A Machine Learning Predictive Model for Sepsis

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Introduction:Timely and accurately predicting the occurrence of sepsis and actively intervening in treatment may effectively improve the survival and cure rate of patients with sepsis. There have been a large number of research results on the prediction of sepsis produced by two methods: the sepsis ...

Introduction:Timely and accurately predicting the occurrence of sepsis and actively intervening in treatment may effectively improve the survival and cure rate of patients with sepsis. There have been a large number of research results on the prediction of sepsis produced by two methods: the sepsis detection and evaluation method based on clinical scoring mechanisms and the sepsis detection method based on machine learning model. Objective: Reasonable and effective data pre-processing can significantly improve the timeliness and accuracy of early warning models of sepsis. Given the problems of high time dispersion, uneven distribution, and large differences of common sepsis prediction modeling indicators, the study proposed a method of hybrid interpolation based on time window-related sepsis indicators. Methods:The study designed the traditional data interpolation method based on linear, MGP, average, nearest neighbour and the hybrid interpolation method (CTWH) based on correlation time window (CTW) proposed in the study for experimental comparison. Experiments were performed respectively in sample sets with no experimental data removal and sample sets with 90% missing values removal. By comparing with the performance of the existing sepsis indicator interpolation methods on the same baseline model, the effectiveness of the method was proven from the accuracy and timeliness of the prediction results. In the end, the results of the experimental method were analyzed and explained from a clinical perspective. Significance: In view of the characteristics of high dispersion, uneven distribution, and large differences between features of commonly used indicators in sepsis prediction models, this study proposed an efficient data interpolation strategy. After elimination of missing data of 90% and 0%, the interpolation method proposed in this study performed better than the existing methods like mean interpolation and linear interpolation, KNN, MGP on the static baseline and time series models. At the same time, this method also provided an idea to explore the length of the interpolation window, and supported the prospective study of missing value interpolation and data pre-processing.

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