{"id":"W4214699115","doi":"10.5220/0010916000003123","title":"Survival Status Prediction for Non-small Cell Lung Cancer Patients using Machine Learning","year":2022,"lang":"en","type":"article","venue":"Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Lung cancer; Machine learning; Artificial intelligence; Oncology; Medicine","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":[0.0005720622,0.0003553609,0.0004685982,0.00106374,0.0002132069,0.0007074606,0.0003271165,0.0004560898,0.001450533],"category_scores_gemma":[0.002069816,0.00009486106,0.0004984877,0.0005005131,0.0001203699,0.0004207667,0.0003272487,0.0004623877,0.000509305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002217022,"about_ca_system_score_gemma":0.0002751385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002133812,"about_ca_topic_score_gemma":0.003474764,"domain_scores_codex":[0.9998099,0.00004256848,0.00002508018,0.00004434793,0.00003586654,0.00004233267],"domain_scores_gemma":[0.9990968,0.0004734687,0.0001083624,0.00004160099,0.0001782545,0.0001015118],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008449362,0.0002485904,0.8994536,0.00004086758,0.0001118152,0.000136439,0.00004122859,0.005621971,0.001804923,0.0001468657,0.002092132,0.08945655],"study_design_scores_gemma":[0.00007931946,0.0008679343,0.5181322,0.00005075806,0.0004075248,0.0004767886,0.0002948828,0.470796,0.004032458,0.001839524,0.002972223,0.00005041462],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9881557,0.0009738442,0.007133785,0.0005306915,0.00009244009,0.00003445219,0.00152641,0.0001247793,0.00142782],"genre_scores_gemma":[0.9965197,0.0001480979,0.001309072,0.00003291417,0.00004811368,0.00001404443,0.001374779,0.000005035334,0.0005483069],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002133812,"threshold_uncertainty_score":0.004852533,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01624470561451037,"score_gpt":0.2497985869515057,"score_spread":0.2335538813369953,"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."}}