北京生物醫(yī)學(xué)工程

基于時間序列相似性的患者結(jié)局預(yù)測模型

Predictive model for patient outcomes based on time series similarity

作者：王牧雨王妮周陽陳卉

單位：首都醫(yī)科大學(xué)生物醫(yī)學(xué)工程學(xué)院（北京  100069） 臨床生物力學(xué)應(yīng)用基礎(chǔ)研究北京市重點實驗室（北京 100069） 通信作者：陳卉。E-mail：[email protected]

關(guān)鍵詞：患者相似性；時間序列；K近鄰；MIMIC-Ⅲ；重癥監(jiān)護(hù)室

分類號：R318.04 

出版年·卷·期（頁碼）：2022·41·3（249-254）

摘要：

目的評估患者時間序列相似性，驗證融合時間序列相似性的K近鄰（K-nearest neighbor，KNN）模型是否可以有效提高患者結(jié)局預(yù)測的效果。方法整合Medical Information Mart for Intensive Care（MIMIC-III）數(shù)據(jù)庫中急性心肌梗死患者的人口學(xué)信息、藥物使用情況、疾病診斷、影像學(xué)報告、實驗室指標(biāo)以及手術(shù)操作數(shù)據(jù)，使用Jaccard系數(shù)、歐氏距離、編輯距離以及動態(tài)時間規(guī)整計算患者相似性。分別以入院基線數(shù)據(jù)和住院全程數(shù)據(jù)計算患者相似性，進(jìn)而對患者死亡、長時住院和長時重癥監(jiān)護(hù)（intensive care unit，ICU）進(jìn)行預(yù)測。使用接受者操作特征曲線下面積（area under curve，AUC）評估預(yù)測效果，與基于靜態(tài)數(shù)據(jù)的支持向量機（support vector machine，SVM）模型、基于時間序列的長短時記憶（long short-term memory，LSTM）模型進(jìn)行對比。結(jié)果輸入數(shù)據(jù)為住院全程數(shù)據(jù)時，KNN模型在死亡和長時住院預(yù)測中AUC值為0.877和0.946，高于SVM模型（0.825，0.930）和LSTM模型（0.853，0.928）；輸入數(shù)據(jù)為入院基線數(shù)據(jù)時，KNN模型在三個結(jié)局預(yù)測中AUC值為0.680、0.738、0.728，與SVM模型（0.719，0.715，0.708）相比各有高低。結(jié)論時間序列患者相似性與機器學(xué)習(xí)方法相結(jié)合可以有效提高信息利用率和模型的預(yù)測效果。

Objective To demonstrate the effectiveness and superiority of the proposed patient similarity measurement via building the K-nearest neighbor (KNN) for predicting patient outcomes in intensive care unit. Methods We firstly extracted the demographic information, drugs, disease diagnoses, radiological reports, laboratory tests and procedures of patients diagnosed with acute myocardial infarction from the medical information mart for intensive care (MIMIC-III) database. We then used the Jaccard distance, Euclidean distance, edit distance, and dynamic time warping algorithm to calculate patient similarity. The patient similarity was also measured using information at admission and during the whole hospitalization separately. The KNN model was built to predict patient mortality, prolonged length of hospital stay and intensive care unit（ICU） duration, whose performance was compared with the support vector machine (SVM) based on static data and the long short-term memory (LSTM) model based on time series. The area under receiver operating characteristic curve (AUC) was used to evaluate the predictive performance. Results When using all available information from admission to discharge, the KNN model outperformed the SVM and LSTM model for mortality and prolonged length of hospital stay prediction with the AUC of 0.877 and 0.946, separately. When using information at admission, the proposed KNN model also outperformed the SVM model when predicting prolonged length of hospital stay and ICU duration, but lost to the SVM in mortality prediction. Conclusions The proposed patient similarity measurement can effectively improve the information utilization and the performance of the similarity-based models for patient outcomes prediction.

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