{"id":"W2282133093","doi":"10.1016/j.artmed.2016.02.001","title":"Evaluation of a machine learning capability for a clinical decision support system to enhance antimicrobial stewardship programs","year":2016,"lang":"en","type":"article","venue":"Artificial Intelligence in Medicine","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":73,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Antimicrobial stewardship; Medical prescription; Clinical decision support system; Medicine; Discontinuation; Stewardship (theology); Decision support system; Knowledge base; Baseline (sea); Computer science; Artificial intelligence; Machine learning; Intensive care medicine; Pharmacology; Antibiotic resistance; Internal 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":[0.002401292,0.0005336142,0.0003156806,0.0005082519,0.0003342971,0.001010486,0.0008455443,0.0008813358,0.004095806],"category_scores_gemma":[0.01330973,0.0001985973,0.0002763344,0.0003450642,0.0002512206,0.001070329,0.0004697252,0.0004804472,0.0006253425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005907504,"about_ca_system_score_gemma":0.001077494,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004089307,"about_ca_topic_score_gemma":0.001921885,"domain_scores_codex":[0.9990491,0.0003711793,0.0001375671,0.000174398,0.0002091852,0.00005865135],"domain_scores_gemma":[0.9882978,0.008428451,0.0003072752,0.0004306977,0.002104904,0.0004309379],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.02359977,0.01666593,0.06827379,0.001331764,0.0005430139,0.00108128,0.001262534,0.07534269,0.1135614,0.001965991,0.007047836,0.689324],"study_design_scores_gemma":[0.001547863,0.009583413,0.02689698,0.00009114196,0.0005389066,0.0003426395,0.0003528788,0.877305,0.07863627,0.0007622062,0.003869376,0.00007338816],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9707577,0.0001290751,0.02297443,0.0004943856,0.0001286697,0.0006288667,0.0003710524,0.001753546,0.002762269],"genre_scores_gemma":[0.9579687,0.00006724843,0.04022431,0.0001305371,0.00003376694,0.0001410467,0.0003229318,0.00003840415,0.001073009],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004095806,"threshold_uncertainty_score":0.0137018,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2776187580155525,"score_gpt":0.5722114721430539,"score_spread":0.2945927141275014,"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."}}