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基于XGBoost和SHAP的急性肾损伤可解释预测模型

罗妍 王枞 叶文玲

罗妍, 王枞, 叶文玲. 基于XGBoost和SHAP的急性肾损伤可解释预测模型[J]. 电子与信息学报, 2022, 44(1): 27-38. doi: 10.11999/JEIT210931
引用本文: 罗妍, 王枞, 叶文玲. 基于XGBoost和SHAP的急性肾损伤可解释预测模型[J]. 电子与信息学报, 2022, 44(1): 27-38. doi: 10.11999/JEIT210931
LUO Yan, WANG Cong, YE Wenling. An Interpretable Prediction Model for Acute Kidney Injury Based on XGBoost and SHAP[J]. Journal of Electronics & Information Technology, 2022, 44(1): 27-38. doi: 10.11999/JEIT210931
Citation: LUO Yan, WANG Cong, YE Wenling. An Interpretable Prediction Model for Acute Kidney Injury Based on XGBoost and SHAP[J]. Journal of Electronics & Information Technology, 2022, 44(1): 27-38. doi: 10.11999/JEIT210931

基于XGBoost和SHAP的急性肾损伤可解释预测模型

doi: 10.11999/JEIT210931
详细信息
    作者简介:

    罗妍:女,1988年生,博士生,研究方向为智能信息处理、医疗数据挖掘与分析

    王枞:女,1958年生,教授,研究方向为智能信息处理、医疗数据挖掘与分析

    叶文玲:女,1969年生,主任医师,研究方向为肾脏疾病的诊断和治疗、医疗数据处理

    通讯作者:

    叶文玲 wenlyepumch@163.com

  • 中图分类号: TP391

An Interpretable Prediction Model for Acute Kidney Injury Based on XGBoost and SHAP

  • 摘要: 重症监护病房(ICU)住院期间发生的急性肾损伤(AKI)与患者发病率和死亡率的增加有关。该研究的目的是提出一个基于机器学习的框架,用于危重病患者的可解释AKI预测,该框架能够同时实现良好的预测和解释能力。该文从重症监护医学信息数据库Ⅲ(MIMIC-III)中提取的数据包括患者的年龄、性别、生命体征和ICU入院第1天及随后的化验值。在该研究中,通过将XGBoost模型与其他4种机器学习模型进行比较,证明了XGBoost模型的预测性能。此外,SHAP(SHapley Additive exPlanation)模型可解释器用于提供个性化评估和解释,以实现个性化的临床决策支持。结果表明,XGBoost能较好地预测AKI,与以往的预测模型相比,此模型更为简单有效,仅用21个特征变量即得到了更稳定的预测结果,预测精度高,模型准确率和受试者工作特征曲线下面积(AUC)分别为0.824和0.840,均高于既往研究结果。此外,该文对两组指标进行了特征依赖分析,发现24h尿量减少和血尿素氮升高可增加AKI风险。综上所述,该可解释预测模型可能有助于临床医生更准确快速地识别重症监护中存在AKI风险的患者,为患者提供更好的治疗。此外,可解释性框架的使用增加了模型透明度,便于临床医生分析预测模型的可靠性。
  • 图  1  数据提取时间间隔示意图(AKI的确诊时间窗为ICU入院24 h后)

    图  2  XGBoost模型5折交叉验证的AUC值结果

    图  3  由测试集计算得到的各模型AUC结果

    图  4  预测模型中前20个变量的排名

    图  5  平均排名前20的特征变量SHAP摘要图

    图  6  重要指标的SHAP特征依赖图示例

    表  1  既往研究结果表明ML技术可以有效地用于AKI预测

    研究数据集记录ML 算法病人
    AUC
    Mohamadlou 等人[30]MIMIC-III; Stanford University data set48582
    19737
    Gradient BoostingICU patients/ inpatients0.73–0.80
    Lei等人[31]University of Pennsylvania Health System42615Gradient Boostingnoncardiac surgery0.82
    Koyner等人[32]University of Chicago (an urban tertiary
    referral hospital)
    121158Gradient Boostingadult admissions0.73
    Churpek等人[33]3 health systems495971
    Gradient Boostingadult admissions0.67-0.72
    Simonov等人[34]3 hospitals169859logistic regressionadult admissions0.74
    Xu等人[35]MIMIC-III8181Gradient Boosting Decision TreeICU patients0.73
    Zimmerman等人[36]MIMIC-III23950Logistic Regressionadult critical care patients0.78
    Rashidi等人[37]UC Davis clinical laboratory101Recursive Neural Networkburn and trauma patients0.92
    下载: 导出CSV

    表  2  各变量在基础数据集中的分布情况(%)

    变量正常对照组疾病组
    Age,years63.41±16.2566.54±14.77
    GenderFemale6,264(43.29%)1,846(40.73%)
    Male8,205(56.71%)2,686(59.27%)
    Median, Interquartile Range (IQR; 25%–75%)Median, Interquartile Range (IQR; 25%–75%)
    GCS14.00 (8.00–15.00)10.00 (3.00–15.00)
    Scr_baseline, mg/dL0.90 (0.70–1.10)1.40 (0.90–2.40)
    MAP, mmHg77.29 (70.39–84.90)74.54 (68.21–81.96)
    HR, beats/min84.39 (74.06–95.84)85.38 (76.44–97.98)
    T, °C37.10 (36.75–37.46)37.07 (36.65–37.41)
    R, /min18.35 (16.09–21.16)18.62 (16.02–21.93)
    PO2, mmHg124.67 (93.00–159.00)124.17 (97.50–152.18)
    A-aDO2, mmHg439.00 (351.00–532.00)475.50 (381.00–556.75)
    HCT, %30.00 (27.00–34.00)28.50 (25.15–32.00)
    WBC, ×109/L10.60 (7.80–14.00)11.62 (8.22–15.90)
    BUN, mg/dL17.00 (12.00–26.00)27.50 (17.67–44.67)
    Na, mmol/L138.67 (136.00–141.00)138.00 (135.88–140.50)
    K, mmol/L4.03 (3.75–4.37)4.27 (3.93–4.65)
    ALB, mg/dL3.10 (2.60–3.60)2.80 (2.40–3.20)
    BIL, mg/dL0.70 (0.40–1.40)1.00 (0.50–3.11)
    pH7.39 (7.35–7.43)7.36 (7.32–7.40)
    Glu, mg/dL127.00 (107.58–153.00)132.21 (114.00–161.00)
    FU, ml/h105.88 (70.42–156.76)67.00 (34.49–108.39)
    TVU, ml/24 h1,810.00 (1,194.00–2,663.00)1,148.00 (549.75–1,995.00)
    缩略词: GCS, 格拉斯哥昏迷评分; R, 呼吸率; PO2, 动脉血氧分压; WBC, 白细胞; BUN, 血尿素氮;Na, 血钠; K, 血钾; pH, 酸碱度; Glu, 血糖; TVU, 24 h总尿量
    下载: 导出CSV

    表  3  5种AKI风险预测模型的性能比较

    模型准确率[95% Cl]精确率[95% Cl]敏感度[95% Cl]F1[95% Cl]
    SVM0.793[0.789, 0.798]0.781[0.772, 0.789]0.793[0.789, 0.798]0.747[0.741, 0.752]
    LR0.788[0.785, 0.793]0.765[0.759, 0.771]0.789[0.785, 0.793]0.762[0.758, 0.766]
    RF0.824[0.813, 0.835]0.809[0.803, 0.816]0.822[0.817, 0.827]0.807[0.796,0.818]
    ANN0.806[0.799,0.815]0.808[0.797, 0.819]0.821[0.812,0.829]0.806[0.797, 0.814]
    XGBoost0.824[0.815, 0.832]0.812[0.802, 0.822]0.824[0.815, 0.832]0.813[0.804, 0.822]
    下载: 导出CSV

    表  4  MIMIC-III数据集上的最新研究(仅用于AKI预测)不同模型性能的比较

    研究记录ML 算法条件AUC
    Mohamadlou等人[30]48582Gradient BoostingICU patients0.728–0.761
    Zimmerman等人[36]23950Logistic RegressionICU patients0.78
    Li等人[57]16560
    Knowledge-guided CNNICU patients0.779
    Shawwa 等人[62]18529Gradient BoostingICU patients0.656
    Sun 等人[63]16558Mixed-feature CNNICU patients0.83
    本文方法19001XGBoostICU patients0.839
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-09-02
  • 修回日期:  2021-10-19
  • 网络出版日期:  2021-10-25
  • 刊出日期:  2022-01-10

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