Fig. 5: DNNs for individualized (treatment) predictions.

In order to employ more complex FNN or RNN models for person-tailored predictions, we can pre-train a NN on multiple individuals. We first reduce the input dimensions, e.g., with an autoencoder (step 1), and then pre-train a DNN on the reduced inputs for a large sample (step 2). The pre-trained network may then be fine-tuned on the specific individual in a third step (right panel). Future data points could then be used to forecast symptom onset, treatment response, or other mental health-related variables.