皮肤癌生存分析人工智能方法现状范围综述（数据表1）
包含皮肤癌生存分析中人工智能方法综述的数据表，涵盖多种AI方法及文献筛选结果。
基本信息
资源简介
皮肤癌生存分析人工智能方法现状范围综述（数据表1）
【一句话概览】
本数据集是 figshare 公开的文本数据集，收录 1 个文件、合计 17.05 KB，主题涵盖 artificial intelligence、skin cancer、oncology、machine learning、deep learning，可用于医学自然语言处理（实体识别、信息抽取等），CC BY 4.0 许可开放获取。
【数据集背景】
本数据集源站标题为《Data_Sheet_1_Current status of artificial intelligence methods for skin cancer survival analysis: a scoping review.docx》。配套论文《Cancer diagnosis using artificial intelligence: a review》发表于 Artificial Intelligence Review（2021），DOI：10.1007/s10462-021-10074-4。源站首次发布：2024-04-22。
【数据内容】
源站原始描述：Skin cancer mortality rates continue to rise, and survival analysis is increasingly needed to understand who is at risk and what interventions improve outcomes. However, current statistical methods are limited by inability to synthesize multiple data types, such as patient genetics, clinical history, demographics, and pathology and reveal significant multimodal relationships through predictive algorithms. Advances in computing power and data science enabled the rise of artificial intelligence (A…
【数据结构与技术规格】
文件数 1 个，合计 17.05 KB。Data_Sheet_1_Current status of artificial intelligence methods for skin cancer survival analysis: a scoping review.docx：application/vnd.openxmlformats-officedocument.wordprocessingml.document，17.05 KB。数据集 DOI：10.3389/fmed.2024.1243659.s001。
【主题与分类】
源站学科分类：Radiology and Organ Imaging、Foetal Development and Medicine、Obstetrics and Gynaecology、Primary Health Care、Medical and Health Sciences not elsewhere classified、Dermatology。主题标签：artificial intelligence、skin cancer、oncology、machine learning、deep learning、supervised learning、unsupervised learning、natural language processing。
【适用方向】
文本类数据可用于医学自然语言处理，如命名实体识别、信息抽取与文本分类。
【获取与许可】
源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Current_status_of_artificial_intelligence_methods_for_skin_cancer_survival_analysis_a_scoping_review_docx/25661982
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Schreidah, Celine M.; Gordon, Emily R.; Adeuyan, Oluwaseyi; Chen, Caroline; Lapolla, Brigit A.; Kent, Joshua A.; et al. (2024). Data_Sheet_1_Current status of artificial intelligence methods for skin cancer survival analysis: a scoping review.docx. Frontiers. Dataset
下载信息
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使用方式
数据集获取（Figshare）
- 源站标题：Data_Sheet_1_Current status of artificial intelligence methods for skin cancer survival analysis: a scoping review.docx
- DOI：10.3389/fmed.2024.1243659.s001
- 发布日期：2024-04-22
- 文件数：1 个
- 文件总体积：17.05 KB
- 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Current_status_of_artificial_intelligence_methods_for_skin_cancer_survival_analysis_a_scoping_review_docx/25661982
命令行下载
curl -L -o "Data_Sheet_1_Current status of artificial intelligence methods for skin cancer survival analysis: a scoping review.docx" "https://ndownloader.figshare.com/files/45777216"
文件清单
Data_Sheet_1_Current status of artificial intelligence methods for skin cancer survival analysis: a scoping review.docx（17.05 KB）
文件总体积 17.05 KB（源站 API 实测）。
数据说明
Skin cancer mortality rates continue to rise, and survival analysis is increasingly needed to understand who is at risk and what interventions improve outcomes. However, current statistical methods are limited by inability to synthesize multiple data types, such as patient genetics, clinical history, demographics, and pathology and reveal significant multimodal relationships through predictive algorithms. Advances in computing power and data science enabled the rise of artificial intelligence (AI), which synthesizes vast amounts of data and applies algorithms that enable personalized diagnostic approaches. Here, we analyze AI methods used in skin cancer survival analysis, focusing on supervised learning, unsupervised learning, deep learning, and natural language processing. We illustrate strengths and weaknesses of these approaches with examples. Our PubMed search yielded 14 publications meeting inclusion criteria for this scoping review. Most publications focused on melanoma, particularly histopathologic interpretation with deep learning. Such concentration on a single type of skin cancer amid increasing focus on deep learning highlight growing areas for innovation; however, it also demonstrates opportunity for additional analysis that addresses other types of cutaneous malignancies and expands the scope of prognostication to combine both genetic, histopathologic, and clinical data. Moreover, researchers may leverage multiple AI methods for enhanced benefit in analyses. Expanding AI to this arena may enable improved survival analysis, targeted treatments, and outcomes.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.3389/fmed.2024.1243659.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Current_status_of_artificial_intelligence_methods_for_skin_cancer_survival_analysis_a_scoping_review_docx/25661982）。
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
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