Problematic internet use (PIU): Associations with the impulsive-compulsive spectrum. An application of machine learning in psychiatry. Ioannidis, K., Chamberlain, S., R., Treder, M., S., Kiraly, F., Leppink, E., W., Redden, S., A., Stein, D., J., Lochner, C., & Grant, J., E. Journal of Psychiatric Research, 83:94-102, 2016.
Problematic internet use (PIU): Associations with the impulsive-compulsive spectrum. An application of machine learning in psychiatry [link]Website  abstract   bibtex   
Problematic internet use is common, functionally impairing, and in need of further study. Its relationship with obsessive-compulsive and impulsive disorders is unclear. Our objective was to evaluate whether problematic internet use can be predicted from recognised forms of impulsive and compulsive traits and symptomatology. We recruited volunteers aged 18 and older using media advertisements at two sites (Chicago USA, and Stellenbosch, South Africa) to complete an extensive online survey. State-of-the-art out-of-sample evaluation of machine learning predictive models was used, which included Logistic Regression, Random Forests and Naïve Bayes. Problematic internet use was identified using the Internet Addiction Test (IAT). 2006 complete cases were analysed, of whom 181 (9.0%) had moderate/severe problematic internet use. Using Logistic Regression and Naïve Bayes we produced a classification prediction with a receiver operating characteristic area under the curve (ROC-AUC) of 0.83 (SD 0.03) whereas using a Random Forests algorithm the prediction ROC-AUC was 0.84 (SD 0.03) [all three models superior to baseline models p < 0.0001]. The models showed robust transfer between the study sites in all validation sets [p < 0.0001]. Prediction of problematic internet use was possible using specific measures of impulsivity and compulsivity in a population of volunteers. Moreover, this study offers proof-of-concept in support of using machine learning in psychiatry to demonstrate replicability of results across geographically and culturally distinct settings.
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 title = {Problematic internet use (PIU): Associations with the impulsive-compulsive spectrum. An application of machine learning in psychiatry},
 type = {article},
 year = {2016},
 identifiers = {[object Object]},
 keywords = {ADHD,Adolescent,Adult,Aged,Aged, 80 and over,Behavior, Addictive,Compulsive Behavior,Compulsivity,Female,Humans,Impulsivity,Internet,Internet use,Machine Learning,Machine learning,Male,Middle Aged,OCD,Obsessive-Compulsive Disorder,Online Systems,Predictive Value of Tests,Psychiatry,ROC Curve,Reproducibility of Results,South Africa,Surveys and Questionnaires,United States,Young Adult},
 pages = {94-102},
 volume = {83},
 websites = {http://files/1161/Ioannidis et al. - 2016 - Problematic internet use (PIU) Associations with .pdf,http://www.ncbi.nlm.nih.gov/pubmed/27580487},
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 abstract = {Problematic internet use is common, functionally impairing, and in need of further study. Its relationship with obsessive-compulsive and impulsive disorders is unclear. Our objective was to evaluate whether problematic internet use can be predicted from recognised forms of impulsive and compulsive traits and symptomatology. We recruited volunteers aged 18 and older using media advertisements at two sites (Chicago USA, and Stellenbosch, South Africa) to complete an extensive online survey. State-of-the-art out-of-sample evaluation of machine learning predictive models was used, which included Logistic Regression, Random Forests and Naïve Bayes. Problematic internet use was identified using the Internet Addiction Test (IAT). 2006 complete cases were analysed, of whom 181 (9.0%) had moderate/severe problematic internet use. Using Logistic Regression and Naïve Bayes we produced a classification prediction with a receiver operating characteristic area under the curve (ROC-AUC) of 0.83 (SD 0.03) whereas using a Random Forests algorithm the prediction ROC-AUC was 0.84 (SD 0.03) [all three models superior to baseline models p < 0.0001]. The models showed robust transfer between the study sites in all validation sets [p < 0.0001]. Prediction of problematic internet use was possible using specific measures of impulsivity and compulsivity in a population of volunteers. Moreover, this study offers proof-of-concept in support of using machine learning in psychiatry to demonstrate replicability of results across geographically and culturally distinct settings.},
 bibtype = {article},
 author = {Ioannidis, Konstantinos and Chamberlain, Samuel R and Treder, Matthias S and Kiraly, Franz and Leppink, Eric W and Redden, Sarah A and Stein, Dan J and Lochner, Christine and Grant, Jon E},
 journal = {Journal of Psychiatric Research}
}
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