{"id":"W2131992341","doi":"10.1109/cbms.2005.84","title":"Predicting Preterm Birth Using Artificial Neural Networks","year":2005,"lang":"en","type":"article","venue":"","topic":"Preterm Birth and Chorioamnionitis","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Carleton University","funders":"","keywords":"Artificial neural network; Artificial intelligence; A priori and a posteriori; Medicine; Computer science; Gestation; Machine learning; Training set; Birth weight; Set (abstract data type); Statistics; Pregnancy; Pediatrics; 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":[0.001658953,0.0006324759,0.0004715718,0.000699902,0.0002270137,0.0005804891,0.0004899176,0.0007560217,0.0005625118],"category_scores_gemma":[0.01085894,0.0002406316,0.0002493214,0.0003913553,0.0002151036,0.0005626343,0.0003579874,0.0005751095,0.0001955878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004984618,"about_ca_system_score_gemma":0.0004122665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004318928,"about_ca_topic_score_gemma":0.00382555,"domain_scores_codex":[0.9995956,0.0001785031,0.00003587267,0.00006282815,0.00008765129,0.00003952999],"domain_scores_gemma":[0.9957165,0.003260949,0.0002352474,0.0001328503,0.0006007848,0.00005370032],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006707269,0.00027602,0.06422941,0.00009587643,0.0001093568,0.0002300776,0.0000681716,0.7726403,0.004421647,0.0004093032,0.0007708264,0.1560781],"study_design_scores_gemma":[0.000008436639,0.00006544562,0.0035323,0.00001231121,0.00001184434,0.00003580831,0.00001023868,0.9943528,0.001458851,0.0004058659,0.00009907764,0.000006958074],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8379259,0.0007357492,0.1579644,0.0006519879,0.00008960017,0.00006805197,0.0002717942,0.0006117404,0.001680867],"genre_scores_gemma":[0.9648699,0.0002362034,0.03369552,0.00007765488,0.00003337816,0.00004872164,0.0003187855,0.00001250495,0.0007073576],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004318928,"threshold_uncertainty_score":0.008773446,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02610259627198896,"score_gpt":0.2716756217062763,"score_spread":0.2455730254342874,"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."}}