{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006722798,0.0007865731,0.0004800631,0.001338315,0.0002318172,0.0004171851,0.0006762269,0.000824853,0.001335136],"category_scores_gemma":[0.004895774,0.0002883691,0.0005746993,0.001078352,0.0003402196,0.001099231,0.0005089064,0.001078812,0.0003267197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008059738,"about_ca_system_score_gemma":0.0006044256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01140611,"about_ca_topic_score_gemma":0.01788558,"domain_scores_codex":[0.9995857,0.0001896319,0.00002403246,0.0001139054,0.00004605881,0.00004054351],"domain_scores_gemma":[0.9978149,0.001462367,0.0002514047,0.0002155148,0.0001775822,0.00007815179],"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":[0.0003433428,0.000311727,0.03162719,0.0002012849,0.0001866439,0.0004377996,0.0001738112,0.7457301,0.001859392,0.004976751,0.00974146,0.2044106],"study_design_scores_gemma":[0.000007808561,0.00003118365,0.002238383,0.00001569874,0.00001883875,0.00005340892,0.00002596331,0.9902322,0.0004395212,0.006150174,0.0007787019,0.000008022535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4879672,0.002520071,0.4933167,0.002849869,0.0003344401,0.0001989166,0.006917864,0.002750886,0.003144101],"genre_scores_gemma":[0.9479908,0.0004367212,0.04504554,0.0002162085,0.00006984727,0.00006003058,0.004495824,0.00008351576,0.001601519],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01140611,"threshold_uncertainty_score":0.02267939,"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."}}