{"id":"W3177006693","doi":"10.21428/594757db.0d86cb14","title":"Hierarchical Reinforcement Learning for Decision Support in Health Care","year":2021,"lang":"en","type":"article","venue":"","topic":"Innovation Diffusion and Forecasting","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Reinforcement learning; Hierarchy; Computer science; Spare part; Artificial intelligence; Plan (archaeology); Machine learning; Markov decision process; Decision support system; Operations research; Management science; Engineering; Markov process; Operations management; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002684892,0.00008608498,0.0002240378,0.0003155372,0.0002267197,0.0001471334,0.0002046783,0.00005432891,0.003728909],"category_scores_gemma":[0.007974342,0.00006508746,0.00008283533,0.001067405,0.0000219628,0.0001209729,0.0001570955,0.0001810816,0.0001132535],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008649431,"about_ca_system_score_gemma":0.0003958865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001292911,"about_ca_topic_score_gemma":0.0001347213,"domain_scores_codex":[0.9972981,0.00009697087,0.001030144,0.0003826359,0.0008972705,0.0002948495],"domain_scores_gemma":[0.99761,0.00127126,0.0001718478,0.0002448378,0.0006066517,0.00009539312],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007615291,0.00003495804,0.006774528,0.00001434592,0.000002350279,0.00001300201,0.003036688,0.007599465,0.000128589,0.0447905,0.01771533,0.9198141],"study_design_scores_gemma":[0.001833731,0.0003673175,0.006826786,0.00008147519,0.000001198347,0.00002177587,0.01617919,0.1010149,0.0008206263,0.01033261,0.8622598,0.0002605955],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08584918,0.00008338549,0.8219733,0.006109513,0.0007484302,0.0004666924,0.000003014533,0.00007066847,0.08469585],"genre_scores_gemma":[0.9514512,0.000006595463,0.02624517,0.003183628,0.00005263875,0.0000147242,0.00003792343,0.00000830623,0.01899975],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9195535,"threshold_uncertainty_score":0.9971818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1029021628977997,"score_gpt":0.4137652075445577,"score_spread":0.3108630446467581,"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."}}