12th International Conference on Uncertainty Modelling in Knowledge Engineering and Decision Making - FLINS 2016, Roubaix (France). 24-26 August 2016
Summary:
Bipolar disorder often leads to periods of sick leave and close attention, thus causing economic and social problems in work and family environments. Most patients suffer crises that can be avoided through early prediction. Common characteristics, identified as patterns of behavior, add valuable information to the study. This paper shows a specification of a prediction system based on data from diverse sources. The dynamic analysis of data from voice monitoring with accelerometers are an adequate complement to the data used in previous studies. For predicting the crisis of bipolar disorder, a combination of machine learning algorithms is proposed into a computer-aided diagnosis (CAD) system.
DOI: 
https://doi.org/10.1142/9789813146976_0028
Published in Uncertainty modelling in knowledge engineering and decision making, pp: 162-167, ISBN: 978-981-3146-96-9
Publication date: 2016-09-01.
Citation:
V. López López, G. Valverde, J.C. Anchiraico Trujillo, D. Urgeles, Specification of a CAD prediction system for bipolar disorder, 12th International Conference on Uncertainty Modelling in Knowledge Engineering and Decision Making - FLINS 2016, Roubaix (France). 24-26 August 2016. In: Uncertainty modelling in knowledge engineering and decision making: Proceedings of FLINS 2016, ISBN: 978-981-3146-96-9
