{"doi":"10.1037/pha0000532","title":"One-year predictions of delayed reward discounting in the adolescent brain cognitive development study.","abstract":"= 4,042) to build machine learning models to predict DRD at the first follow-up visit, 1 year later. In separate machine learning models, we tested elastic net regression, random forest regression, light gradient boosting regression, and support vector regression. In five-fold cross-validation on the training set, models using an array of questionnaire/task variables were able to predict DRD, with these findings generalizing to a held-out (i.e., \"lockbox\") test set of 20% of the sample. Key predictive variables were neuropsychological test performance at baseline, socioeconomic status, screen media activity, psychopathology, parenting, and personality. However, models using magnetic resonance imaging (MRI)-derived brain variables did not reliably predict DRD in either the cross-validation or held-out test set. These results suggest a combination of questionnaire/task variables as antecedents of excessive DRD in late childhood, which may presage the development of problematic substance use in adolescence. (PsycInfo Database Record (c) 2022 APA, all rights reserved).","journal":"Experimental and Clinical Psychopharmacology","year":2021,"id":183752,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":13,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.8674,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":305480,"name":"Sage Hahn","orcid":"0000-0003-3560-2952","position":1,"is_corresponding":false},{"id":305482,"name":"Nicholas Allgaier","orcid":"0000-0002-9498-0200","position":2,"is_corresponding":false},{"id":308313,"name":"James MacKillop","orcid":"0000-0002-8695-1071","position":3,"is_corresponding":false},{"id":305481,"name":"Matthew D. Albaugh","orcid":"0000-0002-5971-6658","position":4,"is_corresponding":false},{"id":305479,"name":"Dekang Yuan","orcid":"0000-0002-9197-7057","position":5,"is_corresponding":false},{"id":106654,"name":"Anthony Juliano","orcid":"0000-0001-7433-6891","position":6,"is_corresponding":false},{"id":305484,"name":"Alexandra Potter","orcid":"0000-0001-5813-6259","position":7,"is_corresponding":false},{"id":285456,"name":"Hugh Garavan","orcid":"0000-0002-8939-1014","position":8,"is_corresponding":false},{"id":305478,"name":"Max M. Owens","orcid":"0000-0002-5560-4077","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-18T23:48:22.008011Z","pmid":"34914494","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}