Detecting Bipolar Disorder from Misdiagnosed Major Depressive Disorder with Mood-Aware Multi-Task Learning (2024)

Abstract: Bipolar Disorder (BD) is a mental disorder characterized by intense mood swings, from depression to manic states. Individuals with BD are at a higher risk of suicide, but BD is often misdiagnosed as Major Depressive Disorder (MDD) due to shared symptoms, resulting in delays in appropriate treatment and increased suicide risk. While early intervention based on social media data has been explored to uncover latent BD risk, little attention has been paid to detecting BD from those misdiagnosed as MDD. Therefore, this study presents a novel approach for identifying BD risk in individuals initially misdiagnosed with MDD. A unique dataset, BD-Risk, is introduced, incorporating mental disorder types and BD mood levels verified by two clinical experts. The proposed multi-task learning for predicting BD risk and BD mood level outperforms the state-of-the-art baselines. Also, the proposed dynamic mood-aware attention can provide insights into the impact of BD mood on future risk, potentially aiding interventions for at-risk individuals.

## introduction bipolar disorder (bd) is a mental disorder characterized by recurring intense mood swings, ranging from depression to manic. unfortunately, individuals with bd are at a higher risk of suicide than the general population @xcite and those with other psychiatric disorders @xcite . hence, accurate and early detection of individuals suffering from bd is vital to ensure timely suicide prevention. according to previous studies, 17% to 50% of bd is misdiagnosed as major depressive disorder (mdd) @xcite @xcite , characterized by persistent sadness and a loss of interest (american psychiatric @xcite . while depressive episodes in bd are similar to those in mdd, critical distinction arises from manic episodes, which include an elevated mood, impulsivity, and racing thoughts @xcite . for instance, individuals with mdd typically exhibit stable low mood variability, whereas those with bd have unstable mood swings due to recurrent mood episodes @xcite . nevertheless, individuals with bd often tend not to perceive manic episodes as abnormal, and some could even find these states preferable @xcite , leading them to recognize themselves as mdd. also, more than 58% of bd patients undergo a depressive episode before experiencing a manic episode and predominantly seek help during the depressive phase @xcite . since bd and mdd require distinct treatments and prognoses, misdiagnosis can lead to significant delays in receiving proper treatment and can also increase the risk of suicide @xcite . therefore, precise identification of patients who may develop bd after an initial diagnosis of mdd is of great clinical importance @xcite . importantly, due to a huge gap in understanding prognosis trajectories between real-world scenarios and clinical settings, where clinicians' interaction with patients is limited and relies on patients' subjective accounts @xcite , there is an emerging interest in utilizing real-world data from non-clinical contexts like social media to gain insights into distinct behavioral traits that can be linked to bd and mdd @xcite . however, while several studies have suggested methods for analyzing and categorizing mental states, including bd and mdd, using social media data @xcite @xcite , little attention has been paid to detecting a shift in diagnosis from mdd to bd that can be crucial in identifying individuals who have developed bd after an initial diagnosis of mdd. instead of a one-time diagnosis in a clinical setting, a behavior trait of an individual who develops bd after a diagnosis of mdd can be comprehensively captured and analyzed using longitudinal social media data that includes dynamic mood variation across time. therefore, this study aims to detect bd risk among patients incorrectly diagnosed with mdd or wrongly perceived as mdd, using their historical mood fluctuations revealed in their past social media activity. to this end, we introduce a novel dataset, bd-risk, that includes (i) the types of mental disorders (e.g., bd or mdd) and (ii) bd mood level on a scale from -3 to 3, which are verified by clinical experts. our data analysis indicates a strong correlation between mood variation and the bd risk of users, which is in line with the dsm-5 @xcite , which emphasizes the importance of evaluating not only the absolute mood level but also the relative mood variation to monitor the risk of bd. thus, a multi-task learning model is proposed in which three tasks are simultaneously learned: (i) predicting the bd risk for users initially diagnosed with mdd, (ii) estimating bd mood level, and (iii) assessing mood variation from their posts over time. our extensive experiments demonstrate that the proposed model outperforms all the baseline models in bd risk prediction. we find that jointly learning bd mood information can improve performance by transferring informative representations and parameters among the tasks. additionally, the attention scores provided by the proposed dynamic mood-aware attention method can provide valuable insights into comprehending the influence of bd mood on a user's future bd risk. this capability has the potential to assist clinicians in deepening their comprehension of the connections between psychiatric conditions, thereby enabling appropriate interventions, including the management of individuals undergoing depressive episodes @xcite . we summarize the contributions of this work as follows. ## bd-risk dataset identifying individuals who may develop bd after being initially diagnosed with mdd is essential due to the potential risks of misdiagnosis, such as delayed treatment and increased suicide risk @xcite . therefore, we aim to detect early signs of bd among people misdiagnosed with mdd. to this end, we build a dataset, bd-risk, that includes the following labels: (i) specific mental disorders (e.g., bd or mdd) and (ii) the bd mood level scaling from -3 to 3. in this section, we provide a data collection approach, annotation strategy, and statistics of annotation evaluation. ## the model this section describes the problem definition and our proposed multi-task learning model that can (i) detect the bd risk for users initially diagnosed with mdd, (ii) estimate bd mood level, and (iii) assess mood variation from their posts over time. the model includes three main components: a temporal convolution layer, a dynamic mood-aware attention layer, and a prediction layer. the overall architecture is illustrated in figure 2 . ## concluding remarks our research presented a multi-task learning model for bd risk detection among users misdiagnosed as mdd, leveraging a clinically validated novel dataset, bd-risk. we demonstrated that jointly learning bd mood information can enhance the performance of bd risk detection. the dynamic mood-aware attention scores offer insights into bd mood's impact on future risk, potentially aiding clinicians in interventions for those who are underrepresented in a clinical setting, such as minorities or patients with a lack of insight.