Abstract: An unbiased ETL (extract, transform, load) system for timed medical event prediction utilizes a rolling series of time-bound cross-sections of patient healthcare data. Patients may be labelled as belonging to one or more classes (e.g. positive or negative) for each cross-section in the series depending on current healthcare status. Rather than using a single snapshot, the unbiased ETL system employs multiple snapshots of patient medical histories to provide a capability to classify a patient at different points in time, as appropriate. Supervised learning for the system is thereby enabled over multiple different periods of a patient's medical journey which advantageously supports a more statistically robust medical event prediction model and eliminates several classes of bias. Additionally, the unbiased ETL system enables customization of a prediction window to account for lags in data collection, data processing, and length of use of the medical event predictions.