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书名：临床大数据分析与挖掘——基于Python和机器学习的临床决策
定价：59.8
ISBN：9787121400391
作者：无
版次：第1版
出版时间：2020-11
内容提要：
本书不仅讲解了机器学习基本原理和基本方法，而且通过大量医疗领域的案例实现对医疗健康数据的处理和分析，能够在很大程度上辅助医护人员进行临床决策。通过本书学习，读者不仅能够掌握机器学习算法建模前的数据准备、筛选构造机器学习算法指标的特征工程、不同类别的机器学习算法，还能够掌握临床诊疗数据、电子病历档案数据及影像数据等多源异构数据的处理方法，以及医疗图像、文本等数据的读取、预处理、可视化等知识。同时，本书还介绍了具有开源、去编程化的TipDM 数据挖掘建模平台，通过拖曳的图形化操作就能实现数据分析的全流程。本书可以作为医学类院校数据科学与大数据技术专业的核心课程教材，以及医工专业的专业核心课程或选修课程教材。在此基础上，还可以作为临床、口腔、医技、检验、影像、公共卫生等医学类专业进阶层次的专业限选课程或拓展课程的教材。
作者简介：
孙丽萍，上海健康医学院医疗器械学副院长、教授。中国自动化学会常务委员、中国机器人大赛医疗服务机器人项目负责人、RoboCup Junior机器人世界杯中国赛医疗服务机器人项目负责人、中国服务机器人大赛负责人。中国科技部生产力促进中心服务机器人专业委员会委员。中国卫生信息与健康医疗大数据学会医疗健康专委会副秘书长。人工智能医疗器械标准化制定组专家。
目录：
第1 章机器学习 ··············································································································1
1.1 机器学习简介·······································································································1
1.1.1 机器学习的概念······························································································1
1.1.2 机器学习的应用领域························································································1
1.2 机器学习通用流程································································································2
1.2.1 目标分析·······································································································2
1.2.2 数据准备·······································································································3
1.2.3 特征工程·······································································································4
1.2.4 模型训练与调优······························································································5
1.2.5 性能度量与模型应用························································································6
1.3 Python 机器学习工具库简介·················································································6
1.3.1 数据准备相关工具库························································································6
1.3.2 数据可视化相关工具库·····················································································7
1.3.3 模型训练与评估相关工具库···············································································8
小结····························································································································9
课后习题 ··················································································································.10
第 2 章数据准备 ···········································································································.12
2.1 数据质量校验····································································································.12
2.1.1 一致性校验·································································································.12
2.1.2 缺失值校验·································································································.15
2.1.3 异常值校验·································································································.17
2.2 数据分布与趋势探查·························································································.18
2.2.1 分布分析····································································································.18
2.2.2 对比分析····································································································.22
2.2.3 描述性统计分析···························································································.25
2.2.4 周期性分析·································································································.28
2.2.5 贡献度分析·································································································.29
2.2.6 相关性分析·································································································.31
VIII
2.3 数据清洗···········································································································.35
2.3.1 缺失值处理·································································································.35
2.3.2 异常值处理·································································································.38
2.4 数据合并···········································································································.39
2.4.1 数据堆叠····································································································.39
2.4.2 主键合并····································································································.43
小结·························································································································.45
课后习题 ··················································································································.45
第 3 章特征工程 ···········································································································.48
3.1 特征变换···········································································································.48
3.1.1 标准化·······································································································.48
3.1.2 独热编码····································································································.54
3.1.3 离散化·······································································································.55
3.2 特征选择···········································································································.58
3.2.1 子集搜索与评价···························································································.58
3.2.2 过滤式选择·································································································.59
3.2.3 包裹式选择·································································································.59
3.2.4 嵌入式选择与L1 范数正则化···········································································.60
3.2.5 稀疏表示与字典学习·····················································································.61
小结·························································································································.63
课后习题 ··················································································································.63
第 4 章有监督学习 ········································································································.66
4.1 有监督学习简介································································································.66
4.2 性能度量···········································································································.66
4.2.1 分类任务性能度量························································································.66
4.2.2 回归任务性能度量························································································.68
4.3 线性模型···········································································································.69
4.3.1 线性模型简介······························································································.69
4.3.2 线性回归····································································································.69
4.3.3 逻辑回归····································································································.72
4.4 k 近邻分类········································································································.75
4.5 决策树··············································································································.78
4.5.1 决策树简介·································································································.78
4.5.2 ID3 算法·····································································································.79
4.5.3 C4.5 算法····································································································.81
4.5.4 CART 算法··································································································.83
4.6 支持向量机·······································································································.86
4.6.1 支持向量机简介···························································································.86
4.6.2 线性支持向量机···························································································.87
4.6.3 非线性支持向量机························································································.91
4.7 朴素贝叶斯·······································································································.94
4.8 神经网络···········································································································.98
4.8.1 神经网络介绍······························································································.98
4.8.2 BP 神经网络································································································.99
4.9 集成学习···········································································································104
4.9.1 Bagging ······································································································104
4.9.2 Boosting ·····································································································106
4.9.3 Stacking ······································································································115
小结·························································································································116
课后习题 ··················································································································116
第 5 章无监督学习 ········································································································118
5.1 无监督学习简介································································································118
5.2 降维··················································································································118
5.2.1 PCA ··········································································································118
5.2.2 核化线性降维······························································································121
5.3 聚类任务···········································································································123
5.3.1 聚类性能度量指标························································································124
5.3.2 距离计算····································································································125
5.3.3 原型聚类····································································································126
5.3.4 密度聚类····································································································137
5.3.5 层次聚类····································································································139
小结·························································································································142
课后习题 ··················································································································142
第 6 章智能推荐 ···········································································································144
6.1 智能推荐简介····································································································144
6.1.1 推荐系统····································································································144
6.1.2 智能推荐的应用···························································································144
6.2 推荐系统性能度量·····························································································146
6.2.1 离线实验评价指标························································································146
6.2.2 用户调查评价指标························································································148
6.2.3 在线实验评价指标························································································149
6.3 基于关联规则的推荐技术··················································································149
6.3.1 关联规则和频繁项集·····················································································150
6.3.2 Apriori 算法·································································································150
6.3.3 FP-Growth 算法····························································································154
6.4 基于协同过滤的推荐技术··················································································159
6.4.1 基于用户的协同过滤·····················································································159
6.4.2 基于物品的协同过滤·····················································································163
小结·························································································································166
课后习题 ··················································································································167
第 7 章医疗保险的欺诈发现 ··························································································169
7.1 目标分析···········································································································169
7.1.1 背景··········································································································169
7.1.2 数据说明····································································································170
7.1.3 分析目标····································································································171
7.2 数据准备···········································································································172
7.2.1 描述性统计分析···························································································172
7.2.2 数据清洗····································································································172
7.2.3 分析投保人和医疗机构的信息·········································································173
7.3 特征工程···········································································································177
7.3.1 特征选择····································································································177
7.3.2 特征变换····································································································178
7.4 模型训练···········································································································182
7.5 性能度量···········································································································184
7.5.1 结果分析····································································································184
7.5.2 聚类性能度量······························································································188
小结·························································································································190
第 8 章中医证型关联规则分析 ······················································································191
8.1 目标分析···········································································································191
8.1.1 背景··········································································································191
8.1.2 数据说明····································································································191
8.1.3 分析目标····································································································192
8.2 数据准备···········································································································193
8.2.1 数据获取····································································································193
8.2.2 数据清洗····································································································195
8.3 特征工程···········································································································196
8.3.1 特征选择····································································································196
8.3.2 特征变换····································································································197
8.4 模型训练···········································································································201
8.5 性能度量···········································································································202
8.5.1 结果分析····································································································203
8.5.2 模型应用····································································································204
小结·························································································································204
第 9 章糖尿病遗传风险预测 ··························································································205
9.1 目标分析···········································································································205
9.1.1 背景··········································································································205
9.1.2 数据说明····································································································206
9.1.3 分析目标····································································································207
9.2 数据准备···········································································································207
9.2.1 数据探索····································································································207
9.2.2 数据清洗····································································································209
9.3 特征工程···········································································································209
9.4 模型构建···········································································································211
9.4.1 交叉验证····································································································211
9.4.2 模型训练····································································································213
9.5 性能度量···········································································································214
9.5.1 结果分析····································································································214
9.5.2 模型评价····································································································216
小结·························································································································216
第 10 章基于深度残差神经网络的皮肤癌检测································································217
10.1 目标分析·········································································································217
10.1.1 背景·········································································································217
10.1.2 图像数据说明·····························································································218
10.1.3 分析方法与过程··························································································219
10.2 图像数据预处理······························································································219
10.2.1 图像预处理································································································219
10.2.2 查看处理后的图像·······················································································222
10.3 模型构建·········································································································223
10.3.1 卷积神经网络（CNN） ················································································223
10.3.2 残差网络（Residual Network） ·······································································226
10.3.3 ImageDataGenerator 参数说明·········································································228
10.3.4 训练深度残差神经网络模型···········································································229
10.4 性能度量·········································································································231
10.4.1 性能分析···································································································231
10.4.2 结果分析···································································································232
小结·························································································································234
第 11 章基于 TipDM 数据挖掘建模平台实现医疗保险的欺诈发现··································236
11.1 TipDM 数据挖掘建模平台················································································236
11.1.1 首页·········································································································237
11.1.2 数据源······································································································238
11.1.3 工程·········································································································239
11.1.4 系统组件···································································································240
11.1.5 TipDM 数据挖掘建模平台的本地化部署···························································241
11.2 快速构建医疗保险的欺诈发现工程··································································243
11.2.1 获取数据···································································································244
11.2.2 数据准备···································································································247
11.2.3 特征工程···································································································250
11.2.4 模型训练···································································································253
小结·························································································································255
参考文献 ·························································································································256
定价：59.8
ISBN：9787121400391
作者：无
版次：第1版
出版时间：2020-11
内容提要：
本书不仅讲解了机器学习基本原理和基本方法，而且通过大量医疗领域的案例实现对医疗健康数据的处理和分析，能够在很大程度上辅助医护人员进行临床决策。通过本书学习，读者不仅能够掌握机器学习算法建模前的数据准备、筛选构造机器学习算法指标的特征工程、不同类别的机器学习算法，还能够掌握临床诊疗数据、电子病历档案数据及影像数据等多源异构数据的处理方法，以及医疗图像、文本等数据的读取、预处理、可视化等知识。同时，本书还介绍了具有开源、去编程化的TipDM 数据挖掘建模平台，通过拖曳的图形化操作就能实现数据分析的全流程。本书可以作为医学类院校数据科学与大数据技术专业的核心课程教材，以及医工专业的专业核心课程或选修课程教材。在此基础上，还可以作为临床、口腔、医技、检验、影像、公共卫生等医学类专业进阶层次的专业限选课程或拓展课程的教材。
作者简介：
孙丽萍，上海健康医学院医疗器械学副院长、教授。中国自动化学会常务委员、中国机器人大赛医疗服务机器人项目负责人、RoboCup Junior机器人世界杯中国赛医疗服务机器人项目负责人、中国服务机器人大赛负责人。中国科技部生产力促进中心服务机器人专业委员会委员。中国卫生信息与健康医疗大数据学会医疗健康专委会副秘书长。人工智能医疗器械标准化制定组专家。
目录：
第1 章机器学习 ··············································································································1
1.1 机器学习简介·······································································································1
1.1.1 机器学习的概念······························································································1
1.1.2 机器学习的应用领域························································································1
1.2 机器学习通用流程································································································2
1.2.1 目标分析·······································································································2
1.2.2 数据准备·······································································································3
1.2.3 特征工程·······································································································4
1.2.4 模型训练与调优······························································································5
1.2.5 性能度量与模型应用························································································6
1.3 Python 机器学习工具库简介·················································································6
1.3.1 数据准备相关工具库························································································6
1.3.2 数据可视化相关工具库·····················································································7
1.3.3 模型训练与评估相关工具库···············································································8
小结····························································································································9
课后习题 ··················································································································.10
第 2 章数据准备 ···········································································································.12
2.1 数据质量校验····································································································.12
2.1.1 一致性校验·································································································.12
2.1.2 缺失值校验·································································································.15
2.1.3 异常值校验·································································································.17
2.2 数据分布与趋势探查·························································································.18
2.2.1 分布分析····································································································.18
2.2.2 对比分析····································································································.22
2.2.3 描述性统计分析···························································································.25
2.2.4 周期性分析·································································································.28
2.2.5 贡献度分析·································································································.29
2.2.6 相关性分析·································································································.31
VIII
2.3 数据清洗···········································································································.35
2.3.1 缺失值处理·································································································.35
2.3.2 异常值处理·································································································.38
2.4 数据合并···········································································································.39
2.4.1 数据堆叠····································································································.39
2.4.2 主键合并····································································································.43
小结·························································································································.45
课后习题 ··················································································································.45
第 3 章特征工程 ···········································································································.48
3.1 特征变换···········································································································.48
3.1.1 标准化·······································································································.48
3.1.2 独热编码····································································································.54
3.1.3 离散化·······································································································.55
3.2 特征选择···········································································································.58
3.2.1 子集搜索与评价···························································································.58
3.2.2 过滤式选择·································································································.59
3.2.3 包裹式选择·································································································.59
3.2.4 嵌入式选择与L1 范数正则化···········································································.60
3.2.5 稀疏表示与字典学习·····················································································.61
小结·························································································································.63
课后习题 ··················································································································.63
第 4 章有监督学习 ········································································································.66
4.1 有监督学习简介································································································.66
4.2 性能度量···········································································································.66
4.2.1 分类任务性能度量························································································.66
4.2.2 回归任务性能度量························································································.68
4.3 线性模型···········································································································.69
4.3.1 线性模型简介······························································································.69
4.3.2 线性回归····································································································.69
4.3.3 逻辑回归····································································································.72
4.4 k 近邻分类········································································································.75
4.5 决策树··············································································································.78
4.5.1 决策树简介·································································································.78
4.5.2 ID3 算法·····································································································.79
4.5.3 C4.5 算法····································································································.81
4.5.4 CART 算法··································································································.83
4.6 支持向量机·······································································································.86
4.6.1 支持向量机简介···························································································.86
4.6.2 线性支持向量机···························································································.87
4.6.3 非线性支持向量机························································································.91
4.7 朴素贝叶斯·······································································································.94
4.8 神经网络···········································································································.98
4.8.1 神经网络介绍······························································································.98
4.8.2 BP 神经网络································································································.99
4.9 集成学习···········································································································104
4.9.1 Bagging ······································································································104
4.9.2 Boosting ·····································································································106
4.9.3 Stacking ······································································································115
小结·························································································································116
课后习题 ··················································································································116
第 5 章无监督学习 ········································································································118
5.1 无监督学习简介································································································118
5.2 降维··················································································································118
5.2.1 PCA ··········································································································118
5.2.2 核化线性降维······························································································121
5.3 聚类任务···········································································································123
5.3.1 聚类性能度量指标························································································124
5.3.2 距离计算····································································································125
5.3.3 原型聚类····································································································126
5.3.4 密度聚类····································································································137
5.3.5 层次聚类····································································································139
小结·························································································································142
课后习题 ··················································································································142
第 6 章智能推荐 ···········································································································144
6.1 智能推荐简介····································································································144
6.1.1 推荐系统····································································································144
6.1.2 智能推荐的应用···························································································144
6.2 推荐系统性能度量·····························································································146
6.2.1 离线实验评价指标························································································146
6.2.2 用户调查评价指标························································································148
6.2.3 在线实验评价指标························································································149
6.3 基于关联规则的推荐技术··················································································149
6.3.1 关联规则和频繁项集·····················································································150
6.3.2 Apriori 算法·································································································150
6.3.3 FP-Growth 算法····························································································154
6.4 基于协同过滤的推荐技术··················································································159
6.4.1 基于用户的协同过滤·····················································································159
6.4.2 基于物品的协同过滤·····················································································163
小结·························································································································166
课后习题 ··················································································································167
第 7 章医疗保险的欺诈发现 ··························································································169
7.1 目标分析···········································································································169
7.1.1 背景··········································································································169
7.1.2 数据说明····································································································170
7.1.3 分析目标····································································································171
7.2 数据准备···········································································································172
7.2.1 描述性统计分析···························································································172
7.2.2 数据清洗····································································································172
7.2.3 分析投保人和医疗机构的信息·········································································173
7.3 特征工程···········································································································177
7.3.1 特征选择····································································································177
7.3.2 特征变换····································································································178
7.4 模型训练···········································································································182
7.5 性能度量···········································································································184
7.5.1 结果分析····································································································184
7.5.2 聚类性能度量······························································································188
小结·························································································································190
第 8 章中医证型关联规则分析 ······················································································191
8.1 目标分析···········································································································191
8.1.1 背景··········································································································191
8.1.2 数据说明····································································································191
8.1.3 分析目标····································································································192
8.2 数据准备···········································································································193
8.2.1 数据获取····································································································193
8.2.2 数据清洗····································································································195
8.3 特征工程···········································································································196
8.3.1 特征选择····································································································196
8.3.2 特征变换····································································································197
8.4 模型训练···········································································································201
8.5 性能度量···········································································································202
8.5.1 结果分析····································································································203
8.5.2 模型应用····································································································204
小结·························································································································204
第 9 章糖尿病遗传风险预测 ··························································································205
9.1 目标分析···········································································································205
9.1.1 背景··········································································································205
9.1.2 数据说明····································································································206
9.1.3 分析目标····································································································207
9.2 数据准备···········································································································207
9.2.1 数据探索····································································································207
9.2.2 数据清洗····································································································209
9.3 特征工程···········································································································209
9.4 模型构建···········································································································211
9.4.1 交叉验证····································································································211
9.4.2 模型训练····································································································213
9.5 性能度量···········································································································214
9.5.1 结果分析····································································································214
9.5.2 模型评价····································································································216
小结·························································································································216
第 10 章基于深度残差神经网络的皮肤癌检测································································217
10.1 目标分析·········································································································217
10.1.1 背景·········································································································217
10.1.2 图像数据说明·····························································································218
10.1.3 分析方法与过程··························································································219
10.2 图像数据预处理······························································································219
10.2.1 图像预处理································································································219
10.2.2 查看处理后的图像·······················································································222
10.3 模型构建·········································································································223
10.3.1 卷积神经网络（CNN） ················································································223
10.3.2 残差网络（Residual Network） ·······································································226
10.3.3 ImageDataGenerator 参数说明·········································································228
10.3.4 训练深度残差神经网络模型···········································································229
10.4 性能度量·········································································································231
10.4.1 性能分析···································································································231
10.4.2 结果分析···································································································232
小结·························································································································234
第 11 章基于 TipDM 数据挖掘建模平台实现医疗保险的欺诈发现··································236
11.1 TipDM 数据挖掘建模平台················································································236
11.1.1 首页·········································································································237
11.1.2 数据源······································································································238
11.1.3 工程·········································································································239
11.1.4 系统组件···································································································240
11.1.5 TipDM 数据挖掘建模平台的本地化部署···························································241
11.2 快速构建医疗保险的欺诈发现工程··································································243
11.2.1 获取数据···································································································244
11.2.2 数据准备···································································································247
11.2.3 特征工程···································································································250
11.2.4 模型训练···································································································253
小结·························································································································255
参考文献 ·························································································································256
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