{"id":"W3106978254","doi":"10.1109/access.2020.3042342","title":"Heath-PRIOR: An Intelligent Ensemble Architecture to Identify Risk Cases in Healthcare","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Universidade Federal de Juiz de Fora; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Government of Canada","keywords":"Computer science; Context (archaeology); Architecture; Recommender system; Health care; Ensemble learning; Quality (philosophy); Machine learning; Predictive analytics; Artificial intelligence; Prioritization; Risk analysis (engineering); Process management","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001912979,0.001001883,0.001316053,0.00200373,0.0008125653,0.0011498,0.00194273,0.001386247,0.002439731],"category_scores_gemma":[0.004184022,0.0005949824,0.001518136,0.001190774,0.0003450525,0.00177654,0.001482997,0.001288999,0.0008341403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009811415,"about_ca_system_score_gemma":0.001398187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02478258,"about_ca_topic_score_gemma":0.03842216,"domain_scores_codex":[0.9992658,0.0001944885,0.00006298359,0.0002116557,0.0001749668,0.00009005118],"domain_scores_gemma":[0.9988927,0.0004561925,0.00007073367,0.0001558576,0.0003428244,0.00008172628],"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":[0.0005436151,0.0006479626,0.02464948,0.0002135645,0.001035949,0.0004158622,0.0004424333,0.3626959,0.003954019,0.005515602,0.01385302,0.5860326],"study_design_scores_gemma":[0.00001147044,0.00008501583,0.001820504,0.00002544389,0.0001235717,0.00006945404,0.00003413694,0.9908327,0.0008818826,0.004117509,0.001980708,0.00001757735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06842472,0.002466136,0.9152726,0.001573883,0.0002891601,0.0002782696,0.001069329,0.005579565,0.005046291],"genre_scores_gemma":[0.732769,0.001684419,0.2550417,0.0006192028,0.0002960748,0.0003001345,0.00251696,0.0001786305,0.006593952],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02478258,"threshold_uncertainty_score":0.04927665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08378344210887977,"score_gpt":0.4147240099834501,"score_spread":0.3309405678745703,"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."}}