宫颈癌切除术复发预测标准数据集

本数据集专为原发性宫颈癌术后复发风险的智能预测任务构建，旨在通过机器学习技术解决传统FIGO分期无法精准量化个体复发风险、指导术后个体化治疗决策效能有限的问题。数据采用高维结构化特征格式，适配逻辑回归、支持向量机、随机森林及深度学习等主流预测模型的训练。
输入端（Features）： 结合人口信息、住院信息、诊断信息、病理信息、检验信息、随访信息等。输出端（Labels）： 严格基于患者长期随访结果判定的复发状态标签，涵盖复发的时间节点与结局。
数据源采用全量“临床真实数据”架构。所有数据均源自权威省级妇科恶性肿瘤精准治疗临床医学研究中心，严格遵循医学伦理进行脱敏处理，保证患者隐私。全量数据经严格质量审核与清洗，排除不完整、不准确病例，确保数据可靠性，是训练高精度、高鲁棒性复发预测模型，辅助医生制定个体化术后辅助放化疗与随访策略的高质量核心科研语料。

This dataset is specifically constructed for the intelligent prediction task of postoperative recurrence risk of primary cervical cancer, aiming to address the limitation that traditional FIGO staging fails to accurately quantify individual recurrence risk and has limited efficacy in guiding individualized postoperative treatment decisions through machine learning technologies.

The dataset adopts a high-dimensional structured feature format, which is compatible with the training of mainstream prediction models including logistic regression, support vector machine (SVM), random forest and deep learning.

Input (Features): It integrates demographic information, hospitalization information, diagnostic information, pathological information, laboratory test information, follow-up information and other relevant clinical data.
Output (Labels): Recurrence status labels strictly determined based on the long-term follow-up results of patients, covering the timing and final outcome of recurrence.

The dataset adopts a full-scale "real-world clinical data" framework. All data are sourced from an authoritative provincial clinical research center for precise treatment of gynecological malignancies, and have undergone strict de-identification processing in compliance with medical ethics to protect patient privacy. The full dataset has been subjected to strict quality review and data cleaning, excluding incomplete and inaccurate cases to ensure data reliability. It serves as a high-quality core scientific research corpus for training high-precision and high-robustness recurrence prediction models, and assisting clinicians in formulating individualized postoperative adjuvant chemoradiotherapy and follow-up strategies.

搜集汇总

背景与挑战

背景概述

该数据集专为原发性宫颈癌术后复发风险的智能预测任务构建，采用高维结构化特征格式，适配逻辑回归、支持向量机等主流机器学习模型。数据源自权威临床医学研究中心的真实临床数据，经过严格脱敏和质量审核，旨在辅助医生制定个体化术后治疗与随访策略，提升预后管理精准度。