{"id":"W6920405760","doi":"10.60692/yn6ve-90w04","title":"Handling Irregularly Sampled Longitudinal Data and Prognostic Modeling of Diabetes Using Machine Learning Technique","year":2020,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; University of Toronto; Toronto Metropolitan University","funders":"","keywords":"Hidden Markov model; Interval (graph theory); Polynomial; Sample (material); Identification (biology); Health records; Confidence interval; Component (thermodynamics)","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":[0.001425316,0.0003204125,0.0005576363,0.0007274117,0.0002844615,0.0004991392,0.0006725877,0.0004879892,0.0004809808],"category_scores_gemma":[0.004271241,0.0001905761,0.0004975909,0.0006997572,0.0002626537,0.0006554739,0.0005559365,0.0008617443,0.0001228639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003901874,"about_ca_system_score_gemma":0.0006275336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005811019,"about_ca_topic_score_gemma":0.003431975,"domain_scores_codex":[0.9995229,0.0001510055,0.00005045317,0.0001157031,0.0001080494,0.00005191264],"domain_scores_gemma":[0.9985451,0.0008991979,0.000188578,0.0001545583,0.0001718781,0.00004074878],"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":[0.0001686416,0.00009473431,0.02646358,0.00007864139,0.00008598431,0.0002656251,0.000188216,0.7742572,0.003946678,0.004439681,0.0007876106,0.1892233],"study_design_scores_gemma":[0.000001543988,0.000009492404,0.0007928925,0.000002462521,0.000003612489,0.00001704938,0.000007037478,0.9977624,0.000304955,0.0009691033,0.0001266575,0.00000282745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06794515,0.0002937386,0.9308357,0.0002109891,0.00003853078,0.00001647612,0.00009265719,0.000291824,0.0002749201],"genre_scores_gemma":[0.8613194,0.0003483725,0.1371229,0.00007330026,0.00006464587,0.0000544119,0.0003524625,0.00002024733,0.000644327],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005811019,"threshold_uncertainty_score":0.01155436,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1110205277219933,"score_gpt":0.267532279022184,"score_spread":0.1565117513001906,"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."}}