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Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (11): 63-73    DOI: 10.11925/infotech.2096-3467.2020.0469
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Predicting Hospital Readmissions with Deep Learning: Case Study of Heart Diseases
Da Jingwei1,Yan Jiaqi1(),Deng Sanhong1,2,Wang Zhongmin3
1School of Information Management, Nanjing University, Nanjing 210023, China
2Jiangsu Key Laboratory of Data Engineering and Knowledge Service, Nanjing 210023, China
3Jiangsu Province Hospital (The First Affiliated Hospital of Nanjing Medical University), Nanjing 210029, China
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Abstract  

[Objective] This paper uses the deep learning method to predict possible readmissions of patients based on their electronic medical records, aiming to improve hospital management. [Methods] We proposed a model based on character-level convolution neural network to process the unstructured texts. Then, with the help of structured data (demographics, clinical records and administrative data) to predict the hospital readmission cases. [Results] The deep learning model combining structured and unstructured data yielded better prediction results at F1-score of 0.735. Compared with the models only using structured or unstructured data, the F1-score was increased by 12.9% and 2.1%, respectively. [Limitations] The experimental medical records were collected from one hospital, which has some impacts on prediction results. [Conclusions] The proposed model provides references for researchers of hospital readmission prediction and hospital administrators.
Key words： Hospital Readmission      Deep Learning      Heart Disease      Predictive Analysis     
Received: 27 May 2020      Published: 02 September 2020
ZTFLH:  TP391  
Corresponding Authors: Yan Jiaqi     E-mail: jiaqiyan@nju.edu.cn
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Jingwei Da
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Zhongmin Wang

Cite this article:

Da Jingwei,Yan Jiaqi,Deng Sanhong,Wang Zhongmin. Predicting Hospital Readmissions with Deep Learning: Case Study of Heart Diseases. Data Analysis and Knowledge Discovery, 2020, 4(11): 63-73.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2020.0469     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2020/V4/I11/63

Research Framework
特征子集 特征名称 数据类型 数据描述
人口统计学数据 性别 分类变量 男(2 394, 65.4%); 女(1 266, 34.6%)
婚姻状态 分类变量 已婚(3 510, 95.9%); 未婚(150, 4.1%)
临床数据 收缩压 数值型变量 平均值=129.582; 方差=18.512
舒张压 数值型变量 平均值=72.314; 方差=12.130
行政数据 住院天数 数值型变量 平均值=14.879; 方差=10.311
ICD_10编码 分类变量 I25.101(57.6%); I25.105(29.3%);其他(13.1%)
美托洛尔使用 分类变量 是 (1 197, 32.7%); 否 (2 463, 67.3%)
厄贝沙坦使用 分类变量 是(215, 5.9%); 否(3 445, 94.1%)
是否手术 分类变量 是 (201, 5.5%); 否 (3 459, 94.5%)
Description Statistics of Structured Data
Examples of Unstructured Data
变量 χ2值 Spearman相关系数 p值
性别 1.493 - 0.224
是否手术 34.905 - 0.000
ICD-10编码 81.145 - 0.000
婚姻状态 41.540 - 0.000
美托洛尔使用 88.998 - 0.000
厄贝沙坦使用 5.284 - 0.024
住院天数 - 0.391 0.000
收缩压 - -0.095 0.000
舒张压 - -0.163 0.000
Correlation Analysis of Independent Variable and Dependent Variable
Architecture of SUCM Model
参数名称 参数值
词嵌入维度(Word Embedding) 64
句子维度(Sentences Dimension) 300
卷积核个数(Number of Filter) 32
卷积核长度(Filter Length) 5
学习率(Learning Rate) 0.001
Parameter Settings of SUCM Model
模 型 ACC F1 P R AUC
结构化数据 NB 0.704 0.649 0.705 0.639 0.701
SVM 0.717 0.647 0.746 0.619 0.713
LR 0.711 0.650 0.730 0.633 0.708
MS(DL) 0.728 0.651 0.771 0.604 0.723
非结构化数据 NB 0.718 0.624 0.802 0.578 0.712
SVM 0.743 0.704 0.771 0.701 0.742
LR 0.731 0.687 0.762 0.685 0.729
MU(DL) 0.743 0.720 0.751 0.728 0.743
结构化数据+
非结构化数据		 NB 0.719 0.654 0.736 0.636 0.715
SVM 0.745 0.710 0.771 0.713 0.744
LR 0.734 0.687 0.760 0.689 0.732
SUCM(DL) 0.754 0.735 0.752 0.749 0.754
Experimental Results
Model Performance Improvement after Structured Data Combined with Unstructured Data
Model Performance Improvement after Unstructured Data Combined with Structured Data
激活函数 ACC F1 P R AUC
ReLU 0.754 0.735 0.752 0.749 0.754
ELU 0.750 0.728 0.767 0.724 0.749
Swish 0.748 0.727 0.752 0.736 0.747
Experimental Results with Different Activation Functions
全连接层数 ACC F1 P R AUC 运行时间/min
(3, 1) 0.750 0.732 0.760 0.738 0.750 1.3
(3, 2) 0.750 0.734 0.744 0.748 0.750 1.8
(4, 1) 0.754 0.735 0.752 0.749 0.754 1.2
(4, 2) 0.738 0.725 0.732 0.754 0.739 1.9
(5, 1) 0.741 0.725 0.739 0.740 0.741 1.4
(5, 2) 0.749 0.730 0.750 0.743 0.749 1.7
Experimental Results with Different Number of Fully Connected Layers
模 型 ACC F1 P R AUC 运行时间/min
MU(Char-CNN) 0.743 0.720 0.751 0.728 0.743 1.0
MU(Word2Vec) 0.728 0.715 0.720 0.732 0.728 2.3
SUCM(Char-CNN) 0.754 0.735 0.752 0.749 0.754 1.2
SUCM(Word2Vec) 0.729 0.713 0.733 0.718 0.729 4.3
Experimental Results with Different Text Processing Methods
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