{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00109803,0.0002316702,0.0004300287,0.000217939,0.0003798126,0.00002304051,0.0003178561,0.0002947209,0.001295052],"category_scores_gemma":[0.00009029937,0.0001844075,0.00002078848,0.000670347,0.00003975434,0.0003823191,0.00004281635,0.000883197,0.00000502687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006084358,"about_ca_system_score_gemma":0.0005518033,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.04645604,"about_ca_topic_score_gemma":0.1694224,"domain_scores_codex":[0.9969545,0.0005858547,0.000877502,0.0005691779,0.0002404797,0.0007724959],"domain_scores_gemma":[0.9986051,0.0003627331,0.0001809702,0.0005867941,0.00009589022,0.0001685556],"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":[0.0004763739,0.00005795121,0.9696684,0.00007884975,0.00001177159,0.00002710843,0.00177637,0.004748789,0.00005764904,0.00006718702,0.0009606584,0.02206891],"study_design_scores_gemma":[0.0004284891,0.0001199383,0.2610835,0.000709635,0.00001123208,0.00000135791,0.0005872479,0.7364956,0.0000225802,0.00003588236,0.000334618,0.0001699426],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9100601,0.002125284,0.0636042,0.01493666,0.001829323,0.003678604,0.0001509072,0.0005473435,0.003067577],"genre_scores_gemma":[0.9957212,0.0003606458,0.001088041,0.001193493,0.0007081783,0.00006554705,0.0002200266,0.00004339714,0.0005994531],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7317468,"threshold_uncertainty_score":0.9996179,"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."}}