{"id":"W3198274375","doi":"10.1101/2021.04.23.441182","title":"Reinforcement learning derived chemotherapeutic schedules for robust patient-specific therapy","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Mathematical Biology Tumor Growth","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Canadian Institutes of Health Research","keywords":"Dosing; Reinforcement learning; Schedule; Leverage (statistics); Computer science; Optimal control; Artificial intelligence; Machine learning; Mathematical optimization; Medicine; Mathematics; Pharmacology","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":[0.001481844,0.0005661119,0.0005103914,0.0002882874,0.000164447,0.0003832839,0.0006346445,0.0006285225,0.00125978],"category_scores_gemma":[0.005973349,0.0002726434,0.0002336356,0.0001374829,0.0007262432,0.0004145879,0.0006943346,0.0009944708,0.0002216523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001096978,"about_ca_system_score_gemma":0.001095898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001940546,"about_ca_topic_score_gemma":0.001488185,"domain_scores_codex":[0.9996011,0.0001656264,0.00001746461,0.0000740379,0.0001065078,0.00003529162],"domain_scores_gemma":[0.9984487,0.0009360044,0.0002962402,0.00008405511,0.0001584042,0.00007653742],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004866043,0.00004795338,0.0002730463,0.0000245763,0.000008791721,0.00001877093,0.00001441751,0.9820984,0.002500644,0.003593101,0.0003261097,0.01104564],"study_design_scores_gemma":[0.00001278368,0.000032966,0.00005919027,0.000003650279,0.000002118915,0.000004915924,0.000001721974,0.996977,0.0009295748,0.001769066,0.0002043934,0.000002654298],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05496334,0.000230623,0.9406682,0.0004519536,0.00004347604,0.0001396529,0.00004940006,0.0004533073,0.002999968],"genre_scores_gemma":[0.8979341,0.00009618965,0.1001818,0.0001581267,0.00001967755,0.0001702004,0.0000593814,0.00006820705,0.001312306],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001940546,"threshold_uncertainty_score":0.007959187,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04870882640118827,"score_gpt":0.2565425233294578,"score_spread":0.2078336969282695,"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."}}