{"id":"W4391770155","doi":"10.1109/aicera/icis59538.2023.10420035","title":"Children Hematopoietic Stem Cell Transplant Survival Status Prediction using Machine Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Digital Imaging for Blood Diseases","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Decision tree; Machine learning; AdaBoost; Random forest; Artificial intelligence; Classifier (UML); Computer science; Hematopoietic stem cell transplantation; Feature selection; Gradient boosting; Correlation; Medicine; Transplantation; Internal medicine; 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":[0.0008336111,0.0003912232,0.0003559705,0.001475072,0.0001775522,0.0005359527,0.0003181466,0.0003637944,0.001068569],"category_scores_gemma":[0.002459876,0.00009882113,0.0005746161,0.0007697043,0.0000868708,0.0003589003,0.0003486614,0.0006066458,0.0003989088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004881097,"about_ca_system_score_gemma":0.0006304717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005538562,"about_ca_topic_score_gemma":0.00617915,"domain_scores_codex":[0.9995878,0.000108615,0.00003912948,0.00008824443,0.0001168575,0.000059299],"domain_scores_gemma":[0.9990649,0.0004398925,0.0001672726,0.00005184918,0.0002034245,0.00007276236],"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":[0.0003613234,0.0003779057,0.6700674,0.0001429017,0.0002252072,0.0002807478,0.0001032146,0.05879026,0.001948301,0.0007625948,0.01040464,0.2565356],"study_design_scores_gemma":[0.00003470094,0.0003839897,0.2743749,0.0001427617,0.0001723692,0.0004065548,0.0003010943,0.7064871,0.005809686,0.00255044,0.009282099,0.00005434395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.952969,0.001738323,0.03377667,0.0009148771,0.0001389209,0.0001180241,0.006938179,0.000473733,0.00293215],"genre_scores_gemma":[0.9779105,0.0004007597,0.0138795,0.00006201091,0.00004491595,0.00005702202,0.00687689,0.00001159895,0.0007569742],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005538562,"threshold_uncertainty_score":0.01101261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01721250869684404,"score_gpt":0.2246180477575274,"score_spread":0.2074055390606834,"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."}}