{"id":"W2171188883","doi":"10.1109/cbms.2007.79","title":"Ontology Engineering to Model Clinical Pathways: Towards the Computerization and Execution of Clinical Pathways","year":2007,"lang":"en","type":"article","venue":"Proceedings - IEEE Symposium on Computer-Based Medical Systems","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Ontology; Computer science; Clinical pathway; Abstraction; Software engineering; Process (computing); Knowledge management; Data science; Medicine; Programming language","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":[0.004572091,0.0007053696,0.0005584328,0.003359125,0.001301172,0.004126681,0.001716741,0.001164346,0.001670821],"category_scores_gemma":[0.01777381,0.0006286806,0.002178661,0.003473612,0.001626649,0.004168666,0.002514209,0.002069751,0.0005177327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002872397,"about_ca_system_score_gemma":0.007979758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0267055,"about_ca_topic_score_gemma":0.02803618,"domain_scores_codex":[0.9970017,0.001345802,0.0004665725,0.000435217,0.0006294486,0.0001212147],"domain_scores_gemma":[0.9928924,0.004033148,0.0006790939,0.001090226,0.00111234,0.0001928842],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001831248,0.0003019043,0.01081793,0.001115719,0.0003352732,0.0008121104,0.006408883,0.2974799,0.004599327,0.3870112,0.009122703,0.2818118],"study_design_scores_gemma":[0.00007844662,0.00006248205,0.001300697,0.0005129757,0.0001770413,0.0003201582,0.001417294,0.6104072,0.0048047,0.3192078,0.06163577,0.00007539361],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006317702,0.0001235096,0.9887612,0.0009406003,0.00002948307,0.0003844952,0.0008255958,0.0007960485,0.001821282],"genre_scores_gemma":[0.04767914,0.0002753283,0.9491746,0.0001296491,0.00001081399,0.0004416379,0.001687663,0.00008811829,0.000512973],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0267055,"threshold_uncertainty_score":0.05310011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06430275580445946,"score_gpt":0.3236194644948927,"score_spread":0.2593167086904332,"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."}}