{"id":"W4234323918","doi":"10.1109/iembs.2006.4398852","title":"Risk Factors for Apgar Score using Artificial Neural Networks","year":2006,"lang":"en","type":"article","venue":"Conference proceedings","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Children's Hospital of Eastern Ontario; Carleton University; University of Ottawa","funders":"Physicians' Services Incorporated Foundation","keywords":"Apgar score; Artificial neural network; Computer science; Machine learning; Artificial intelligence; Framingham Risk Score; F1 score; Domain (mathematical analysis); Birth weight; Pregnancy; Medicine; Internal medicine; Mathematics; Disease","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003087731,0.0002404241,0.0002440466,0.0001173427,0.000461205,0.0005331336,0.000806453,0.0001427992,0.00001270338],"category_scores_gemma":[0.0002432876,0.0002240456,0.00009202085,0.000362067,0.00007184946,0.0005476858,0.0001875515,0.000408861,0.000003395276],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006758313,"about_ca_system_score_gemma":0.00008461249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006883883,"about_ca_topic_score_gemma":0.00002758632,"domain_scores_codex":[0.9981941,0.00001989737,0.0003584155,0.0005931332,0.0002469915,0.0005874479],"domain_scores_gemma":[0.9988015,0.0001274698,0.000300044,0.0001914571,0.0004575519,0.0001219445],"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":[0.00003312236,0.00007223094,0.5905188,0.0001652579,0.00001590262,0.000002976532,0.001730518,0.01283431,0.0008871041,0.364203,0.0006808495,0.02885595],"study_design_scores_gemma":[0.000114141,0.00009468717,0.01586565,0.0000310058,0.0000108767,0.000006258063,0.00008915956,0.9685831,0.0003816614,0.01433348,0.0002370983,0.0002529002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5573805,0.00003646577,0.4411286,0.0002865092,0.0003854634,0.0003040885,0.000003766135,0.000241583,0.0002329828],"genre_scores_gemma":[0.9810976,0.000002416501,0.01823659,0.00008267541,0.0004793453,0.00002658193,0.000007595317,0.00002235685,0.00004483022],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9557488,"threshold_uncertainty_score":0.9136313,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06231717561133152,"score_gpt":0.2977966352313483,"score_spread":0.2354794596200167,"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."}}