{"id":"W3159309869","doi":"10.2196/28272","title":"Document Retrieval for Precision Medicine Using a Deep Learning Ensemble Method","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China","keywords":"Computer science; Information retrieval; Query expansion; Relevance (law); Ranking (information retrieval); Boosting (machine learning); Learning to rank; Document retrieval; Matching (statistics); Relevance feedback; Context (archaeology); Search engine; Artificial intelligence; Data mining; Image retrieval","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002816596,0.001395169,0.002104108,0.005441757,0.0006429272,0.001236962,0.001243807,0.001457074,0.002154103],"category_scores_gemma":[0.004703765,0.0003186654,0.001788964,0.003549921,0.0002933751,0.001480985,0.0009300254,0.001190836,0.001305922],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001060745,"about_ca_system_score_gemma":0.001525156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007452096,"about_ca_topic_score_gemma":0.006671105,"domain_scores_codex":[0.9981357,0.0003777077,0.0002119653,0.0004217403,0.0006716901,0.000181193],"domain_scores_gemma":[0.9982646,0.000590076,0.0001440176,0.000228258,0.0007134026,0.00005966585],"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":[0.0003845054,0.0002734825,0.003604111,0.0002010435,0.0003490757,0.0001017879,0.00007388808,0.07637226,0.009746783,0.002008143,0.008832104,0.8980529],"study_design_scores_gemma":[0.00003272917,0.0001275285,0.001361432,0.00002308405,0.0001492268,0.0001208925,0.00001918949,0.989122,0.004504061,0.00227214,0.002243063,0.00002466213],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06400369,0.009032402,0.9157729,0.00074254,0.0003871357,0.0003285638,0.001153778,0.004653653,0.003925295],"genre_scores_gemma":[0.5745583,0.002939713,0.4114874,0.0004367515,0.0007555678,0.0003461246,0.003450798,0.0001670741,0.005858301],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007452096,"threshold_uncertainty_score":0.0148958,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04199195534955583,"score_gpt":0.3853112491381145,"score_spread":0.3433192937885587,"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."}}