{"id":"W2973296562","doi":"10.1177/2327857919081027","title":"The Challenges of Data Visualization for Precision Medicine","year":2019,"lang":"en","type":"article","venue":"Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Visualization; Precision medicine; Data science; Computer science; Data visualization; Artificial intelligence; 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":[0.0008591237,0.0001055314,0.0001928649,0.00008137488,0.0001499596,0.00003539024,0.001821887,0.00004856227,0.000001519129],"category_scores_gemma":[0.0001920771,0.00006330429,0.00002984272,0.00007267925,0.0000492697,0.0002251713,0.0005392124,0.0001316645,1.916835e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001529686,"about_ca_system_score_gemma":0.00004961447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003078574,"about_ca_topic_score_gemma":0.0001325422,"domain_scores_codex":[0.9987251,0.0000175689,0.0005071206,0.000332905,0.0002764571,0.000140907],"domain_scores_gemma":[0.9984074,0.0004155111,0.0005670121,0.0002957129,0.0002770224,0.00003736619],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0001188309,0.00003050468,0.09549668,0.0009582441,0.00001793301,1.144334e-8,0.008724041,0.000179217,0.0004250197,0.8875467,0.0003494744,0.00615334],"study_design_scores_gemma":[0.00455009,0.005157053,0.7043552,0.006922959,0.00002265719,0.000007373072,0.01386277,0.1680613,0.005284108,0.03143478,0.05944717,0.0008945592],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9819536,0.00109515,0.00009381055,0.01405372,0.001110564,0.0007985547,0.00003232745,0.00001861164,0.0008436437],"genre_scores_gemma":[0.998668,0.000749688,0.0003135526,0.000131582,0.0000753796,0.00001027121,0.00001212596,0.000009572097,0.00002988835],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8561119,"threshold_uncertainty_score":0.3385549,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0635736490025035,"score_gpt":0.3707679763537129,"score_spread":0.3071943273512094,"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."}}