{"id":"W2177271944","doi":"10.1111/poms.12514","title":"Designing Personalized Treatment: An Application to Anticoagulation Therapy","year":2015,"lang":"en","type":"article","venue":"Production and Operations Management","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; McGill University","funders":"","keywords":"Partially observable Markov decision process; Unobservable; Warfarin; Sensitivity (control systems); Markov decision process; Computer science; Set (abstract data type); Medicine; Personalized medicine; Markov chain; Markov process; Machine learning; Markov model; Mathematics; Atrial fibrillation; Econometrics; Statistics; Internal medicine; Bioinformatics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.008185278,0.001385219,0.001517813,0.0009062522,0.0009976047,0.001955314,0.001771948,0.003076178,0.004677503],"category_scores_gemma":[0.02717447,0.001063759,0.001151027,0.001199905,0.002836181,0.002080514,0.001856348,0.003056462,0.00035184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003006972,"about_ca_system_score_gemma":0.004537072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005727498,"about_ca_topic_score_gemma":0.005623579,"domain_scores_codex":[0.9947373,0.00371909,0.0001614373,0.0006094154,0.0005748949,0.0001979162],"domain_scores_gemma":[0.9798529,0.01764569,0.0009918171,0.0006233645,0.0005567786,0.0003293545],"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":[0.00007423574,0.0001447204,0.001596229,0.0001694035,0.00009513387,0.0001814825,0.0003363319,0.7272857,0.0005310627,0.2298264,0.001465626,0.03829363],"study_design_scores_gemma":[0.00006604064,0.0001176847,0.0003630796,0.00005409081,0.00003765467,0.00008258356,0.00008791385,0.8269699,0.000343351,0.1669031,0.004947975,0.00002653522],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005485073,0.0003952039,0.9891817,0.00218293,0.00005308778,0.0000996743,0.00004064668,0.00006576599,0.002495987],"genre_scores_gemma":[0.3106592,0.001448923,0.6832511,0.0008320797,0.0002490981,0.0005209575,0.00007844171,0.00007548955,0.002884641],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008185278,"threshold_uncertainty_score":0.04328841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5810209444683472,"score_gpt":0.545120450267885,"score_spread":0.03590049420046215,"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."}}