{"id":"W4311927287","doi":"10.1111/bdi.13282","title":"Information theory characteristics improve the prediction of lithium response in bipolar disorder patients using a support vector machine classifier","year":2022,"lang":"en","type":"article","venue":"Bipolar Disorders","topic":"Bipolar Disorder and Treatment","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Agence Nationale de la Recherche; Bonfils-Stanton Foundation; Israel Science Foundation; Simons Foundation","keywords":"Support vector machine; Bipolar disorder; Mood; Treatment of bipolar disorder; Mood stabilizer; Computer science; Psychology; Artificial intelligence; Pattern recognition (psychology); Neuroscience; Machine learning; Psychiatry; Mania","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000865695,0.0002606375,0.0003359306,0.0003799764,0.000314715,0.00002165464,0.0001815745,0.0001037879,0.0003068151],"category_scores_gemma":[0.0002535084,0.0002042804,0.0001714639,0.0005863221,0.0001362448,0.0002922468,0.0001732958,0.000491788,0.00001059462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002602362,"about_ca_system_score_gemma":0.0002536345,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004455186,"about_ca_topic_score_gemma":0.0000909672,"domain_scores_codex":[0.9976904,0.0004934185,0.0006656193,0.0002208819,0.0005728384,0.0003568135],"domain_scores_gemma":[0.9988666,0.0001441079,0.000316637,0.0004906238,0.00009586704,0.00008619672],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004199031,0.001326796,0.8807986,0.000102771,0.0001542928,0.000001003179,0.003803922,0.0000280613,0.001245849,0.0002215665,0.00004820678,0.1080699],"study_design_scores_gemma":[0.002982853,0.001242805,0.7412038,0.00001598007,0.0001719212,0.000007807881,0.000997089,0.001650381,0.00007615815,0.00007744127,0.2514279,0.0001459213],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9849705,0.008901391,0.0009833151,0.0006056809,0.00061462,0.001798497,0.001930769,0.00006954615,0.0001256874],"genre_scores_gemma":[0.9976072,0.0009107034,0.0000637227,0.0002002074,0.00002355587,0.0001450195,0.0008818583,0.00004070502,0.0001270294],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2513797,"threshold_uncertainty_score":0.8330311,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008617455268865915,"score_gpt":0.2288277358925707,"score_spread":0.2202102806237048,"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."}}