Feature engineering
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REGRESSION
Only data up to the day of predicting CrCl were used as input to the models. The considered data included: 1) admission data: demographics, diagnosis, and comorbidities, 2) time-series data such as minute-by-minute monitoring data and daily or hourly laboratory results, 3) medication-related data, 4) time-related data: day of the week, and day from ICU admission. Data were retrieved from both the EPaNIC study database (Filemaker Pro®; FileMaker Inc, FileMaker International) and the patient data management system (PDMS) database (Microsoft SQL Server®; Microsoft®, Redmond, Washington, USA).The minimum, maximum, mean, standard deviation, linear regression slope, fast Fourier transform (FFT), cepstrum analysis, autoregressive analyses, and first-order derivative were applied to derive characteristics from the timestamped data. All the features with more than 10% missing values were excluded. For the remaining features, missing values were imputed with the mean and the mode from the development cohort for continuous data and categorical data, respectively. Finally, continuous data were standardized to zero mean and unit variance, and categorical data without order relation were converted into a form with binary data for each category.