{"id":"W4319333499","doi":"10.1016/j.dib.2023.108921","title":"A synthetic dataset of liver disorder patients","year":2023,"lang":"en","type":"article","venue":"Data in Brief","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Bayesian network; Scripting language; Casual; Data set; Set (abstract data type); Artificial intelligence; Machine learning; Population; Synthetic data; Patient data; Data mining; Medicine; Database","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":[0.0004682028,0.00007059761,0.0001150672,0.0001255313,0.00003264265,0.00002720208,0.002148907,0.00003214923,0.00003276569],"category_scores_gemma":[0.0005449689,0.00006816434,0.000008941824,0.0006367401,0.00003236339,0.000395976,0.002125016,0.0001359836,0.0001836836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001193553,"about_ca_system_score_gemma":0.00003567385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002818255,"about_ca_topic_score_gemma":0.0003297583,"domain_scores_codex":[0.9987801,0.0001151518,0.0002351551,0.0004119647,0.0002533704,0.0002042198],"domain_scores_gemma":[0.9974788,0.000185585,0.00007664263,0.002190806,0.00002601193,0.00004211752],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000008977473,0.000227816,0.6442338,0.0003095305,0.000009747157,0.00005528998,0.00085328,0.0002978238,0.000005070397,0.005120954,0.1345673,0.2143104],"study_design_scores_gemma":[0.0002655333,0.00004617453,0.5574676,0.00005620478,0.000001934927,0.000001621346,0.000007110724,0.2352943,0.000003905884,0.0001762425,0.2065617,0.0001177463],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8628137,0.0007170585,0.04997037,0.007676196,0.002087045,0.001945402,0.07278503,0.001015207,0.0009900151],"genre_scores_gemma":[0.9585977,0.00009037605,0.00983035,0.0005400251,0.00003358621,0.00001774151,0.0308141,0.00001958861,0.00005652877],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2349965,"threshold_uncertainty_score":0.4260378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03964919403901191,"score_gpt":0.3172690331896421,"score_spread":0.2776198391506302,"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."}}