{"id":"W4366526020","doi":"10.1016/j.artmed.2023.102526","title":"RIMD: A novel method for clinical prediction","year":2023,"lang":"en","type":"article","venue":"Artificial Intelligence in Medicine","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Timestamp; Modular design; Machine learning; Deep learning; Identification (biology); Function (biology)","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":[0.002989848,0.001168898,0.001526299,0.003398976,0.000506853,0.001829187,0.001866386,0.001393405,0.006295325],"category_scores_gemma":[0.01113978,0.0005384139,0.001276802,0.001718535,0.0003582604,0.001193967,0.002014602,0.001596321,0.004694302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005147588,"about_ca_system_score_gemma":0.001996322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003033852,"about_ca_topic_score_gemma":0.003747688,"domain_scores_codex":[0.9980705,0.0004721108,0.0001992745,0.000435554,0.0007128131,0.0001097153],"domain_scores_gemma":[0.9968598,0.001649038,0.0001823924,0.0005197546,0.0006144857,0.0001745445],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005370749,0.0002363534,0.009709369,0.0002619356,0.0002145702,0.0002315345,0.00006525512,0.01748358,0.003810682,0.003969074,0.04262997,0.9208507],"study_design_scores_gemma":[0.0001478964,0.0001865113,0.003695596,0.0001033149,0.0001412268,0.0007752667,0.00003530507,0.947052,0.007361733,0.01140124,0.02902208,0.00007776199],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008191678,0.001432113,0.9704014,0.0007017289,0.0005659585,0.0002675653,0.003648912,0.01227021,0.002520452],"genre_scores_gemma":[0.1366646,0.0009318238,0.8456594,0.0007055929,0.0006305468,0.0005632233,0.007016119,0.0005763898,0.007252281],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006295325,"threshold_uncertainty_score":0.02105993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3202670903476609,"score_gpt":0.5364371720074873,"score_spread":0.2161700816598264,"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."}}