Deriving information from missing data: implications for mood prediction

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

Identifying psychiatric diagnosis from missing mood data through the use of log-signature features

The availability of mobile technologies has enabled the efficient collection of prospective longitudinal, ecologically valid self-reported clinical questionnaires from people with psychiatric diagnoses. 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 these should be dealt with in practice. In this study, the missing-response-incorporated log-signature method achieves roughly 74.8% correct diagnosis, with f1 scores for three diagnostic groups 66% (bipolar disorder), 83% (healthy control) and 75% (borderline personality disorder) respectively. This was superior to the naive model which excluded missing data and advanced models which implemented different imputation approaches, namely, k-nearest neighbours (KNN), probabilistic principal components analysis (PPCA) and random forest-based multiple imputation by chained equations (rfMICE). The log-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. Because of treating missing responses as a signal, its superiority also highlights that missing data conveys valuable clinical information

Have I argued with my family this week?": What questions do those with lived experience choose to monitor their bipolar disorder?

Background Electronic self-report mood monitoring tools for individuals with bipolar disorder (BD) are rapidly emerging and predominately employ predefined symptom-based questions. Allowing individuals to additionally choose what they monitor in relation to their BD offers the unique opportunity to capture and gain a deeper insight into patient priorities in this context. Methods In addition to monitoring mood symptoms with two standardised self-rated questionnaires, 308 individuals with BD participating in the Bipolar Disorder Research Network True Colours electronic mood-monitoring tool for research chose to create and complete additional personalised questions. A content analysis approach was used to analyse the content of these questions. Results 35 categories were created based on the personalised questions with the most common being physical activity and exercise, anxiety and panic, sleep and coping/stress levels. The categories were grouped into six overarching themes 1) mental health; 2) behaviour and level of functioning; 3) physical wellbeing; 4) health behaviours; 5) active self-management; and, 6) interpersonal. Limitations The average age of the sample was around 50 years meaning our findings may not be generalisable to younger individuals with BD. Conclusions Aspects of BD important to patients in relation to longitudinal monitoring extend well beyond mood symptoms, highlighting the limitations of solely relying on standardised questions/mood rating scales based on symptoms primarily used for diagnosis. Additional symptoms and aspects of life not necessarily useful diagnostically for BD may be more important for individuals themselves to monitor and have more meaning in capturing their own experience of changes in BD severity