Deriving information from missing data: implications for mood prediction

Abstract

The availability of mobile technologies has enabled the efficient collection prospective longitudinal, ecologically valid self-reported mood data from psychiatric patients. These data streams have potential for improving the efficiency and accuracy of psychiatric diagnosis as well predicting future mood states enabling earlier intervention. However, missing responses are common in such datasets and there is little consensus as to how this should be dealt with in practice. A signature-based method was used to capture different elements of self-reported mood alongside missing data to both classify diagnostic group and predict future mood in patients with bipolar disorder, borderline personality disorder and healthy controls. The missing-response-incorporated signature-based method achieves roughly 66% correct diagnosis, with f1 scores for three different clinic groups 59% (bipolar disorder), 75% (healthy control) and 61% (borderline personality disorder) respectively. This was significantly more efficient than the naive model which excluded missing data. Accuracies of predicting subsequent mood states and scores were also improved by inclusion of missing responses. The signature method provided an effective approach to the analysis of prospectively collected mood data where missing data was common and should be considered as an approach in other similar datasets. 1

Sequential Path Signature Networks for Personalised Longitudinal Language Modeling

“…They have been used to embed sequential data to a continuous path and from there to form compressed features of different granularity for different downstream tasks. Path signatures have shown strong performance as feature extractors in various tasks such as online Chinese character recognition (Yang et al, 2016;Xie et al, 2017), psychiatric disorders distinction (Arribas et al, 2018), video action recognition (Yang et al, 2017), mood prediction with missing longitudinal data (Wu et al, 2020), healthcare (Morrill et al, 2020 and financial time series (Levin et al, 2013). Recent work has integrated signatures directly in neural models (Bonnier et al, 2019) allowing their operation as a layer of sequential pooling in neural networks.…”