{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007778358,0.0004904696,0.0005510602,0.0006295323,0.0002351605,0.0007490491,0.0006303038,0.0007259972,0.001154167],"category_scores_gemma":[0.001475748,0.0002123254,0.0007452052,0.0005250914,0.0002845684,0.000503408,0.0005301072,0.0008591626,0.0001909462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000910908,"about_ca_system_score_gemma":0.001249871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02451126,"about_ca_topic_score_gemma":0.01435975,"domain_scores_codex":[0.9998103,0.00004448866,0.00001521839,0.00004236621,0.00003594294,0.00005159752],"domain_scores_gemma":[0.9996189,0.000162135,0.00004166736,0.00002247003,0.0001211309,0.00003355438],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003819643,0.0002730593,0.03054219,0.0001376435,0.0002142503,0.0002986056,0.00008644813,0.7959087,0.002771294,0.004057326,0.006082746,0.1592457],"study_design_scores_gemma":[0.000003493721,0.00001213271,0.0007348847,0.000006080045,0.000009973454,0.000009895623,0.000006256509,0.9983016,0.0002014249,0.0005659318,0.0001458032,0.00000234722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5975427,0.005695295,0.3811042,0.005347064,0.0003357764,0.0001512904,0.001095464,0.0009370102,0.007791166],"genre_scores_gemma":[0.9761564,0.0007890944,0.01969271,0.0002955376,0.00006288764,0.00004557849,0.0004864674,0.00001600954,0.002455294],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02451126,"threshold_uncertainty_score":0.04873717,"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."}}