{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Kohler RJ"],"funding":["National Institute on Alcohol Abuse and Alcoholism","Veteran' Affairs New England","NIAAA NIH HHS"],"pagination":["e70069"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12285684"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["30(7)"],"pubmed_abstract":["In clinical settings targeting alcohol use disorder (AUD), it is often unclear whether a treatment option may best suit a patient's clinical needs. Clinicians providing AUD treatment are often required to predict patients' responses to guide treatment decisions. Recently, machine learning approaches have been used as tools in precision medicine to help guide these clinical decisions. However, the extent of their clinical utility in populations undergoing treatment is largely unknown. Using data from four Phase 2 randomized clinical trials affiliated with the NIAAA Clinical Investigations Group and a Phase 3 trial sponsored by the NIAAA, we developed a machine learning model to predict treatment response phenotypes derived from clustering drinking rates at the end of treatment. Harmonized d"],"journal":["Addiction biology"],"pubmed_title":["Empirical Derivation and Prediction of Treatment Trajectories in Harmonized AUD Clinical Trial Datasets."],"pmcid":["PMC12285684"],"funding_grant_id":["R03AA028361","CDA1 Award","K01 AA029706","R34AA031678","K01AA029706","R01AA030971","U54AA027989","K01AA025670","R01AA031959"],"pubmed_authors":["Kohler RJ","McKee SA","Chavez CLJ","Zakiniaeiz Y","Peltier MR","Verplaetse TL","Zhou H","Roberts W"],"additional_accession":[]},"is_claimable":false,"name":"Empirical Derivation and Prediction of Treatment Trajectories in Harmonized AUD Clinical Trial Datasets.","description":"In clinical settings targeting alcohol use disorder (AUD), it is often unclear whether a treatment option may best suit a patient's clinical needs. Clinicians providing AUD treatment are often required to predict patients' responses to guide treatment decisions. Recently, machine learning approaches have been used as tools in precision medicine to help guide these clinical decisions. However, the extent of their clinical utility in populations undergoing treatment is largely unknown. Using data from four Phase 2 randomized clinical trials affiliated with the NIAAA Clinical Investigations Group and a Phase 3 trial sponsored by the NIAAA, we developed a machine learning model to predict treatment response phenotypes derived from clustering drinking rates at the end of treatment. Harmonized d","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Jul","modification":"2026-07-15T12:29:40.822Z","creation":"2025-08-13T03:04:27.689Z"},"accession":"S-EPMC12285684","cross_references":{"pubmed":["40700031"],"doi":["10.1111/adb.70069"]}}