{"id":"W2183708446","doi":"","title":"Population Health Record: An Informatics Infrastructure for Management, Integration, and Analysis of Large Scale Population Health Data","year":2013,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Health informatics; Public health informatics; Population health; Data science; Population; Health indicator; Scale (ratio); Health care; Computer science; Informatics; Data integration; Public health; Data quality; Knowledge management; HRHIS; Environmental health; Data mining; Health policy; Business; Medicine; Geography; Engineering; Nursing; Marketing","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":[0.001512541,0.0001666858,0.0003507278,0.0006743004,0.0003219043,0.0002342847,0.0007636346,0.00008003291,0.00006656403],"category_scores_gemma":[0.0002363947,0.0001619242,0.00004181541,0.001051591,0.00003117816,0.001294013,0.0001577484,0.0001982408,0.000007383615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001563913,"about_ca_system_score_gemma":0.0001653252,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002023524,"about_ca_topic_score_gemma":0.003094662,"domain_scores_codex":[0.9973776,0.0002137945,0.001077231,0.0004429347,0.0006080812,0.0002803064],"domain_scores_gemma":[0.9975718,0.0001598643,0.0007786289,0.0006223054,0.0007126111,0.0001548297],"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":[0.00001058592,0.00005709308,0.01098174,0.00008888048,0.0000359137,2.561523e-8,0.001457028,0.006260878,0.000002610903,0.5432922,0.0002207709,0.4375923],"study_design_scores_gemma":[0.000027001,0.0001446539,0.1703547,0.00003854347,0.000009383665,3.542699e-7,0.0004492819,0.7541449,0.000009589407,0.0746382,0.00008161148,0.0001017966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03744408,0.00001492169,0.9565338,0.004631184,0.0001478172,0.0008690254,0.0001673004,0.00007422635,0.0001175649],"genre_scores_gemma":[0.8358657,0.00003474878,0.1601371,0.001531174,0.0000359989,0.00003474997,0.002338413,0.000006968437,0.00001515711],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7984216,"threshold_uncertainty_score":0.6603079,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1024855328483445,"score_gpt":0.4116339901271095,"score_spread":0.3091484572787649,"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."}}