{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02188842,0.001895491,0.00316898,0.01432951,0.001741921,0.006240265,0.005007929,0.002041109,0.01826382],"category_scores_gemma":[0.04792731,0.00189931,0.001579616,0.01769842,0.0009351417,0.008430618,0.008332783,0.003502355,0.01954192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002209112,"about_ca_system_score_gemma":0.009503759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00831099,"about_ca_topic_score_gemma":0.005649961,"domain_scores_codex":[0.9861156,0.00436956,0.003240589,0.002284141,0.003555965,0.0004341354],"domain_scores_gemma":[0.9473661,0.01815126,0.006353445,0.01746593,0.008036794,0.002626494],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001138468,0.0005424481,0.01867103,0.004407877,0.001010109,0.0006050667,0.001782733,0.004502926,0.008401948,0.02936752,0.4893925,0.4401773],"study_design_scores_gemma":[0.000775709,0.0004330485,0.03259782,0.001192845,0.0008088845,0.001177872,0.0007661448,0.03801094,0.01833862,0.02782635,0.8774756,0.0005961918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006008756,0.002517431,0.4961223,0.003169491,0.000481191,0.003376642,0.2275648,0.2393873,0.0213721],"genre_scores_gemma":[0.05499663,0.003975084,0.598652,0.0019103,0.0009010038,0.005373138,0.3141001,0.01071718,0.009374628],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02188842,"threshold_uncertainty_score":0.1157584,"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."}}