{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001472911,0.0001554422,0.00014474,0.0001606104,0.0001426089,0.0003241047,0.0003749859,0.00002805849,0.000009927636],"category_scores_gemma":[0.000004499338,0.0001444034,0.00008089851,0.0006062108,0.00002903247,0.0007733701,0.0001182555,0.0001139447,0.0001539877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002999379,"about_ca_system_score_gemma":0.00005941888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000172486,"about_ca_topic_score_gemma":0.000005881877,"domain_scores_codex":[0.998543,0.00005499819,0.0002060133,0.0003763833,0.0003725646,0.0004470789],"domain_scores_gemma":[0.9993876,0.00005396332,0.00005219337,0.0002950164,0.00002915229,0.0001820603],"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":[0.00002883141,0.0005743772,0.9270788,0.0002224451,0.0001494497,0.0002007488,0.003974397,0.03637686,0.005234224,0.007763434,0.0006919513,0.0177045],"study_design_scores_gemma":[0.001087897,0.0001325353,0.05015651,0.00007415135,0.00005967887,0.00008535803,0.0001896401,0.9361362,0.01053236,0.0005807723,0.0004349305,0.0005300048],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9191843,0.0001842358,0.07281912,0.00007818732,0.0004352579,0.0001822202,0.00008183981,0.002290556,0.004744334],"genre_scores_gemma":[0.9973578,0.0000187743,0.001432951,0.00003375793,0.00004068452,0.00000377576,0.00006744796,0.00002103912,0.001023749],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8997593,"threshold_uncertainty_score":0.58886,"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."}}