{"id":"W4401154586","doi":"10.1111/insr.12583","title":"Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventions","year":2024,"lang":"en","type":"article","venue":"International Statistical Review","topic":"Mental Health Research Topics","field":"Psychology","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Office of Naval Research; Natural Sciences and Engineering Research Council of Canada; Duke-NUS Medical School; Sapienza Università di Roma; Ministry of Education - Singapore; NIHR Cambridge Biomedical Research Centre","keywords":"Reinforcement learning; Leverage (statistics); Psychological intervention; Computer science; Biostatistics; Bridge (graph theory); Suite; Management science; Health care; Risk analysis (engineering); Artificial intelligence; Data science; Engineering; Public health; Medicine; Nursing; Political science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05538533,0.001068564,0.002587456,0.002320846,0.0006107471,0.002937312,0.002385206,0.001867928,0.002813846],"category_scores_gemma":[0.1641009,0.0009538329,0.001733638,0.00140966,0.004779047,0.00208961,0.003284352,0.004499916,0.0004706407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00264864,"about_ca_system_score_gemma":0.006412231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00347905,"about_ca_topic_score_gemma":0.002736095,"domain_scores_codex":[0.9394299,0.05452974,0.001578246,0.002061645,0.002076742,0.0003237937],"domain_scores_gemma":[0.7970083,0.187313,0.004335312,0.006037284,0.004422495,0.0008835775],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000281378,0.0002498033,0.006534127,0.002663076,0.0009425448,0.0001431874,0.0006675747,0.4072157,0.0007162376,0.2556819,0.00377061,0.3211337],"study_design_scores_gemma":[0.0001039329,0.0002177139,0.0006197113,0.0006946605,0.00009564336,0.00005968132,0.000112012,0.6597873,0.0005295942,0.3325711,0.00516634,0.00004236659],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003535032,0.001626099,0.9917841,0.001970786,0.00008673358,0.0001523692,0.00002949633,0.0001512306,0.0006641814],"genre_scores_gemma":[0.2145731,0.002363892,0.7797521,0.001065786,0.0003552078,0.00114986,0.0001144572,0.0001361595,0.0004894946],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05538533,"threshold_uncertainty_score":0.2929091,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1544487364558986,"score_gpt":0.5208742735654636,"score_spread":0.3664255371095649,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}