Entropy-Based Time Window Features Extraction for Machine Learning to Predict Acute Kidney Injury in ICU
نویسندگان
چکیده
Acute kidney injury (AKI) refers to rapid decline of function and is manifested by decreasing urine output or abnormal blood test (elevated serum creatinine). Electronic health records (EHRs) fundamental for clinicians machine learning algorithms predict the clinical outcome patients in Intensive Care Unit (ICU). Early prediction AKI could automatically warn review possible risk factors act advance prevent it. However, enormous amount patient data usually consists a relatively incomplete set very challenging supervised process. In this paper, we propose an entropy-based feature engineering framework vital signs based on their frequency records. particular, address missing at random (MAR) not (MNAR) types according different scenarios. Regarding its applicability, applied it establish model future ICU using 4278 admissions from tertiary hospital. Our result shows that proposed features are feasible be used performance improves as availability increases. addition, study comparing time gaps windows with sign entropy features. This work guidance selection processing during development ICU.
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ژورنال
عنوان ژورنال: Applied sciences
سال: 2021
ISSN: ['2076-3417']
DOI: https://doi.org/10.3390/app11146364