{"id":"W4294276952","doi":"10.18178/ijmlc.2022.12.5.1107","title":"Lifespan Prediction for Lung and Bronchus Cancer Patients via Machine Learning Techniques","year":2022,"lang":"en","type":"article","venue":"International Journal of Machine Learning and Computing","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; University of Saskatchewan","keywords":"Computer science; Lung cancer; Bronchus; Artificial intelligence; Machine learning; Lung; Medicine; Oncology; Internal medicine; Respiratory disease","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005113683,0.0003262886,0.0002638162,0.001066947,0.0001312434,0.0002882666,0.0002413812,0.0003268042,0.000823556],"category_scores_gemma":[0.002484113,0.00009092925,0.0004105845,0.0005347456,0.00006393886,0.0003314151,0.0002406966,0.0004087792,0.0003006496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003663373,"about_ca_system_score_gemma":0.0004011635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005057126,"about_ca_topic_score_gemma":0.0062569,"domain_scores_codex":[0.999877,0.00004039224,0.00001423983,0.00002989358,0.00002329896,0.00001530205],"domain_scores_gemma":[0.9992322,0.000386424,0.0001717561,0.00003441398,0.0001225044,0.00005274066],"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":[0.0003921545,0.0002530792,0.5290216,0.0001337608,0.0001085774,0.0002670762,0.0001200528,0.2722955,0.001622965,0.0009231002,0.004056953,0.1908052],"study_design_scores_gemma":[0.000008397133,0.0001610138,0.08279313,0.00003336085,0.0000322097,0.0001540319,0.00009172078,0.9128901,0.001163609,0.001329601,0.001326229,0.00001663073],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9347762,0.001473644,0.05605079,0.0007311911,0.000057375,0.00005957561,0.004328565,0.0004052888,0.002117302],"genre_scores_gemma":[0.9843864,0.0003390998,0.01243976,0.00003262243,0.00002544425,0.0000377417,0.002195782,0.000008982016,0.0005341519],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.005057126,"threshold_uncertainty_score":0.01005536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03266609773388481,"score_gpt":0.4235129509432974,"score_spread":0.3908468532094126,"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."}}