Exploring and comparing machine learning approaches for predicting mood over time

Mental health related problems are responsible for great sorrow for patients and social surrounding involved. The costs for society are estimated to be 2.5 trillion dollar worldwide. More detailed data about the mental states and behaviour is becoming available due to technological developments, e.g. using Ecological Momentary Assessments. Unfortunately this wealth of data is not utilized: data-driven predictive models for short-term developments could contribute to more personalized interventions, but are rarely seen. In this paper we study how modern machine learning techniques can contribute to better models for predicting shortterm mood in the context of depression. The models are based on data obtained from an experiment among 27 participants. During the study frequent mood assessments were performed and usage and sensor data of the mobile phone was recorded. Results show that much can be improved before fine-grained mood prediction is useful within E-health applications. Subsequently important next steps are identified.