心力衰竭临床记录数据集

Provide the names, email addresses, institutions, and other contact information of the donors and creators of the data set.The original dataset version was collected by Tanvir Ahmad, Assia Munir, Sajjad Haider Bhatti, Muhammad Aftab, and Muhammad Ali Raza (Government College University, Faisalabad, Pakistan) and made available by them on FigShare under the Attribution 4.0 International (CC BY 4.0: freedom to share and adapt the material) copyright in July 2017. The current version of the dataset was elaborated by Davide Chicco (Krembil Research Institute, Toronto, Canada) and donated to the University of California Irvine Machine Learning Repository under the same Attribution 4.0 International (CC BY 4.0) copyright in January 2020. Davide Chicco can be reached at <davidechicco '@' davidechicco.it> Data Set Information: A detailed description of the dataset can be found in the Dataset section of the following paper: Davide Chicco, Giuseppe Jurman: "Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone". BMC Medical Informatics and Decision Making 20, 16 (2020). [Web link] Attribute Information: Thirteen (13) clinical features: - age: age of the patient (years) - anaemia: decrease of red blood cells or hemoglobin (boolean) - high blood pressure: if the patient has hypertension (boolean) - creatinine phosphokinase (CPK): level of the CPK enzyme in the blood (mcg/L) - diabetes: if the patient has diabetes (boolean) - ejection fraction: percentage of blood leaving the heart at each contraction (percentage) - platelets: platelets in the blood (kiloplatelets/mL) - sex: woman or man (binary) - serum creatinine: level of serum creatinine in the blood (mg/dL) - serum sodium: level of serum sodium in the blood (mEq/L) - smoking: if the patient smokes or not (boolean) - time: follow-up period (days) - [target] death event: if the patient deceased during the follow-up period (boolean) For more information, please check Table 1, Table 2, and Table 3 of the following paper: Davide Chicco, Giuseppe Jurman: "Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone". BMC Medical Informatics and Decision Making 20, 16 (2020). [Web link] Relevant Papers: Original dataset version: Tanvir Ahmad, Assia Munir, Sajjad Haider Bhatti, Muhammad Aftab, and Muhammad Ali Raza: "Survival analysis of heart failure patients: a case study". PLoS ONE 12(7), 0181001 (2017). [Web link] Current dataset version on the UCI ML Repository: Davide Chicco, Giuseppe Jurman: "Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone". BMC Medical Informatics and Decision Making 20, 16 (2020). [Web link] Citation Request: Davide Chicco, Giuseppe Jurman: "Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone". BMC Medical Informatics and Decision Making 20, 16 (2020). [Web link]

请提供本数据集捐赠者与创建者的姓名、电子邮箱、所属机构及其他联系方式。本数据集的原始版本由巴基斯坦费萨拉巴德政府学院大学的Tanvir Ahmad、Assia Munir、Sajjad Haider Bhatti、Muhammad Aftab及Muhammad Ali Raza收集，并于2017年7月通过FigShare平台以署名4.0国际许可（CC BY 4.0：自由共享和改编素材）发布。当前版本的数据集由加拿大多伦多克雷姆比尔研究所的Davide Chicco整理完善，并于2020年1月以相同的CC BY 4.0许可捐赠至加州大学欧文分校机器学习存储库（UCI Machine Learning Repository）。可通过<davidechicco '@' davidechicco.it>联系Davide Chicco。

数据集信息：本数据集的详细说明可参阅下述论文的数据集章节：Davide Chicco、Giuseppe Jurman：《仅通过血清肌酐和射血分数即可预测心力衰竭患者生存率的机器学习方法》，BMC Medical Informatics and Decision Making 20, 16 (2020)。[网页链接]

属性信息：共包含13项临床特征：
- 年龄（age）：患者年龄（单位：年）
- 贫血（anaemia）：红细胞或血红蛋白水平降低（布尔型）
- 高血压（high blood pressure）：患者是否患有高血压（布尔型）
- 肌酸激酶（creatinine phosphokinase, CPK）：血液中CPK酶水平（单位：mcg/L）
- 糖尿病（diabetes）：患者是否患有糖尿病（布尔型）
- 射血分数（ejection fraction）：每次心脏收缩时泵出的血液百分比（单位：%）
- 血小板（platelets）：血液中血小板计数（单位：kiloplatelets/mL）
- 性别（sex）：女性/男性（二分类）
- 血清肌酐（serum creatinine）：血液中血清肌酐水平（单位：mg/dL）
- 血清钠（serum sodium）：血液中血清钠水平（单位：mEq/L）
- 吸烟（smoking）：患者是否吸烟（布尔型）
- 随访时间（time）：随访周期（单位：天）
- [目标变量] 死亡事件（death event）：随访期内患者是否死亡（布尔型）

如需更多详细信息，请参阅下述论文的表1、表2及表3：Davide Chicco、Giuseppe Jurman：《仅通过血清肌酐和射血分数即可预测心力衰竭患者生存率的机器学习方法》，BMC Medical Informatics and Decision Making 20, 16 (2020)。[网页链接]

相关论文：
1. 原始数据集版本：Tanvir Ahmad、Assia Munir、Sajjad Haider Bhatti、Muhammad Aftab及Muhammad Ali Raza：《心力衰竭患者的生存分析：案例研究》，PLoS ONE 12(7), 0181001 (2017)。[网页链接]
2. UCI机器学习存储库中的当前数据集版本：Davide Chicco、Giuseppe Jurman：《仅通过血清肌酐和射血分数即可预测心力衰竭患者生存率的机器学习方法》，BMC Medical Informatics and Decision Making 20, 16 (2020)。[网页链接]

引用要求：引用本数据集时请使用下述文献：Davide Chicco、Giuseppe Jurman：《仅通过血清肌酐和射血分数即可预测心力衰竭患者生存率的机器学习方法》，BMC Medical Informatics and Decision Making 20, 16 (2020)。[网页链接]

搜集汇总

背景与挑战

背景概述

该数据集包含13个心力衰竭患者的临床特征，如年龄、贫血、高血压、射血分数等，用于预测患者生存情况。数据集最初由巴基斯坦的研究团队收集，后经加拿大的研究者整理并公开，适用于机器学习在医疗决策支持中的应用。