Introduction to Medical Data Analysis with Python
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Summary
This book covers traditional statistical analysis methods and more complex machine learning algorithms, combining a large number of carefully selected examples and using Python for data analysis. It provides an accessible introduction to commonly used analysis methods to help readers solve practical problems in data analysis.
The book emphasizes hands-on practice and application, deliberately downplaying the derivation and computation processes of analysis methods. The numerous Python program examples are the highlight of this book. By reading it, readers will not only master methods for quickly solving practical problems using Python and related libraries, but also gain a deeper understanding of data analysis.
This book is suitable not only for undergraduates and graduate students in clinical medicine, public health, and other medicine-related majors, but also as a reference for students and researchers in other fields learning data analysis.
Editor's review
Unlike broad data analysis tutorials, the book closely follows the characteristics of medical data from the outset—such as clinical trial data, electronic medical records and genomics data—and introduces common formats in medical data (such as the CDISC standard), ethical considerations, and special issues in preprocessing (such as the meaning of missing-value handling and outlier judgment in a medical context). At the technical level, the book centers on pandas, NumPy, Matplotlib and seaborn, and appropriately introduces scikit-learn for basic machine learning modeling, with content arranged from shallow to deep. Its highlight is the large number of cases based on real medical research scenarios, such as using survival analysis to handle patient follow-up data and using statistical tests to compare efficacy differences between treatment groups. These cases keep code learning from being an airy abstraction.
As an editor, I find the book's positioning very clear: it is the first Python tool book tailored for researchers, students and healthcare industry practitioners in the medical field. It avoids overly abstruse computer theory and emphasizes 'learn and use immediately,' driving learning through solving specific medical problems one by one. The book's layout and code examples are relatively clear, and the sample data provided with the book enhances operability. Of course, as an 'introductory' reader, it makes trade-offs in the depth of statistical theory and the complexity of machine learning models, aiming to help readers quickly build a bridge from medical problems to code implementation and lay a solid practical foundation for more in-depth professional study.
Who it's for
A good fit for
- Senior undergraduates or graduate students in clinical medicine, public health, biomedical and related fields who wish to use Python to process research data
- Newcomers to data analysis roles at medical institutions or pharmaceutical companies who need to quickly get started processing clinical or experimental data
- Computer science students or engineers interested in medical data analysis who want to understand domain-specific needs and application scenarios
- Medical researchers hoping to improve research efficiency and learn to use programming tools to replace some manual statistical operations
Not a good fit for
- Readers seeking advanced AI applications in medicine such as deep learning or complex medical image processing
- Readers who want to systematically study statistical principles rather than tool implementation
Reading tips
- Follow the chapters in order, focusing on Chapters 3-5 on data cleaning, exploration, and visualization, which are the foundation for later analysis
- Be sure to run the code examples in the book on your computer as you study, and try applying them to the provided datasets
- When encountering unfamiliar medical concepts (such as 'survival analysis' or 'ROC curve'), use them as leads and consult professional textbooks for further study to deepen understanding
About the publisher
Posts & Telecom Press is known for its strengths in science and technology, computing, and information and communications titles, and has extended its publishing into photography, art, software learning, and inspirational growth, giving it a fairly diverse range. Its catalog includes computing and communications works such as Refactoring and Patterns (Revised Edition) and Huawei Switch Learning Guide (Second Edition), alongside bilingual collector's editions such as The Little Prince (Chinese-English-French illustrated collector's edition), The Great Gatsby, and Three Days to See. Beijing Ten Years: 1980-1990 and Ghost Blade: WLOP Personal Illustration Collection reflect its presence in photography and art. Its authors include Antoine de Saint-Exupéry, Helen Keller, Joshua Kerievsky, Wang Da, Zhang Zhaozeng, and WLOP.
Contents
- 版权信息
- 内容提要
- 前言
- 资源与支持
- 第1章 Python语言基础
- 1.1 关于Python
- 1.2 为什么使用Python分析数据
- 1.3 重要的Python库
- 1.4 安装与设置
- 1.4.1 在Windows或MacOS系统上安装Anaconda
- 1.4.2 在Linux系统上安装Anaconda
- 1.4.3 安装和更新包
- 1.4.4 Python解释器
- 1.4.5 导入库
- 1.5 代码编写工具
- 1.6 开始使用Python
- 1.6.1 获取帮助
- 1.6.2 把Python当作一个计算器
- 1.6.3 Python对象
- 1.7 工作目录
- 1.8 习题
- 第2章 基本数据结构
- 2.1 列表
- 2.1.1 列表的创建
- 2.1.2 列表基本操作
- 2.1.3 列表方法与函数操作
- 2.2 元组
- 2.2.1 元组的创建
- 2.2.2 元组的操作
- 2.3 字典
- 2.3.1 字典的创建
- 2.3.2 字典的操作
- 2.4 集合
- 2.4.1 集合的创建
- 2.4.2 集合的操作
- 2.5 习题
- 第3章 控制流、函数与文件操作
- 3.1 条件语句
- 3.1.1 简单条件结构
- 3.1.2 嵌套条件结构
- 3.2 循环语句
- 3.2.1 for循环
- 3.2.2 while循环
- 3.3 函数
- 3.3.1 定义函数
- 3.3.2 默认参数
- 3.3.3 任意参数
- 3.3.4 匿名函数
- 3.4 文件操作
- 3.4.1 读取txt文件
- 3.4.2 写入txt文件
- 3.4.3 读写CSV文件
- 3.5 习题
- 第4章 NumPy基础
- 4.1 创建数组对象
- 4.1.1 使用函数array创建数组对象
- 4.1.2 使用专门函数创建数组对象
- 4.1.3 生成伪随机数
- 4.2 数组操作
- 4.2.1 数组重塑
- 4.2.2 数组转置和轴变换
- 4.2.3 数组的索引和切片
- 4.3 数组运算
- 4.3.1 通用函数
- 4.3.2 基本统计运算
- 4.3.3 矩阵运算
- 4.4 数组文件的保存与导入
- 4.5 习题
- 第5章 Pandas入门
- 5.1 Pandas数据结构
- 5.1.1 Series
- 5.1.2 DataFrame
- 5.2 Pandas对象基本操作
- 5.2.1 索引操作
- 5.2.2 DataFrame的查询与子集选择
- 5.3 DataFrame的导入和导出
- 5.3.1 读写文本文件
- 5.3.2 读写其他格式的文件
- 5.4 Pandas数据预处理
- 5.4.1 数据的合并
- 5.4.2 数据长宽格式的转换
- 5.4.3 缺失值的识别与处理
- 5.4.4 数据值的转换
- 5.5 习题
- 第6章 数据可视化
- 6.1 Matplotlib绘图基础
- 6.1.1 函数plot与图形元素
- 6.1.2 全局参数查看与设置
- 6.1.3 一页多图
- 6.1.4 保存图形
- 6.1.5 基本统计图形
- 6.2 Seaborn数据可视化
- 6.2.1 Seaborn简介
- 6.2.2 直方图和密度曲线图
- 6.2.3 条形图
- 6.2.4 箱线图和小提琴图
- 6.2.5 点图
- 6.2.6 带状点图与簇状点图
- 6.2.7 散点图
- 6.2.8 散点图矩阵
- 6.2.9 多面板图
- 6.2.10 回归图
- 6.2.11 分面网格图
- 6.2.12 Seaborn图形保存
- 6.3 其他Python数据可视化工具
- 6.4 习题
- 第7章 基本统计分析
- 7.1 查看数据集信息
- 7.2 数值型变量的统计描述
- 7.3 数值型变量的假设检验
- 7.3.1 单个样本的t检验
- 7.3.2 独立样本的t检验
- 7.3.3 非独立样本的t检验
- 7.3.4 单因素方差分析
- 7.3.5 组间差异的非参数检验
- 7.3.6 连续型变量之间的相关性
- 7.4 分类变量的列联表和独立性检验
- 7.4.1 生成频数表
- 7.4.2 独立性检验
- 7.5 习题
- 第8章 线性模型与广义线性模型
- 8.1 线性模型
- 8.1.1 简单线性回归模型
- 8.1.2 多重线性回归模型
- 8.2 Logistic回归
- 8.2.1 Logistic回归模型
- 8.2.2 Logistic回归实例
- 8.3 Poisson回归
- 8.3.1 Poisson回归模型
- 8.3.2 Poisson回归实例
- 8.4 生存分析与Cox回归
- 8.4.1 生存分析简介
- 8.4.2 生存率的Kaplan-Meier估计
- 8.4.3 Cox回归
- 8.5 习题
- 第9章 Scikit-learn机器学习入门
- 9.1 机器学习简介
- 9.2 加载数据集
- 9.3 学习和预测
- 9.3.1 无监督学习
- 9.3.2 监督学习
- 9.4 模型的选择与评估
- 9.5 习题
- 第10章 TensorFlow深度学习入门
- 10.1 深度学习简介
- 10.2 感知机与神经网络
- 10.3 激活函数
- 10.4 损失函数
- 10.5 优化器
- 10.6 构建并训练神经网络
- 10.7 习题
- 第11章 图像分类卷积神经网络模型
- 11.1 卷积神经网络
- 11.1.1 局部感受野
- 11.1.2 共享权重和偏置
- 11.1.3 池化
- 11.2 加载数据集
- 11.3 构建卷积神经网络模型
- 11.4 编译并训练模型
- 11.5 评估模型
- 11.6 习题
- 习题参考答案
FAQ
Can I study this book without any programming background?
What is the difference between this book and general Python data analysis books (such as Python for Data Analysis)?
Which Python version are the code and environment in the book based on?
Comments
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About the author
Zhao Jun is an author whose works span a notably wide range of subjects. Public information about him is limited, and his nationality, dates of birth and death, and educational background are not clearly recorded. Judging from the four works held in the collection, his writing crosses history, politics, programming and design, and the social sciences. In history, he focuses on Sino-Japanese relations and modern Chinese politics, having written on Japanese "continental adventurers" and the war of aggression against China, as well as a study of the Zunyi Conference, which reflects his interest in modern Chinese history and the sources of the War of Resistance. On the technical side, he has written an introduction to Python for medical data analysis and a study of spatial information development and models, showing an engagement with programming practice and information science. This parallel path of humanities and technology gives his works both a documentary and an applied orientation, useful to readers in the relevant fields.
Liu Wenting: currently, publicly available information is limited, and her educational background, affiliated institution, and personal experience cannot yet be confirmed. Judging from the two works held in the collection, her writing spans two rather distant fields. The first, Python Medical Data Analysis for Beginners, addresses the practical needs of medical data processing and uses Python as a tool to explain the introductory methods of data analysis, making it a practical book in programming and design. The second, Knowledge Collaboration in Technical Standard Alliances: Theory and Cases, explores theoretical issues of knowledge collaboration within technical standard alliances and combines them with case analysis, falling under society and culture. One work leans toward technical practice, the other toward research on organization and knowledge management, suggesting that her interests cross data technology and industrial collaboration. The available information is insufficient to outline her full body of work, and readers may refer to the above works to understand the directions she focuses on.
