{"id":"W3196700564","doi":"10.1145/3472163.3472175","title":"Explainable Data Analytics for Disease and Healthcare Informatics","year":2021,"lang":"en","type":"article","venue":"","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba","keywords":"Computer science; Data science; Interpretability; Big data; Analytics; Health informatics; Informatics; Data analysis; Health care; Component (thermodynamics); Variety (cybernetics); Disease; Data modeling; Data mining; Artificial intelligence; Medicine; Database; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001934902,0.00005933546,0.00007986523,0.00003689716,0.00009073692,0.0001945219,0.0007159016,0.00002231779,0.00000361103],"category_scores_gemma":[0.0001575432,0.00005551561,0.00001032671,0.0001923822,0.00001882212,0.001106984,0.0007630619,0.00003286493,0.000002836854],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001842818,"about_ca_system_score_gemma":0.0001926162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004226501,"about_ca_topic_score_gemma":0.00001066384,"domain_scores_codex":[0.9993426,0.0000140234,0.000188128,0.0001971961,0.0001127201,0.0001453948],"domain_scores_gemma":[0.998002,0.00006964788,0.00005523036,0.001593927,0.0001583684,0.0001208173],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004292054,0.00004455696,0.0008619246,0.000313018,0.00001317498,0.000006593244,0.000211051,0.000003527075,0.0000491868,0.842477,0.09904037,0.05697536],"study_design_scores_gemma":[0.0001909433,0.00002233697,0.0007541847,0.00002025416,0.000008916176,0.000007134599,0.0002196548,0.7816365,0.001703559,0.01892264,0.1963493,0.0001645648],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00007687309,0.0001293823,0.9919192,0.006953807,0.00003940878,0.0001486247,0.0001571494,0.000183427,0.0003921041],"genre_scores_gemma":[0.03039083,0.0002034581,0.965335,0.002489448,0.00002324161,0.00002982662,0.0006500476,0.000005230646,0.0008728827],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8235543,"threshold_uncertainty_score":0.2263861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09696791973435473,"score_gpt":0.3332649963416341,"score_spread":0.2362970766072794,"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."}}