{"id":"W2954560470","doi":"10.5539/ijsp.v8n4p60","title":"Medical Intervention for Disease Stages Using Game Theory, Markov Chains, and Bayesian Inference","year":2019,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Markov chain; Bayesian probability; Inference; Mathematics; Bayesian inference; Bayesian game; Computer science; Markov process; Artificial intelligence; Game theory; Econometrics; Mathematical economics; Mathematical optimization; Machine learning; Statistics; Sequential game","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.01007027,0.001630008,0.00202118,0.002500399,0.001206085,0.002471987,0.002328819,0.002669052,0.004536721],"category_scores_gemma":[0.03159816,0.0010087,0.001958857,0.001361101,0.002927341,0.003726114,0.001891744,0.003371388,0.0002976734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004093721,"about_ca_system_score_gemma":0.00416838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0236041,"about_ca_topic_score_gemma":0.01962793,"domain_scores_codex":[0.994589,0.003687627,0.0001582856,0.0006225174,0.0006158624,0.0003268186],"domain_scores_gemma":[0.9774016,0.02056757,0.001051793,0.0002599307,0.0004163981,0.0003026704],"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":[0.00008387327,0.0001063865,0.003005665,0.0001422812,0.0001976335,0.0002067902,0.0002514427,0.5292219,0.0003232898,0.4451468,0.001165804,0.02014814],"study_design_scores_gemma":[0.00004562028,0.0000378344,0.0004791301,0.00004117737,0.0000432994,0.00005670896,0.00004155367,0.6720192,0.0001056164,0.3264282,0.0006717137,0.00002993113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01060371,0.0005947777,0.9843158,0.002012696,0.00005346066,0.0001108936,0.00009342057,0.00007560697,0.002139671],"genre_scores_gemma":[0.660057,0.002361566,0.3294927,0.0007881125,0.0003069074,0.0007065905,0.0002154081,0.00009327078,0.00597846],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0236041,"threshold_uncertainty_score":0.05325729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05640725535649568,"score_gpt":0.4160866617305908,"score_spread":0.3596794063740951,"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."}}