{"id":"W4402900266","doi":"10.3390/diagnostics14192151","title":"Hospital Re-Admission Prediction Using Named Entity Recognition and Explainable Machine Learning","year":2024,"lang":"en","type":"article","venue":"Diagnostics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Transformer; Machine learning; Gradient boosting; Encoder; Boosting (machine learning); Named-entity recognition; Emergency department; Medicine; Engineering; Random forest","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":[0.0004282799,0.0001385828,0.0001229,0.0001523472,0.0003040455,0.0003468099,0.0001640489,0.0001018202,0.00003521669],"category_scores_gemma":[0.001943496,0.0001390438,0.00003389965,0.0003381333,0.00002724914,0.0005490784,0.0002132963,0.0005140397,0.00003681584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001019887,"about_ca_system_score_gemma":0.00007480511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003424647,"about_ca_topic_score_gemma":0.000007441816,"domain_scores_codex":[0.9986769,0.0001635446,0.0002175179,0.0004275946,0.0002558481,0.0002586028],"domain_scores_gemma":[0.9989303,0.000568254,0.00006357265,0.000200859,0.00009193458,0.0001451128],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002001485,0.0002486568,0.5007819,0.001906665,0.00007106637,0.0007550634,0.01152172,0.004706651,0.00051268,0.004477332,0.003226468,0.4717718],"study_design_scores_gemma":[0.0001419925,0.0002658973,0.00727672,0.000465365,0.00001986379,0.00003754714,0.00007056953,0.9791328,0.0002431487,0.002615611,0.00953974,0.0001907002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7269539,0.004786809,0.2623286,0.001722132,0.002398689,0.0003938338,0.00003645819,0.001043075,0.0003365299],"genre_scores_gemma":[0.9805603,0.001110856,0.0177729,0.00008439347,0.0002419615,0.00001643987,0.00008393689,0.00002401498,0.0001051836],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9744262,"threshold_uncertainty_score":0.5670042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03194079163786902,"score_gpt":0.2788225185716251,"score_spread":0.2468817269337561,"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."}}