_Applied_  
_sciences_  
_Article_  
Entropy-Based Time Window Features Extraction for Machine Learning to Predict Acute Kidney Injury in ICU  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]  
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Footnote 7: https://www. and high medical cost [2]. Early prediction of AKI could help clinicians to provide timely intervention and probably avoid the grave prognosis of end-stage renal disease. With the growth of computing power, artificial intelligence has been applied extensively in various fields from fingerprint recognition to arrhythmia prediction [3; 4; 5]. The clear and objective definition of AKI provide an ideal labelling outcome for machine learning. [PERSON] et al. [6] developed a deep learning approach for continuous prediction of future AKI using more than 700,000 patients Electronic Health Records (EHRs) from a multi-site dataset of US Department of Veterans Affairs. Based on their prediction model, clinicians could be warned 48 h in advance before AKI occurrence. However, the prediction algorithm created by [PERSON] et al. consists of more than 100 features and may not be easily applied to other hospitals where many features might have high proportion of missing data. Entropy, also known as Shannon's Entropy or self-information of an event, denotes the uncertainty and contributed information about the state of the system or data [7]. Entropy can be interpreted as a measure of the probability distribution for the amount of missing information [8]. The occurrence of the random event has higher information contribution than the common event observed. Entropy has been applied in numerous research fields, including medical, biometrics, and other real-world applications. [PERSON] et al. [9] applied entropy analysis to Electroencephalography (EEG) data to examine its performance on epilepsy detection. [PERSON] et al. [10] used fuzzy entropy and sample entropy as predictors for Out-of-hospital cardiac arrest (OHCA). Their study showed that entropy is a reliable predictor and outperformed other predictors. [PERSON] et al. [11] concluded that multi-scale entropy could potentially be an early predictor of stroke-in-evolution in ICU-admitted non-atrial fibrillation stroke patients. In this study, we use Random Forest (RF) machine learning algorithm to build the AKI prediction model [12]. RF has become one of the major machine learning methods, due to its robustness and non-linear ensemble nature and has been used in ICU early warning system [13; 14]. RF algorithm can use cross-entropy and entropy-as-loss-functions to find the best split in a classification tree. However, directly applying it without considering the potential impact of missing data may result in poor model performance or bias.