{"id":"W4406831168","doi":"10.2196/58649","title":"Interpretable Machine Learning Model for Predicting Postpartum Depression: Retrospective Study","year":2025,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Maternal Mental Health During Pregnancy and Postpartum","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Receiver operating characteristic; Machine learning; Artificial intelligence; Medicine; Postpartum depression; Feature selection; Predictive modelling; Random forest; Lasso (programming language); Cohort; Computer science; Pregnancy; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006241537,0.0006580486,0.0005824399,0.001184558,0.0003196741,0.0008827472,0.0008068106,0.0005490698,0.001309091],"category_scores_gemma":[0.01983491,0.0002925138,0.0009057007,0.0008095299,0.0002851825,0.0007377171,0.0004744971,0.001126193,0.0004516272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000517377,"about_ca_system_score_gemma":0.0006524763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005127668,"about_ca_topic_score_gemma":0.0028594,"domain_scores_codex":[0.99843,0.0008338117,0.0001433083,0.0002601537,0.0002293478,0.0001034283],"domain_scores_gemma":[0.9913508,0.005399258,0.0008765604,0.001005693,0.001137376,0.0002304502],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003221777,0.000353607,0.9706613,0.00003943398,0.000205214,0.0003761312,0.0002120127,0.008759981,0.0001546538,0.000226214,0.0008836979,0.01780571],"study_design_scores_gemma":[0.00008892082,0.001563838,0.514183,0.0001314014,0.0005636137,0.001700157,0.0009384197,0.4760183,0.0008007088,0.00190134,0.002042952,0.0000673825],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9897697,0.0003458129,0.008457563,0.0001466044,0.00002631166,0.00008532256,0.0007599701,0.00003014306,0.000378685],"genre_scores_gemma":[0.9960718,0.0001652265,0.002571757,0.0000306583,0.00001913568,0.00004637591,0.0009008971,0.00000795673,0.0001862146],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006241537,"threshold_uncertainty_score":0.03300875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01621977990747558,"score_gpt":0.3375167038404321,"score_spread":0.3212969239329566,"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."}}