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Identifying the relative importance of non-suicidal self-injury features in classifying suicidal ideation, plans, and behavior using exploratory data mining.
Psychiatry Res 2018; 262:175-183PR

Abstract

Individuals with a history of non-suicidal self-injury (NSSI) are at alarmingly high risk for suicidal ideation (SI), planning (SP), and attempts (SA). Given these findings, research has begun to evaluate the features of this multi-faceted behavior that may be most important to assess when quantifying risk for SI, SP, and SA. However, no studies have examined the wide range of NSSI characteristics simultaneously when determining which NSSI features are most salient to suicide risk. The current study utilized three exploratory data mining techniques (elastic net regression, decision trees, random forests) to address these gaps in the literature. Undergraduates with a history of NSSI (N = 359) were administered measures assessing demographic variables, depression, and 58 NSSI characteristics (e.g., methods, frequency, functions, locations, scarring) as well as current SI, current SP, and SA history. Results suggested that depressive symptoms and the anti-suicide function of NSSI were the most important features for predicting SI and SP. The most important features in predicting SA were the anti-suicide function of NSSI, NSSI-related medical treatment, and NSSI scarring. Overall, results suggest that NSSI functions, scarring, and medical lethality may be more important to assess than commonly regarded NSSI severity indices when ascertaining suicide risk.

Authors+Show Affiliations

Temple University, Department of Psychology, Philadelphia, PA, USA. Electronic address: taylor.burke@temple.edu.University of Notre Dame, Department of Psychology, Notre Dame, IN, USA.Temple University, Department of Psychology, Philadelphia, PA, USA.Washington University in St. Louis, Department of Psychology, St. Louis, MO, USA.Temple University, Department of Psychology, Philadelphia, PA, USA.Temple University, Department of Psychology, Philadelphia, PA, USA.Temple University, Department of Psychology, Philadelphia, PA, USA.

Pub Type(s)

Journal Article
Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.

Language

eng

PubMed ID

29453036

Citation

Burke, Taylor A., et al. "Identifying the Relative Importance of Non-suicidal Self-injury Features in Classifying Suicidal Ideation, Plans, and Behavior Using Exploratory Data Mining." Psychiatry Research, vol. 262, 2018, pp. 175-183.
Burke TA, Jacobucci R, Ammerman BA, et al. Identifying the relative importance of non-suicidal self-injury features in classifying suicidal ideation, plans, and behavior using exploratory data mining. Psychiatry Res. 2018;262:175-183.
Burke, T. A., Jacobucci, R., Ammerman, B. A., Piccirillo, M., McCloskey, M. S., Heimberg, R. G., & Alloy, L. B. (2018). Identifying the relative importance of non-suicidal self-injury features in classifying suicidal ideation, plans, and behavior using exploratory data mining. Psychiatry Research, 262, pp. 175-183. doi:10.1016/j.psychres.2018.01.045.
Burke TA, et al. Identifying the Relative Importance of Non-suicidal Self-injury Features in Classifying Suicidal Ideation, Plans, and Behavior Using Exploratory Data Mining. Psychiatry Res. 2018;262:175-183. PubMed PMID: 29453036.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Identifying the relative importance of non-suicidal self-injury features in classifying suicidal ideation, plans, and behavior using exploratory data mining. AU - Burke,Taylor A, AU - Jacobucci,Ross, AU - Ammerman,Brooke A, AU - Piccirillo,Marilyn, AU - McCloskey,Michael S, AU - Heimberg,Richard G, AU - Alloy,Lauren B, Y1 - 2018/01/31/ PY - 2017/06/08/received PY - 2017/11/29/revised PY - 2018/01/24/accepted PY - 2018/2/18/pubmed PY - 2018/12/12/medline PY - 2018/2/18/entrez KW - Decision trees KW - Elastic net regression KW - Exploratory data mining KW - Non-suicidal self-injury KW - Suicidal ideation KW - Suicide attempt KW - Suicide plan SP - 175 EP - 183 JF - Psychiatry research JO - Psychiatry Res VL - 262 N2 - Individuals with a history of non-suicidal self-injury (NSSI) are at alarmingly high risk for suicidal ideation (SI), planning (SP), and attempts (SA). Given these findings, research has begun to evaluate the features of this multi-faceted behavior that may be most important to assess when quantifying risk for SI, SP, and SA. However, no studies have examined the wide range of NSSI characteristics simultaneously when determining which NSSI features are most salient to suicide risk. The current study utilized three exploratory data mining techniques (elastic net regression, decision trees, random forests) to address these gaps in the literature. Undergraduates with a history of NSSI (N = 359) were administered measures assessing demographic variables, depression, and 58 NSSI characteristics (e.g., methods, frequency, functions, locations, scarring) as well as current SI, current SP, and SA history. Results suggested that depressive symptoms and the anti-suicide function of NSSI were the most important features for predicting SI and SP. The most important features in predicting SA were the anti-suicide function of NSSI, NSSI-related medical treatment, and NSSI scarring. Overall, results suggest that NSSI functions, scarring, and medical lethality may be more important to assess than commonly regarded NSSI severity indices when ascertaining suicide risk. SN - 1872-7123 UR - https://www.unboundmedicine.com/medline/citation/29453036/Identifying_the_relative_importance_of_non_suicidal_self_injury_features_in_classifying_suicidal_ideation_plans_and_behavior_using_exploratory_data_mining_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S0165-1781(17)31048-X DB - PRIME DP - Unbound Medicine ER -