{"id":"W4322616811","doi":"10.1002/hbm.26243","title":"Prediction of suicidality in bipolar disorder using variability of intrinsic brain activity and machine learning","year":2023,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Institute on Alcohol Abuse and Alcoholism; National High-tech Research and Development Program; Jiangsu Provincial Key Research and Development Program; National Natural Science Foundation of China; Jiangsu Commission of Health","keywords":"Neuroimaging; Bipolar disorder; Psychology; Anterior cingulate cortex; Precuneus; Functional magnetic resonance imaging; Resting state fMRI; Suicidal ideation; Brain activity and meditation; Major depressive disorder; Default mode network; Persistent vegetative state; Poison control; Neuroscience; Electroencephalography; Medicine; Injury prevention; Cognition; Consciousness; Minimally conscious state","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":[0.0008751617,0.0004132927,0.0003842897,0.0007011857,0.0001449613,0.0004488465,0.000231756,0.0003420302,0.0003290407],"category_scores_gemma":[0.001716183,0.0001715266,0.0004721975,0.0002439066,0.0001379319,0.0002280933,0.0002144947,0.0003587734,0.0001259031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001796871,"about_ca_system_score_gemma":0.0001858306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002151978,"about_ca_topic_score_gemma":0.003350232,"domain_scores_codex":[0.9997904,0.00008693273,0.00001955735,0.0000530315,0.00002641141,0.00002368967],"domain_scores_gemma":[0.9995379,0.000203908,0.0001054915,0.00004778568,0.00006833686,0.00003657243],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009957702,0.0003791725,0.7325606,0.0000966072,0.0005633349,0.0002913963,0.0001737126,0.09234042,0.01172335,0.0003335611,0.0009891604,0.1595531],"study_design_scores_gemma":[0.00002223406,0.0002824443,0.2597095,0.00003440807,0.00008509795,0.0002330504,0.00006574987,0.7369716,0.001475806,0.0009011261,0.0001936762,0.00002534004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9725041,0.0005256204,0.02613471,0.000109908,0.00002099608,0.00003097626,0.0002137181,0.000109695,0.0003503491],"genre_scores_gemma":[0.9946195,0.00009665459,0.004863319,0.00001398446,0.00001221159,0.00001246189,0.000273267,0.00000433092,0.000104242],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002151978,"threshold_uncertainty_score":0.00462836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0968209140416311,"score_gpt":0.2907367412414136,"score_spread":0.1939158271997825,"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."}}