为患者和家属定制复杂临床信息的人工智能：技术视角

数据集获取（Figshare）

• 源站标题：Artificial intelligence for tailoring complex clinical information to patients and families: A technical perspective
• DOI：10.6084/m9.figshare.31915973.v1
• 发布日期：2026-04-01
• 文件数：2 个
• 文件总体积：45.58 KB
• 源站页面：https://tandf.figshare.com/articles/dataset/Artificial_intelligence_for_tailoring_complex_clinical_information_to_patients_and_families_A_technical_perspective/31915973

命令行下载

curl -L -o "yscm_a_2639819_sm5451.docx" "https://ndownloader.figshare.com/files/63377459"
curl -L -o "yscm_a_2639819_sm5450.docx" "https://ndownloader.figshare.com/files/63377462"

文件清单

• yscm_a_2639819_sm5451.docx（17.02 KB）
• yscm_a_2639819_sm5450.docx（28.56 KB）

文件总体积 45.58 KB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

Effective communication of complex medical information is critical for individuals with spinal cord injury (SCI) and their families, but this need remains largely unmet. Artificial intelligence (AI), including large language models (LLMs) like GPT-4o, may help simplify clinical texts while preserving essential medical information. However, their ability to adapt content across ages, education levels, and languages without compromising accuracy has not been systematically evaluated.
We analyzed short excerpts (∼100 words) from de-identified Spanish-language SCI clinical reports that had historically posed communication challenges. A bilingual co-author produced human English translations, creating parallel English and Spanish corpora (10 texts per language: 4 originals and 6 audience-tailored simplifications). GPT-4o was applied for within-language simplification, not translation. Baseline readability quantifies the complexity of real-world clinical documentation directly. All original English texts had very high complexity by Flesch-Kincaid Grade Level (FKGL >18), whereas simplified texts reached FKGL 7.3 for children and 9.8–10.6 for adolescents and adults. Experts rated child-directed fidelity 2–3/5; adult-directed texts scored 4–5/5 for fidelity and appropriateness.
Original Spanish texts ranged from “Difficult” to “Very Difficult” by the Fernández–Huerta (FH) index, showing greater variability than English. Readability classifications were not concordant across languages (3/10 cases, 30%); e.g. one simplified version was “Plain English” by FKGL but “Fairly Difficult” by FH, highlighting language-specific behavior.
GPT-4o can tailor complex SCI clinical excerpts to specific audiences in English and Spanish, but child-directed versions may lose clinically relevant information. Clinician oversight remains essential for safe patient communication.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.31915973.v1（source: https://tandf.figshare.com/articles/dataset/Artificial_intelligence_for_tailoring_complex_clinical_information_to_patients_and_families_A_technical_perspective/31915973）。