{"id":"W4395078638","doi":"10.18280/ria.380234","title":"Predicting Emergency Healthcare Requirements Using Deep Learning","year":2024,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Health care; Computer science; Deep learning; Artificial intelligence; Medical emergency; Medicine; Political science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004910799,0.0005807625,0.0002535723,0.0008126384,0.0001898653,0.0006631457,0.0005285305,0.0007524893,0.001874994],"category_scores_gemma":[0.003232935,0.0002841455,0.0003220469,0.0005684843,0.0001676483,0.001184355,0.0006828477,0.0009247612,0.0003918062],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007483138,"about_ca_system_score_gemma":0.0009396471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01012958,"about_ca_topic_score_gemma":0.01634133,"domain_scores_codex":[0.9997019,0.00007572992,0.0000249828,0.00006211203,0.00007605823,0.00005914327],"domain_scores_gemma":[0.9991443,0.0004711449,0.0001228277,0.00003727765,0.0001686793,0.00005577401],"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":[0.0002084092,0.0003973303,0.03760759,0.0001430591,0.00008384672,0.0003108096,0.0001477693,0.8147533,0.003333052,0.004074541,0.007599138,0.1313411],"study_design_scores_gemma":[0.000002678785,0.00001805164,0.001506023,0.000008200844,0.000004140959,0.00001678063,0.00002955119,0.9955712,0.000494101,0.001886893,0.0004580474,0.000004327224],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5479313,0.0009865368,0.4307289,0.004108456,0.0002202289,0.0001457281,0.002946674,0.001578442,0.0113536],"genre_scores_gemma":[0.9701015,0.000284943,0.02579602,0.0002030485,0.0000437831,0.0000399319,0.001472789,0.00002298688,0.002035015],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01012958,"threshold_uncertainty_score":0.02014124,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2558333569115681,"score_gpt":0.4880091448392039,"score_spread":0.2321757879276359,"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."}}