DOI: 10.25934/pr00007811
title: Using Artificial Intelligence to Personalize Treatment Selection and Trajectory Monitoring of Bipolar Disorder

description: Bipolar disorder is a mood disorder characterized by extreme mood swings that cycle between emotional lows (depression) and highs (mania or hypomania). In bipolar disorder type 1, the mania is often severe with psychotic features and requires hospitalization. In bipolar disorder type 2, periods of elevated mood are milder and referred to as hypomania. An estimated 2.8% of the US population lives with bipolar disorder, with 82.9% of them having serious levels of impairment (figures from the National Institute of Mental Health [NIMH]). It is a serious and persistent mental illness (SPMI) that causes tremendous suffering to patients and their loved ones, with a 10-30 times greater risk of suicide in bipolar disorder as compared to general population. Bipolar disorder also poses a significant economic burden to healthcare systems. For example in the USA, the total cost burden is $202.1 billion, which breaks down to an estimated $81,559 per person.

Bipolar disorder is highly clinically complex and there is a dearth of strategies to effectively deal with failure of first line treatments. Only one third of bipolar patients will respond to initial treatment and the majority of patients must try several treatment options or combinations, spanning months or years before identifying one that works best for them. Even once and if the initial treatment selection is successful, there is an 80-90% chance of lifetime recurrence - 49% of these within a 2 year period, increasing the risk of prolonged functional impairment and suicide. Therefore, it is crucial to track the trajectory of the patient longitudinally to identify risk of relapse or switches towards manic or depressive phases, in order to intervene as quickly as possible. Improving our ability to select the most effective treatment for individual patients would mark a significant advancement in the treatment of bipolar disorder.

Deep learning is a process that can identify complex patterns in clinical trial data to embed knowledge of treatments within an artificial neural network (ANN). Our research aims to train an ANN with clinical trial data in order to find patterns in patient characteristics that respond best to a specific treatment and to identify how their condition will vary over time. We have previously demonstrated that this approach has been successful in generating differential treatment benefit prediction in unipolar major depression (please see Mehltretter et al., 2019; Mehltretter et al., 2020; Kleinerman et al., 2021 for more information). Now, we aim to apply this method to bipolar depression as similar datasets exist for this condition, and there are similar challenges in treatment selection and morbidity as in unipolar major depression. The aim of the present analysis will be to predict remission probability via a machine learning model of individual subject data, with a view to identifying predictors of individual patient response in bipolar depression. In addition to predicting remission probability for the current depressive episode, our project will seek to identify predictors and to generate predictive models of switch from bipolar depression to manic and hypomanic phases, or of a lack of sustained remission (i.e., return to a depressive state soon after remission is achieved). The ultimate aim of this research is to develop an evidence-based approach to personalizing treatments for bipolar disorder while monitoring the trajectory of the patient to assist in the prevention of relapse or recurrence.