical patient data、simulated case studies、individual risk assessments、dataset five times、clinical risk assessment、4 models agreed、4 independently scored、4 hold promise、4 correlates closely。
【适用方向】
结构化表格数据可直接用于统计建模、特征工程与队列分析。
【获取与许可】
源站页面：https://plos.figshare.com/articles/dataset/TIMI_dataset_correlation_matrix_/25618661
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Heston, Thomas F.; Lewis, Lawrence M. (2024). TIMI dataset correlation matrix.. PLOS ONE. Dataset
下载信息
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使用方式
数据集获取（Figshare）
- 源站标题：TIMI dataset correlation matrix.
- DOI：10.1371/journal.pone.0301854.t001
- 发布日期：2024-04-16
- 文件数：1 个
- 文件总体积：5.63 KB
- 源站页面：https://plos.figshare.com/articles/dataset/TIMI_dataset_correlation_matrix_/25618661
命令行下载
curl -L -o "Table 1.xls" "https://ndownloader.figshare.com/files/45694700"
文件清单
Table 1.xls（5.63 KB）
文件总体积 5.63 KB（源站 API 实测）。
数据说明
Background
ChatGPT-4 is a large language model with promising healthcare applications. However, its ability to analyze complex clinical data and provide consistent results is poorly known. Compared to validated tools, this study evaluated ChatGPT-4’s risk stratification of simulated patients with acute nontraumatic chest pain.
Methods
Three datasets of simulated case studies were created: one based on the TIMI score variables, another on HEART score variables, and a third comprising 44 randomized variables related to non-traumatic chest pain presentations. ChatGPT-4 independently scored each dataset five times. Its risk scores were compared to calculated TIMI and HEART scores. A model trained on 44 clinical variables was evaluated for consistency.
Results
ChatGPT-4 showed a high correlation with TIMI and HEART scores (r = 0.898 and 0.928, respectively), but the distribution of individual risk assessments was broad. ChatGPT-4 gave a different risk 45–48% of the time for a fixed TIMI or HEART score. On the 44-variable model, a majority of the five ChatGPT-4 models agreed on a diagnosis category only 56% of the time, and risk scores were poorly correlated (r = 0.605).
Conclusion
While ChatGPT-4 correlates closely with established risk stratification tools regarding mean scores, its inconsistency when presented with identical patient data on separate occasions raises concerns about its reliability. The findings suggest that while large language models like ChatGPT-4 hold promise for healthcare applications, further refinement and customization are necessary, particularly in the clinical risk assessment of atraumatic chest pain patients.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0301854.t001（source: https://plos.figshare.com/articles/dataset/TIMI_dataset_correlation_matrix_/25618661）。
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