  Mobile sensing-based modeling of behavioral changes could predict an oncoming
psychotic relapse in schizophrenia patients for timely interventions. Deep
learning models could complement existing non-deep learning models for relapse
prediction by modeling latent behavioral features relevant to the prediction.
However, given the inter-individual behavioral differences, model
personalization might be required for a predictive model. In this work, we
propose RelapsePredNet, a Long Short-Term Memory (LSTM) neural network-based
model for relapse prediction. The model is personalized for a particular
patient by training using data from patients most similar to the given patient.
Several demographics and baseline mental health scores were considered as
personalization metrics to define patient similarity. We investigated the
effect of personalization on training dataset characteristics, learned
embeddings, and relapse prediction performance. We compared RelapsePredNet with
a deep learning-based anomaly detection model for relapse prediction. Further,
we investigated if RelapsePredNet could complement ClusterRFModel (a random
forest model leveraging clustering and template features proposed in prior
work) in a fusion model, by identifying latent behavioral features relevant for
relapse prediction. The CrossCheck dataset consisting of continuous mobile
sensing data obtained from 63 schizophrenia patients, each monitored for up to
a year, was used for our evaluations. The proposed RelapsePredNet outperformed
the deep learning-based anomaly detection model for relapse prediction. The F2
score for prediction were 0.21 and 0.52 in the full test set and the Relapse
Test Set (consisting of data from patients who have had relapse only),
respectively. These corresponded to a 29.4% and 38.8% improvement compared to
the existing deep learning-based model for relapse prediction.
