{"id":"W4281620354","doi":"10.3233/shti220086","title":"OpenMRS Analytics Engine: A FHIR Based Approach","year":2022,"lang":"en","type":"article","venue":"Studies in health technology and informatics","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Google (Canada)","funders":"","keywords":"Analytics; Interoperability; Scalability; Computer science; Pipeline (software); Data science; Architecture; Database; World Wide Web; Operating system","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":[0.008993055,0.0001114559,0.0003565383,0.001700471,0.0008125274,0.00005710521,0.0008557982,0.00003880368,0.000023328],"category_scores_gemma":[0.002171242,0.00008866285,0.00002650596,0.003741562,0.0003585912,0.0001475383,0.002022762,0.0003729874,0.000008778974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001233532,"about_ca_system_score_gemma":0.0001226462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008308416,"about_ca_topic_score_gemma":0.00000668603,"domain_scores_codex":[0.9974961,0.0001052207,0.001144479,0.0002588891,0.0006413006,0.0003540287],"domain_scores_gemma":[0.9982831,0.0004644009,0.0003560146,0.0007611742,0.00008968733,0.00004558106],"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":[0.00004899384,0.0003406552,0.03360918,0.000668454,0.00009711557,0.00001400105,0.014522,0.1247392,2.310121e-7,0.118358,0.2613733,0.4462288],"study_design_scores_gemma":[0.0005542354,0.0002223784,0.0007832106,0.00002311991,0.000004556274,0.00001520389,0.1173324,0.4656286,0.000001199642,0.01078568,0.4044915,0.0001579013],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4808189,0.02464256,0.3287435,0.0890839,0.01099516,0.005438019,0.000375487,0.001603642,0.05829879],"genre_scores_gemma":[0.964711,0.0001808414,0.03204454,0.002391556,0.00001102836,0.00007942522,0.00001204499,0.000004860296,0.0005647749],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.483892,"threshold_uncertainty_score":0.6249386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2485296359890251,"score_gpt":0.4559431301719775,"score_spread":0.2074134941829525,"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."}}