{"id":"W4386707630","doi":"10.32920/24132873","title":"Heterogeneous Patient Graph Embedding in Readmission Prediction","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; McMaster University; Hamilton Health Sciences","keywords":"Embedding; Computer science; Graph; Machine learning; Mental health; Emergency department; Graph embedding; Artificial intelligence; Mental healthcare; Context (archaeology); Health care; Medical record; Recurrent neural network; Artificial neural network; Medicine; Theoretical computer science; Psychiatry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004506216,0.0002570582,0.0002705655,0.0005481399,0.00009115331,0.0001550583,0.0009062964,0.0003363297,0.00002961025],"category_scores_gemma":[0.0001291435,0.0002436875,0.0001106954,0.0004158371,0.00001282162,0.0001035665,0.00246891,0.001095407,0.00005083416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002060314,"about_ca_system_score_gemma":0.0001255875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001047322,"about_ca_topic_score_gemma":0.00008226932,"domain_scores_codex":[0.9972496,0.0002647958,0.000567467,0.00103863,0.0004930815,0.0003864202],"domain_scores_gemma":[0.9982549,0.0001191966,0.0002067833,0.001173076,0.00007862013,0.0001674544],"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":[0.00001365886,0.00009061135,0.03441935,0.0004568076,0.00002197644,0.0002955708,0.003875245,0.8241502,0.00005305161,0.001840872,0.004018739,0.1307639],"study_design_scores_gemma":[0.0001217706,0.0001359001,0.007789427,0.0006211381,0.000002153593,0.00002574389,0.00002364992,0.9770008,0.0001879712,0.01212474,0.001647773,0.0003188792],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2735753,0.0006921313,0.7011609,0.005600657,0.01027699,0.001791072,0.00002887358,0.004716877,0.002157186],"genre_scores_gemma":[0.9478282,0.0002329043,0.0508223,0.0002874073,0.0001421348,0.0001655273,0.00006890481,0.0000488301,0.0004037539],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6742529,"threshold_uncertainty_score":0.9937288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03983277650499736,"score_gpt":0.3233241191514278,"score_spread":0.2834913426464304,"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."}}