{"id":"W4386159467","doi":"10.23919/fusion52260.2023.10224085","title":"DEMDE: Decision Making Design based on Bayesian Network for Personalized Monitoring System","year":2023,"lang":"en","type":"article","venue":"","topic":"Healthcare Technology and Patient Monitoring","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Robustness (evolution); Bayesian network; Decision support system; Domain (mathematical analysis); Reliability (semiconductor); Context (archaeology); Probabilistic logic; Machine learning; Data mining; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.000943004,0.0001621103,0.0002995697,0.0002373902,0.0003081723,0.00001318065,0.00009733997,0.0002609057,0.0000179461],"category_scores_gemma":[0.0003145564,0.0001371542,0.0001051538,0.0005545728,0.00002318553,0.00003151947,0.00001990303,0.0002226498,0.00007954266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002060986,"about_ca_system_score_gemma":0.00009777938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006337159,"about_ca_topic_score_gemma":8.911093e-7,"domain_scores_codex":[0.9985168,0.00007120072,0.0003008195,0.0003220515,0.0002816116,0.0005075081],"domain_scores_gemma":[0.9980025,0.001415637,0.00006746373,0.0003113236,0.00008751547,0.0001156181],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.006656225,0.0001358467,0.5392613,0.002052061,0.0001685036,0.0004349861,0.000380817,0.02369668,0.0005449705,0.007758936,0.0175175,0.4013922],"study_design_scores_gemma":[0.008234896,0.003528075,0.04683147,0.01849834,0.0002480101,0.00005804032,0.002235852,0.9036053,0.005663699,0.001021492,0.009267194,0.0008075887],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1036539,0.0001285778,0.8880755,0.0007562963,0.002404004,0.001704639,0.000003694871,0.002332339,0.0009411019],"genre_scores_gemma":[0.885994,0.000006890274,0.1125685,0.0001278457,0.0007651906,0.0001944395,0.000007754461,0.00003853736,0.0002968697],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8799087,"threshold_uncertainty_score":0.5592986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09512982472463485,"score_gpt":0.3764585223365461,"score_spread":0.2813286976119113,"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."}}