Classifying Bipolar Personality Disorder (BPD) Using Long Short-Term Memory (LSTM)

With the advancement in technology, we are offered new opportunities for long-term monitoring of health conditions. There are a tremendous amount of opportunities in psychiatry where the diagnosis relies on the historical data of patients as well as the states of mood that increase the complexity of distinguishing between bipolar disorder and borderline disorder during diagnosis. This paper is inspired by prior work where the symptoms were treated as a time series phenomenon to classify disorders. This paper introduces a signature-based machine learning model to extract unique temporal pattern that can be attributed as a specific disorder. This model uses sequential nature of data as one of the key features to identify the disorder. The cases of borderline disorder that are either passed down genetically from parents or stem from exposure to intense stress and fear during childhood are discussed in this study. The model is tested with the synthetic signature dataset provided by the Alan Turing Institute in signature-psychiatry repository. The end result has 0.95 AUC which is an improvement over the last result of 0.90 AUC.