Abigail Ortiz1, Kamil Bradler2, Arend Hintze3,4,5. 1. Department of Psychiatry, University of Ottawa, Ottawa, ON, Canada. 2. Department of Mathematics and Statistics, University of Ottawa, Ottawa, ON, Canada. 3. Department of Integrative Biology, Michigan State University, East Lansing, MI, USA. 4. Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA. 5. BEACON Center for studying Evolution in Action, Michigan State University, East Lansing, MI, USA.
Abstract
OBJECTIVE: Bipolar disorder is a severe mood disorder characterized by alternating episodes of mania and depression. Several interventions have been developed to decrease high admission rates and high suicides rates associated with the illness, including psychoeducation and early episode detection, with mixed results. More recently, machine learning approaches have been used to aid clinical diagnosis or to detect a particular clinical state; however, contradictory results arise from confusion around which of the several automatically generated data are the most contributory and useful to detect a particular clinical state. Our aim for this study was to apply machine learning techniques and nonlinear analyses to a physiological time series dataset in order to find the best predictor for forecasting episodes in mood disorders. METHODS: We employed three different techniques: entropy calculations and two different machine learning approaches (genetic programming and Markov Brains as classifiers) to determine whether mood, energy or sleep was the best predictor to forecast a mood episode in a physiological time series. RESULTS: Evening energy was the best predictor for both manic and depressive episodes in each of the three aforementioned techniques. This suggests that energy might be a better predictor than mood for forecasting mood episodes in bipolar disorder and that these particular machine learning approaches are valuable tools to be used clinically. CONCLUSIONS: Energy should be considered as an important factor for episode prediction. Machine learning approaches provide better tools to forecast episodes and to increase our understanding of the processes that underlie mood regulation.
OBJECTIVE:Bipolar disorder is a severe mood disorder characterized by alternating episodes of mania and depression. Several interventions have been developed to decrease high admission rates and high suicides rates associated with the illness, including psychoeducation and early episode detection, with mixed results. More recently, machine learning approaches have been used to aid clinical diagnosis or to detect a particular clinical state; however, contradictory results arise from confusion around which of the several automatically generated data are the most contributory and useful to detect a particular clinical state. Our aim for this study was to apply machine learning techniques and nonlinear analyses to a physiological time series dataset in order to find the best predictor for forecasting episodes in mood disorders. METHODS: We employed three different techniques: entropy calculations and two different machine learning approaches (genetic programming and Markov Brains as classifiers) to determine whether mood, energy or sleep was the best predictor to forecast a mood episode in a physiological time series. RESULTS: Evening energy was the best predictor for both manic and depressive episodes in each of the three aforementioned techniques. This suggests that energy might be a better predictor than mood for forecasting mood episodes in bipolar disorder and that these particular machine learning approaches are valuable tools to be used clinically. CONCLUSIONS: Energy should be considered as an important factor for episode prediction. Machine learning approaches provide better tools to forecast episodes and to increase our understanding of the processes that underlie mood regulation.
Authors: Elisabeth A Frazier; Jeffrey I Hunt; Heather Hower; Richard N Jones; Boris Birmaher; Michael Strober; Benjamin I Goldstein; Martin B Keller; Tina R Goldstein; Lauren M Weinstock; Daniel P Dickstein; Rasim S Diler; Neal D Ryan; Mary Kay Gill; David Axelson; Shirley Yen Journal: J Affect Disord Date: 2020-04-18 Impact factor: 4.839