{"id":"W6901614103","doi":"10.60692/gy5bb-znc29","title":"Unlocking Maternal Outcome Prediction Potential: A Comprehensive Analysis of the ConvXGB Model Integrating XGBoost and Deep Learning","year":2024,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mount Royal University","funders":"","keywords":"Outcome (game theory); Deep learning; Robustness (evolution); Gradient boosting; Health care; Maternal health","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":[0.002455628,0.0008860062,0.0007652135,0.0005058595,0.000316189,0.0007959798,0.001260945,0.0006994665,0.001478716],"category_scores_gemma":[0.002570865,0.0002777491,0.0004823681,0.0003604971,0.000464774,0.0006998625,0.0009093969,0.001160144,0.0002673882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008318171,"about_ca_system_score_gemma":0.0016154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01973569,"about_ca_topic_score_gemma":0.0166085,"domain_scores_codex":[0.9996723,0.0001271647,0.00001202641,0.00005894596,0.00006623504,0.00006332192],"domain_scores_gemma":[0.9992855,0.0003968356,0.00004796846,0.00004262759,0.0001782051,0.0000488185],"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":[0.0003062846,0.0001753425,0.02431286,0.0001121706,0.0001635126,0.0001452934,0.00006161584,0.8800043,0.001185259,0.003248358,0.004266772,0.08601822],"study_design_scores_gemma":[0.000004435391,0.0000336589,0.0008754376,0.000013008,0.00001176035,0.00001047311,0.000009534488,0.9977344,0.0002036259,0.0008655818,0.0002352496,0.000002697304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5939742,0.003608278,0.386688,0.004111102,0.000311614,0.0001935997,0.001036444,0.001484615,0.008592154],"genre_scores_gemma":[0.975485,0.0003659479,0.0203103,0.0003476442,0.00004801332,0.0000761837,0.0007810144,0.00004189966,0.002544032],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01973569,"threshold_uncertainty_score":0.03924161,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02932248586502857,"score_gpt":0.2528222648224951,"score_spread":0.2234997789574665,"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."}}