## 3 Results
### Prediction Outcomes and Horizons  
A predictive algorithm can be used in many ways to anticipate depressive symptoms. Variation may lie in the type of outcome that is predicted or the prediction horizon. The prediction outcome may be a continuous score indicating symptom severity or a categorical outcome classifying by either depression diagnosis (yes or no) or clinical cut-off (above or below a clinical cut-off). With a smaller prediction horizon, a model may predict whether symptoms are being experienced in the present moment or whether symptoms have changed over recent weeks. With a larger prediction horizon, a model may predict whether depressive symptoms will change in the near future or even whether they will arise several weeks in advance. Each of these outcomes can be useful as long as the appropriate context is considered. The following will highlight some studies to exemplify the various options. Firstly, [PERSON] et al. [51] looked at a continuous outcome, aiming to predict daily HDRS-17 scores for a period of 8 weeks. Predicting daily mood scores this way may be useful for more detailed symptom tracking in between regular treatment visits. Conversely, [PERSON] et al. [52] used a binary outcome, as their model predicted whether the current PHQ-9 scores of patients with treatment-resistant depression (TRD) were above or below the clinical cut-off (PHQ-9 \(\geq\) 20) for severe depression. Such a binary outcome reduces nuance but makes interpretability more direct in the clinic. In fact, in clinical practice, the category is often sufficient without knowing an exact severity score. The authors note that this type of prediction for symptom monitoring is likely redundant for TRD patients due to their frequent in-person monitoring. However, with some adjustments, predicting on which side of the clinical cut-off patients fall can be useful for monitoring treatment progress, for example. [PERSON] et al. [48] investigated the binary outcome of whether symptoms changed over time. This was classified based on the difference between the highest and lowest of three consecutive PHQ-9 scores, measured at least one week apart. The distinction was made primarily between swinging mood (symptom change) and stable mood (no symptom change), and groups were further specified as moderate or drastic swing and stable remission or depression for a total of four outcome groups. A plethora of models were created that decided which of two groups an individual belonged in, each using different features and algorithm training methods. A major disadvantage of this method is that each model assumes a binary outcome (e.g., stable depression or moderate swing), thereby ignoring the possibility that the individual belongs to one of the other groups (e.g., stable remission). The highest accuracy was reached with models deciding between stable remission and moderate swing and between stable remission and drastic swing. These particular models may be useful for patients known to be in stable remission who wish to be alerted early of potential relapses. Exploring a slightly larger prediction horizon, [PERSON] et al. (2018) aimed to predict mood states and impending mood episodes in patients with pre-existing mood disorders (MDD, Bipolar disorder type I/II). The algorithm used 18 days' worth of data to predict mood state and occurrence of mood episodes over the following three days. The ground truth was determined using the absolute mood score (AMS) as self-reported in the eMoodChart app that was newly designed for this study. The ground truth distinction between stable (neutral) and biased (depressed, manic, hypomanic) was made using AMS percentiles rather than cut-off scores. For example, the best model considered the mood state of the 30% highest AMS to be biased and the lowest 70% to be stable. As such, the distinction relies on group distribution rather than true mood score. The occurrence of mood episodes was instead confirmed through in-person interviews, as is the clinical standard.