Table 1.
Key characteristics of the employed outlier detection methods
| Method | Key characteristics | |||
|---|---|---|---|---|
| Type of detection method | Types of outliers | Input parameters | Advantages | |
| Static BIV (sBIV) | Standardized | Measurements | Fixed cut-offs | Simple |
| Modified BIV (mBIV) | Empirical | Measurements | Fixed cut-offs | Time consensus, simple |
| Single-model outlier measurement detection (SMOM) | Statistical based | Measurements | Semi-dynamica, based on the dataset | Population adjusted |
| Multi-model outlier measurement detection (MMOM) | Statistical and clustering based | Measurements | Semi-dynamic, based on the dataset | Group-adjusted |
| Clustering-based outlier trajectory (COT) | Clustering based | Trajectory | Dynamic, based on data size | Population adjusted |
| Multi-model outlier trajectory (MMOT) | Statistical and clustering based | Trajectory | Semi-dynamic, based on the dataset | Group-adjusted |
aCombination of fixed thresholds (i.e. WHO) and dynamic thresholds or values derived from the dataset (including averages, number of clusters and so on)