{"id":"W2789037897","doi":"10.1609/aaai.v32i1.11289","title":"Learning Differences Between Visual Scanning Patterns Can Disambiguate Bipolar and Unipolar Patients","year":2018,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Mental Health Research Topics","field":"Psychology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto; Nvidia","keywords":"Artificial intelligence; Pattern recognition (psychology); Classifier (UML); Convolutional neural network; Computer science; Bipolar disorder; Recurrent neural network; Mood; Artificial neural network; Medicine; Psychiatry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005255194,0.0004149746,0.0003239945,0.000974767,0.0001498065,0.0005582367,0.0001397798,0.0004164205,0.001212743],"category_scores_gemma":[0.002889334,0.0001504047,0.0003121771,0.0002445227,0.0001525318,0.0003317834,0.0003019175,0.000294886,0.0004718306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002241781,"about_ca_system_score_gemma":0.0001211662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002050777,"about_ca_topic_score_gemma":0.004951649,"domain_scores_codex":[0.9997891,0.00004763377,0.00002498383,0.00007904177,0.0000276578,0.00003158866],"domain_scores_gemma":[0.9994012,0.0002396065,0.0001417061,0.00004701481,0.00009998662,0.00007053708],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004107945,0.0002557193,0.6871049,0.0001871069,0.000382353,0.0006560556,0.0005041045,0.005983908,0.04878927,0.0004840895,0.005300246,0.2462443],"study_design_scores_gemma":[0.00008266144,0.0004489228,0.9280857,0.00009185082,0.0001626339,0.001221567,0.0004158203,0.05896034,0.007279367,0.001695474,0.001507637,0.00004790263],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9922609,0.0004536789,0.004927398,0.0001398921,0.00005004889,0.00003160224,0.0003886777,0.0001028849,0.001645006],"genre_scores_gemma":[0.9963875,0.0001383562,0.002580026,0.00006376993,0.00002270552,0.0000115272,0.0005443248,0.00001270088,0.0002390906],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002050777,"threshold_uncertainty_score":0.004077613,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1382425566420743,"score_gpt":0.4066659573390452,"score_spread":0.2684234006969709,"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."}}