人工智能在神经外科住院患者围手术期青霉素过敏标签去除中的潜力

Name: 人工智能在神经外科住院患者围手术期青霉素过敏标签去除中的潜力
Creator: Taylor & Francis
License: CC BY 4.0

该数据集包含神经外科住院患者的青霉素过敏标签及AI分类结果，用于评估AI在围手术期过敏标签去除中的潜力。

Taylor & Francis

DataCite Commons

2025-01-27 更新

表格Artificial intelligenceneurosurgerypenicillinallergydelabeling

Artificial intelligence and the potential for perioperative delabeling of penicillin allergies for neurosurgery inpatients

基本信息

模态

表格

大小

0.000297604 GB

许可

CC BY 4.0

创建/更新时间

2025-01-27

版本

资源简介

人工智能在神经外科住院患者围手术期青霉素过敏标签去除中的潜力

【一句话概览】

本数据集是 figshare 公开的结构化表格数据集，收录 4 个文件、合计 297.60 KB，主题涵盖 Artificial intelligence、neurosurgery、penicillin、allergy、delabeling，可用于统计建模、特征工程与队列分析，CC BY 4.0 许可开放获取。

【数据集背景】

本数据集源站标题为《Artificial intelligence and the potential for perioperative delabeling of penicillin allergies for neurosurgery inpatients》。配套论文《Artificial intelligence and the potential for perioperative delabeling of penicillin allergies for neurosurgery inpatients》发表于 British Journal of Neurosurgery（2023），DOI：10.1080/02688697.2023.2173724。源站首次发布：2023-02-16。

【数据内容】

源站原始描述：Patients with penicillin allergy labels are more likely to have postoperative wound infections. When penicillin allergy labels are interrogated, a significant number of individuals do not have penicillin allergies and may be delabeled. This study was conducted to gain preliminary evidence into the potential role of artificial intelligence in assisting with perioperative penicillin adverse reaction (AR) evaluation. A single-centre retrospective cohort study of consecutive emergency and elective n…

中文译文（机器翻译，仅供快速理解，请以原文为准）：带有青霉素过敏标签的患者更可能发生术后伤口感染。当对青霉素过敏标签进行核查时，相当数量的个体实际上并无青霉素过敏，可能可以去除标签。本研究旨在获取初步证据，探讨人工智能在辅助围手术期青霉素不良反应（AR）评估中的潜在作用。对连续两年内收治的急诊和择期神经外科入院患者进行了一项单中心回顾性队列研究。将既往构建的用于青霉素AR分类的人工智能算法应用于这些数据。研究共纳入2063例次入院。带有青霉素过敏标签的个体数为124；1例患者带有青霉素不耐受标签。在这些标签中，22.4%与使用专家标准进行的分类不一致。当将该人工智能算法应用于该队列时，算法保持了较高的分类性能（过敏与不耐受分类的分类准确率为98.1%）。青霉素过敏标签在神经外科住院患者中很常见。人工智能可以在该队列中准确分类青霉素AR，并可能有助于识别适合去除标签的患者。

【数据结构与技术规格】

文件数 4 个，合计 297.60 KB。ibjn_a_2173724_sm0004.docx：application/vnd.openxmlformats-officedocument.wordprocessingml.document，14.81 KB。ibjn_a_2173724_sm9999.docx：application/vnd.openxmlformats-officedocument.wordprocessingml.document，14.71 KB。ibjn_a_2173724_sm9991.docx：application/vnd.openxmlformats-officedocument.wordprocessingml.document，51.00 KB。数据集 DOI：10.6084/m9.figshare.22109592.v1。

【主题与分类】

源站学科分类：Biochemistry、Microbiology、Cell Biology、Neuroscience、Pharmacology、Biotechnology。主题标签：Artificial intelligence、neurosurgery、penicillin、allergy、delabeling。

【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://tandf.figshare.com/articles/dataset/Artificial_intelligence_and_the_potential_for_perioperative_delabeling_of_penicillin_allergies_for_neurosurgery_inpatients/22109592

使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。

规范引用：Artificial intelligence and the potential for perioperative delabeling of penicillin allergies for neurosurgery inpatients. Taylor & Francis. Jiang, Melinda; Lam, Antoinette; Lam, Lydia; Kovoor, Joshua; Inglis, Joshua; Shakib, Sepehr; et al

【使用注意】

· 源站最后修改于 2025-01-27，引用时请注意版本时效。

原始链接

https://tandf.figshare.com/articles/dataset/Artificial_intelligence_and_the_potential_for_perioperative_delabeling_of_penicillin_allergies_for_neurosurgery_inpatients/22109592/1

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数据集获取（Figshare）

源站标题：Artificial intelligence and the potential for perioperative delabeling of penicillin allergies for neurosurgery inpatients
DOI：10.6084/m9.figshare.22109592.v1
发布日期：2023-02-16
文件数：4 个
文件总体积：297.60 KB
源站页面：https://tandf.figshare.com/articles/dataset/Artificial_intelligence_and_the_potential_for_perioperative_delabeling_of_penicillin_allergies_for_neurosurgery_inpatients/22109592

命令行下载

curl -L -o "ibjn_a_2173724_sm0004.docx" "https://ndownloader.figshare.com/files/39284211"
curl -L -o "ibjn_a_2173724_sm9999.docx" "https://ndownloader.figshare.com/files/39284214"
curl -L -o "ibjn_a_2173724_sm9991.docx" "https://ndownloader.figshare.com/files/39284217"
curl -L -o "ibjn_a_2173724_sm9990.docx" "https://ndownloader.figshare.com/files/39284220"

文件清单

ibjn_a_2173724_sm0004.docx（14.81 KB）
ibjn_a_2173724_sm9999.docx（14.71 KB）
ibjn_a_2173724_sm9991.docx（51.00 KB）
ibjn_a_2173724_sm9990.docx（217.09 KB）

文件总体积 297.60 KB（源站 API 实测）。

数据说明

Patients with penicillin allergy labels are more likely to have postoperative wound infections. When penicillin allergy labels are interrogated, a significant number of individuals do not have penicillin allergies and may be delabeled. This study was conducted to gain preliminary evidence into the potential role of artificial intelligence in assisting with perioperative penicillin adverse reaction (AR) evaluation.
A single-centre retrospective cohort study of consecutive emergency and elective neurosurgery admissions was conducted over a two-year period. Previously derived artificial intelligence algorithms for the classification of penicillin AR were applied to the data.
There were 2063 individual admissions included in the study. The number of individuals with penicillin allergy labels was 124; one patient had a penicillin intolerance label. Of these labels, 22.4% were not consistent with classifications using expert criteria. When the artificial intelligence algorithm was applied to the cohort, the algorithm maintained a high level of classification performance (classification accuracy 98.1% for allergy versus intolerance classification).
Penicillin allergy labels are common among neurosurgery inpatients. Artificial intelligence can accurately classify penicillin AR in this cohort, and may assist in identifying patients suitable for delabeling.