{"id":"W2927467691","doi":"10.1002/cpe.5252","title":"Deep learning models for diagnosing spleen and stomach diseases in smart Chinese medicine with cloud computing","year":2019,"lang":"en","type":"article","venue":"Concurrency and Computation Practice and Experience","topic":"AI in cancer detection","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Cloud computing; Computer science; Spleen; Stomach; Deep learning; Modern medicine; Traditional Chinese medicine; Artificial intelligence; Medicine; Pathology; Internal medicine; Intensive care medicine","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":[0.0002507686,0.0001447757,0.0001999362,0.00008840271,0.0002292478,0.0001453602,0.0001073249,0.00003548399,0.000001411393],"category_scores_gemma":[0.000214173,0.0001209031,0.000009936682,0.0002779807,0.0001139903,0.001923122,0.0001023582,0.0001489514,4.913657e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002173143,"about_ca_system_score_gemma":0.00002971313,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007994132,"about_ca_topic_score_gemma":0.000009156762,"domain_scores_codex":[0.9988734,0.0001004702,0.0002148126,0.0004629081,0.0001675458,0.0001808541],"domain_scores_gemma":[0.9981208,0.001404773,0.0001844374,0.0001006204,0.0001066479,0.00008267381],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002237942,0.00009553066,0.1297596,0.0002773359,0.00002815535,0.00001544876,0.08901145,0.05433118,0.0001108299,0.01048615,0.00002296863,0.7156376],"study_design_scores_gemma":[0.001027893,0.0004483628,0.01260872,0.0001419938,0.00001223868,0.00006277706,0.003307363,0.9800953,0.00001005548,0.001819339,0.0002852876,0.0001806837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4215223,0.002358092,0.5751727,0.0003832462,0.0002209491,0.000211659,2.516335e-7,0.00003984222,0.00009088717],"genre_scores_gemma":[0.9918812,0.0003528623,0.007424627,0.0002290271,0.00006523619,0.00003120789,0.000003396008,0.000007037727,0.000005410478],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9257641,"threshold_uncertainty_score":0.4930285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01942941235621537,"score_gpt":0.31814882606555,"score_spread":0.2987194137093346,"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."}}