{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006948072,0.0005592139,0.0004217926,0.001353845,0.0001492394,0.0009172457,0.0004351431,0.0004355955,0.001394208],"category_scores_gemma":[0.005574321,0.000160764,0.0003972133,0.001106427,0.0001265596,0.0005496895,0.0003043953,0.0007099995,0.0003869971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003344016,"about_ca_system_score_gemma":0.000468069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007908167,"about_ca_topic_score_gemma":0.005708952,"domain_scores_codex":[0.9997143,0.0001092597,0.00002671813,0.00004973319,0.00008072817,0.00001930769],"domain_scores_gemma":[0.9990709,0.0006152784,0.0001306942,0.00003561916,0.000121549,0.00002599096],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005414247,0.0003066917,0.2095069,0.0002250333,0.0005397234,0.0005546475,0.0001375473,0.3475606,0.002737653,0.005626671,0.005151288,0.4271118],"study_design_scores_gemma":[0.00002117617,0.00006915558,0.0262054,0.00008119288,0.00007560305,0.0002010866,0.00004185822,0.9629491,0.0007685257,0.007885205,0.001671091,0.00003061149],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3172604,0.004982484,0.6591982,0.002717909,0.0003164221,0.0002590541,0.002041748,0.002352848,0.010871],"genre_scores_gemma":[0.9187585,0.002271288,0.0747513,0.0001158803,0.000192869,0.0001383155,0.001397382,0.00004624934,0.002328188],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007908167,"threshold_uncertainty_score":0.01572424,"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."}}