北京生物醫(yī)學工程

決策樹算法應用于MIMIC-III數(shù)據(jù)庫的ICU患者急性腎損傷預測研究

Decision tree algorithm applied to MIMIC-III database for the prediction of acute kidney injury in ICU patients

作者：高文鵬呂海金周瑯郭圣文

單位：華南理工大學材料科學與工程學院生物醫(yī)學工程系 (廣州 510006) 中山大學附屬第三醫(yī)院外科ICU (廣州510630)  華南理工大學自動化科學與工程學院(廣州510640) 通信作者:郭圣文。 E-mail: [email protected]

關(guān)鍵詞：急性腎損傷;重癥監(jiān)護室;機器學習;風險預測;重要特征

分類號：R318

出版年·卷·期（頁碼）：2021·40·6（609-617）

摘要：

目的急性腎損傷(acute kidney injury,AKI)是重癥監(jiān)護病房(intensive care unit,ICU)最常見的并發(fā)癥和致死因素之一。準確預測具AKI風險的患者，明確與AKI發(fā)生相關(guān)的關(guān)鍵因素，可為臨床決策與風險患者干預提供有效指導。方法采用公開的重癥監(jiān)護室數(shù)據(jù)庫MIMIC-III,提取30 020例患者記錄(包括AKI患者17 222名，Non-AKI患者12798名)，收集其住ICU期間基本信息、生理生化指標、藥物使用、合并癥等臨床信息。將患者按4 ： 1比例隨機劃分訓練集和獨立測試集，應用邏輯回歸、隨機森林與LightGBM 3種機器學習方法，分別建立24 h、48 h與72 h 3個時間點的AKI預測模型，采用十折交叉驗證法,對各種模型進行訓練與測試,預測患者是否發(fā)生AKI,并獲取重要特征。此外，利用24 h預測模型,在一周時間窗口內(nèi)對ICU患者進行每隔24h預測。結(jié)果3種學習模型中,LightGBM性能最優(yōu)，其24 h、48 h和72 h模型預測AKI的受試者工作特征曲線(receiver operator characteristic curve, ROC 曲線)下面積(area under curve, AUG)值分別為 0. 90.0. 88.0. 87,Fl 值分別為 0.91.0. 88,0. 86,在每隔24 h預測時，提前1 d、2 d和3 d預測AKI的成功率分別為89%、83%、80%。已住院時長、體質(zhì)量、白蛋白、收縮壓、碳酸氫鹽、葡萄糖、白細胞計數(shù)、體溫、舒張壓、血尿素氮等是預測ICU患者AKI的重要特征,僅使用24個重要特征，模型仍能取得良好的預測性能。結(jié)論基于ICU患者的基本信息、生理生化指標、藥物使用及合并癥等臨床信息，應用機器學習模型，可對其是否發(fā)生AKI進行多時間點的有效預測，并明確其關(guān)鍵風險因素。

Objective Acute kidney injury ( AKI) is one of the most common complications and fatal factors in intensive care unit (ICU). Accurate prediction of AKI risk and identification of key factors related to AKI can provide effective guidance for clinical decision-making and intervention for patients with AKI risk. Methods A total of 30020 patients in ICU (including 17 222 AKI patients and 12 798 Non-AKI patients) were selected from the public database MIMIC-III in this study, and basic information, physiological and biochemical indicators, drug use, and comorbidity during their stay in ICU were collected. All patients were randomly divided into training sets and independent testing sets according to the ratio of 4 ： 1, and logistic regression, random forest, and LightGBM were applied to construct models for AKI predication in three time points including 24 h, 48 h and 72 h, respectively. The 10-fold cross validation was used to train and validate various models to predict the occurrence of AKI,and obtain important features. Furthermore,24 h prediction models were used to predict AKI every 24 h during the 7-day window. Results LightGBM achieved the best performance with AUC values of 0. 90,0. 88,0. 87 for 24 h,48 h,and 72 h prediction,respectively,and Fl values were 0. 91,0. 88,and 0. 86. In prediction of every 24 h,the success rates of identifying AKI patients were 89% ,83%,and 80% in one day,two days and three days in advance, respectively. It was found that the length of stay in ICU, body weight, albumin, systolic blood pressure, bicarbonate, glucose, white blood cell count, body temperature, diastolic blood pressure and blood urea nitrogen played vital roles in predicting AKI for ICU patients. Using only 24 important features, the models could still achieve prominent prediction performance. Conclusions Based on basic information, physiological and biochemical indicators, drug use, and comorbidity, machine learning methods can be adopted to effectively predict AKI risk for ICU patients at several time points, and determine the dominant factors relative to AKI.

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