{"id":"W3211038758","doi":"10.2196/19812","title":"Predicting Hepatocellular Carcinoma With Minimal Features From Electronic Health Records: Development of a Deep Learning Model","year":2021,"lang":"en","type":"article","venue":"JMIR Cancer","topic":"Hepatocellular Carcinoma Treatment and Prognosis","field":"Medicine","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Hepatocellular carcinoma; Receiver operating characteristic; Internal medicine; Medical record; Odds ratio; Cancer; Oncology; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001451468,0.0006139688,0.000623714,0.0009311275,0.000264608,0.0006349225,0.0008677804,0.0008402038,0.0008178697],"category_scores_gemma":[0.004002313,0.0003307983,0.0006204016,0.0005642744,0.0002551947,0.000717057,0.0007347664,0.001011454,0.0002633054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008472851,"about_ca_system_score_gemma":0.001133057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01250063,"about_ca_topic_score_gemma":0.007643573,"domain_scores_codex":[0.9996428,0.000107925,0.00003816425,0.00009505955,0.00005588449,0.00006007596],"domain_scores_gemma":[0.9986326,0.00084346,0.0001242278,0.00005391381,0.0002798171,0.00006589682],"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":[0.0003703952,0.0006457646,0.09145069,0.00008189791,0.0002826827,0.0002485974,0.000102908,0.7530099,0.001675542,0.001131627,0.003260997,0.147739],"study_design_scores_gemma":[0.000004796017,0.0000238605,0.001217876,0.000005642871,0.00001030207,0.00001155906,0.000005589514,0.9981248,0.0001392594,0.0003921668,0.00006118177,0.000002871638],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6726894,0.00097187,0.3188944,0.002307714,0.0001252855,0.0002708666,0.001614664,0.0008126243,0.002313301],"genre_scores_gemma":[0.9597841,0.0002412669,0.03691252,0.0002269131,0.00005317279,0.0001718627,0.001301956,0.00001365762,0.001294624],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01250063,"threshold_uncertainty_score":0.02485579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03369052656293291,"score_gpt":0.2697382859434895,"score_spread":0.2360477593805566,"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."}}