{"id":"W2113852177","doi":"10.1145/1362550.1362554","title":"Intelligent decision support in medicine","year":2007,"lang":"en","type":"article","venue":"","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Pace; Machine learning; Artificial intelligence; Personalization; Presentation (obstetrics); Bayesian network; Feature (linguistics); Artificial neural network; Clinical decision support system; Bayesian probability; Decision support system; Naive Bayes classifier; Data science; Bayes' theorem; Medicine; World Wide Web; Support vector machine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004830333,0.00077667,0.001078058,0.001708869,0.0009069398,0.007363748,0.001423126,0.003375072,0.01540854],"category_scores_gemma":[0.01379768,0.0003354392,0.0006253687,0.00143559,0.002559279,0.004337898,0.003105092,0.002618204,0.003915064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001825799,"about_ca_system_score_gemma":0.002123058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001253574,"about_ca_topic_score_gemma":0.001047604,"domain_scores_codex":[0.9963068,0.001953189,0.0002820862,0.0004241464,0.0008816645,0.0001521692],"domain_scores_gemma":[0.9938115,0.004364458,0.0004010281,0.000422986,0.0006514429,0.0003485601],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000748892,0.00007503082,0.001200745,0.0009918006,0.0001190006,0.0002033641,0.000423713,0.00801996,0.0003427637,0.6083941,0.07055347,0.309601],"study_design_scores_gemma":[0.00004030959,0.00006685308,0.0004668907,0.0007862789,0.00004114557,0.0002595948,0.0002801029,0.01216478,0.000331245,0.7423593,0.2431566,0.00004696883],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007786102,0.1475042,0.3740082,0.1646309,0.00755331,0.0003429829,0.001033987,0.0009936661,0.2961465],"genre_scores_gemma":[0.4825577,0.1405106,0.286464,0.02268962,0.01144038,0.0006284322,0.001334886,0.0001555876,0.0542188],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01540854,"threshold_uncertainty_score":0.05154669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03838031148591913,"score_gpt":0.3803132447813472,"score_spread":0.3419329332954281,"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."}}