TY  - JOUR
PY  - 2021//
TI  - Uncovering social-contextual and individual mental health factors associated with violence via computational inference
JO  - Patterns (New York, N.Y.)
A1  - Santamaría-García, Hernando
A1  - Baez, Sandra
A1  - Aponte-Canencio, Diego Mauricio
A1  - Pasciarello, Guido Orlando
A1  - Donnelly-Kehoe, Patricio Andrés
A1  - Maggiotti, Gabriel
A1  - Matallana, Diana
A1  - Hesse, Eugenia
A1  - Neely, Alejandra
A1  - Zapata, José Gabriel
A1  - Chiong, Winston
A1  - Levy, Jonathan
A1  - Decety, Jean
A1  - Ibáñez, Agustín
SP  - e100176
EP  - e100176
VL  - 2
IS  - 2
N2  - The identification of human violence determinants has sparked multiple questions from different academic fields. Innovative methodological assessments of the weight and interaction of multiple determinants are still required. Here, we examine multiple features potentially associated with confessed acts of violence in ex-members of illegal armed groups in Colombia (N = 26,349) through deep learning and feature-derived machine learning. We assessed 162 social-contextual and individual mental health potential predictors of historical data regarding consequentialist, appetitive, retaliative, and reactive domains of violence. Deep learning yields high accuracy using the full set of determinants. Progressive feature elimination revealed that contextual factors were more important than individual factors. Combined social network adversities, membership identification, and normalization of violence were among the more accurate social-contextual factors. To a lesser extent the best individual factors were personality traits (borderline, paranoid, and antisocial) and psychiatric symptoms. The results provide a population-based computational classification regarding historical assessments of violence in vulnerable populations.<p />  <p>Language: en</p>
LA  - en
SN  - 2666-3899
UR  - http://dx.doi.org/10.1016/j.patter.2020.100176
ID  - ref1
ER  -