{"id":"W2120701494","doi":"10.1109/icmla.2009.92","title":"Survival Prediction in Lung Cancer Treated with Radiotherapy: Bayesian Networks vs. Support Vector Machines in Handling Missing Data","year":2009,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre","funders":"","keywords":"Missing data; Support vector machine; Computer science; Bayesian network; Artificial intelligence; Domain (mathematical analysis); Bayesian probability; Lung cancer; Machine learning; Medicine; Oncology; Mathematics","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":[0.01569809,0.0005561061,0.0009694319,0.001794961,0.0003195474,0.001022371,0.0006851292,0.001196299,0.0006070704],"category_scores_gemma":[0.03901799,0.0003759999,0.0006326205,0.0009921456,0.0003901015,0.002221029,0.0007256462,0.001263542,0.0002288409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008261269,"about_ca_system_score_gemma":0.0008764601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006006123,"about_ca_topic_score_gemma":0.005517581,"domain_scores_codex":[0.9955156,0.003570372,0.0001760212,0.0002209317,0.0003752903,0.0001418382],"domain_scores_gemma":[0.9804255,0.01674474,0.001059726,0.0004983091,0.0008899185,0.00038186],"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":[0.002940075,0.0004363444,0.1567886,0.000351682,0.0006140374,0.0001637395,0.0003779081,0.500389,0.001201297,0.005903326,0.002270987,0.3285631],"study_design_scores_gemma":[0.00007244927,0.0002327009,0.009595974,0.00007303575,0.00005527242,0.00006204993,0.00006636827,0.9792876,0.0004209235,0.009773878,0.0003327008,0.00002703207],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6817653,0.005391911,0.3039843,0.005555634,0.0001452388,0.0001383785,0.0005803305,0.00043319,0.00200575],"genre_scores_gemma":[0.9723555,0.000682221,0.02599975,0.0001789334,0.0001069007,0.00006309539,0.0002716984,0.00001651657,0.0003254854],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01569809,"threshold_uncertainty_score":0.08302045,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09553098012307575,"score_gpt":0.4551401833412734,"score_spread":0.3596092032181976,"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."}}