TY - CHAP

T1 - Social network activities as a predictor for phase transitions of patients with bipolar affective disorders

T2 - 33rd Sunbelt Conference of the International Network for Social Network Analysis - INSNA 2013

AU - Barbian, Guido

AU - Maier, Andreas

N1 - Conference code: 33

PY - 2013

Y1 - 2013

N2 - This study applies social network analysis to predict phase transitions of patients with bipolar affective disorder (manic depressive).During a manic period, which includes a high degree of social activities, some patients tend to drop medication. When mood swings from manic to depressive these patients are endangered committing suicide. Timely prediction of mood swings and adjustment of medication could potentially safe patient's lifes. We analyse phase-specific patterns of social network activities (communication, in-person-meetings, moving to specific locations, social network dynamics) and derive phase transition predictors (early warners). For data collection by mobile smartphones we developed SIMBA, a social activity monitoring system, which is based on MIT's funf sensing framework (Aharony et al. 2011). The resulting overlapping networks based on communication, personal contact and location (two-mode) are analysed and SNA methods are used for clustering and evaluation. Predictors are derived by a Gaussian mixture model.An expectation-maximization algorithm is used for parameter estimation. The resulting phase transition predictors are used as signals for attending physicians and psychiatrists.

AB - This study applies social network analysis to predict phase transitions of patients with bipolar affective disorder (manic depressive).During a manic period, which includes a high degree of social activities, some patients tend to drop medication. When mood swings from manic to depressive these patients are endangered committing suicide. Timely prediction of mood swings and adjustment of medication could potentially safe patient's lifes. We analyse phase-specific patterns of social network activities (communication, in-person-meetings, moving to specific locations, social network dynamics) and derive phase transition predictors (early warners). For data collection by mobile smartphones we developed SIMBA, a social activity monitoring system, which is based on MIT's funf sensing framework (Aharony et al. 2011). The resulting overlapping networks based on communication, personal contact and location (two-mode) are analysed and SNA methods are used for clustering and evaluation. Predictors are derived by a Gaussian mixture model.An expectation-maximization algorithm is used for parameter estimation. The resulting phase transition predictors are used as signals for attending physicians and psychiatrists.

KW - Informatics

KW - Sociology

M3 - Published abstract in conference proceedings

SP - 111

BT - Sunbelt XXXIII

A2 - INSNA,

Y2 - 21 May 2013 through 26 May 2013

ER -