The Proposition for Bipolar Depression Forecasting Based on Wearable Data Collection - Llamocca, Pavel; López, Victoria; Čukić, Milena (Frontiers in Physiology, 2022)
Bipolar depression is treated wrongly as unipolar depression, on average, for eight years. It is shown that this mismedication affects the occurrence of a manic episode and aggravates the overall condition of bipolar depression patients. Significant effort was invested in the early detection of depression and forecasting of responses to certain therapeutic approaches using a combination of features extracted from standard and online testing, wearables monitoring, and machine learning. In the case of unipolar depression, this approach yielded evidence that this data-based computational psychiatry approach would be helpful in clinical practice. Following a similar pipeline, we examined the usefulness of this approach to foresee a manic episode in bipolar depression, so that clinicians and the patient’s family can help the patient navigate through the time of crisis. Our projects combined the results from self-reported daily questionnaires, the data obtained from smartwatches, and the data from regular reports from standard psychiatric interviews to feed various machine learning models to predict a crisis in bipolar depression. Contrary to satisfactory predictions in unipolar depression, we found that bipolar depression, having more complex dynamics, requires a personalized approach. Older work on physiological complexity suggests that inclusion of electrophysiological data, properly quantified, might lead to better solutions, as shown in other projects of our group concerning unipolar depression. Here we make a comparison of previously performed research in a methodological sense, revisiting and additionally interpreting our own results aiming to provide an accurate prediction in bipolar depression.