{"id":"W3121063510","doi":"10.1002/mcda.1732","title":"Application of spatial multicriteria decision analysis in healthcare: Identifying drivers and triggers of infectious disease outbreaks using ensemble learning","year":2021,"lang":"en","type":"article","venue":"Journal of Multi-Criteria Decision Analysis","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"National Oceanic and Atmospheric Administration; Illinois Department of Transportation; Mississippi State University; U.S. Department of Transportation","keywords":"Multiple-criteria decision analysis; Geospatial analysis; Decision tree; Computer science; Ensemble learning; Weighting; Machine learning; Learning vector quantization; Artificial intelligence; Geographic information system; Support vector machine; Infectious disease (medical specialty); Artificial neural network; Geography; Disease; Cartography; Operations research; Mathematics; 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":[0.0041518,0.0007477404,0.001087644,0.001945485,0.000465751,0.001346253,0.0007530416,0.0008198297,0.0007825128],"category_scores_gemma":[0.007067609,0.000296336,0.001174404,0.001270632,0.0002992644,0.0009377228,0.001022253,0.0008361301,0.0001085882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008622117,"about_ca_system_score_gemma":0.0007111934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009364295,"about_ca_topic_score_gemma":0.005602213,"domain_scores_codex":[0.9986373,0.0008165534,0.00008130406,0.0002249824,0.0001480309,0.00009189449],"domain_scores_gemma":[0.9952734,0.003550647,0.0003281175,0.0002227348,0.0004874742,0.000137525],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001324959,0.0001506626,0.04134192,0.0000529483,0.0003884652,0.000098948,0.0001109983,0.894895,0.000532375,0.001572852,0.0005604888,0.06016289],"study_design_scores_gemma":[0.000002094179,0.00002082396,0.001251877,0.000005755859,0.00001755718,0.000008311899,0.00002852542,0.9973677,0.0001137718,0.001112725,0.00006640365,0.000004500624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5031999,0.000711697,0.4914244,0.001544763,0.0001289119,0.0001109823,0.0003270366,0.0002838877,0.002268281],"genre_scores_gemma":[0.9710404,0.0001378317,0.02832965,0.00005114329,0.00003717597,0.00002569896,0.0001331837,0.000007399789,0.0002376671],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009364295,"threshold_uncertainty_score":0.0219571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1420549737841562,"score_gpt":0.4577482133799339,"score_spread":0.3156932395957777,"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."}}