{"id":"W2902761513","doi":"10.12688/gatesopenres.12891.2","title":"Automated verbal autopsy classification: using one-against-all ensemble method and Naïve Bayes classifier","year":2019,"lang":"en","type":"preprint","venue":"Gates Open Research","topic":"Topic Modeling","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; University of Toronto; Centre for Global Health Research; St. Michael's Hospital; Toronto Metropolitan University","funders":"Bill and Melinda Gates Foundation","keywords":"Verbal autopsy; Artificial intelligence; Naive Bayes classifier; Classifier (UML); Pattern recognition (psychology); Bayes' theorem; Computer science; Natural language processing; Bayesian probability; Support vector machine; Medicine; Pathology; Cause of death","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":[0.005591487,0.002249657,0.002662346,0.003419275,0.001269724,0.001423714,0.001878242,0.001686407,0.001378634],"category_scores_gemma":[0.008550521,0.0003959111,0.001663464,0.001539958,0.0003722463,0.001088831,0.001037489,0.001596781,0.0008606386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006819015,"about_ca_system_score_gemma":0.001704991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0119602,"about_ca_topic_score_gemma":0.009832357,"domain_scores_codex":[0.9967276,0.001074755,0.0003845636,0.0008355124,0.0006516304,0.0003259347],"domain_scores_gemma":[0.9953282,0.0022103,0.0002735478,0.0004326048,0.001587636,0.0001677101],"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":[0.000846519,0.0006657552,0.03742037,0.0002417076,0.0007179717,0.0003335432,0.0002065675,0.1264913,0.004336434,0.00105064,0.009734801,0.8179545],"study_design_scores_gemma":[0.00004218351,0.0002623943,0.004888895,0.00005947546,0.0002385352,0.0001687242,0.0001174606,0.9862043,0.003715985,0.002356006,0.001902198,0.00004383037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3634534,0.004874005,0.6143865,0.001052281,0.0009893345,0.0007306641,0.002261095,0.006190636,0.006062172],"genre_scores_gemma":[0.7716097,0.00066006,0.2180061,0.0004419247,0.0004126819,0.0002959961,0.005092261,0.0001613397,0.003320028],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0119602,"threshold_uncertainty_score":0.02957094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4047329856759461,"score_gpt":0.4866043839533529,"score_spread":0.08187139827740686,"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."}}