{"id":"W7070712313","doi":"","title":"Prediction of Pneumonia Mortality Risk and Cognitive Test Scores With Interpretable Machine Learning Models","year":2024,"lang":"en","type":"dissertation","venue":"QSpace (Queen's University Library)","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Test set; Feature (linguistics); Test (biology); Feature engineering; Pruning; Gradient boosting; Relevance (law); Cognition","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.004134319,0.001069095,0.0007534009,0.001150637,0.0002643898,0.001778408,0.001165088,0.0008270899,0.0009651286],"category_scores_gemma":[0.01627151,0.0005277889,0.001305419,0.0007453124,0.0004442266,0.001540376,0.0009332969,0.002580248,0.000394903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001039793,"about_ca_system_score_gemma":0.001221342,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004461663,"about_ca_topic_score_gemma":0.005503623,"domain_scores_codex":[0.9986363,0.0006872659,0.0001037547,0.0002800298,0.0002069593,0.00008568596],"domain_scores_gemma":[0.9908733,0.006658825,0.0009159575,0.0008654059,0.0005700293,0.0001165036],"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":[0.0002161359,0.0003888377,0.05865113,0.0001787851,0.0003570507,0.0001989027,0.0002527868,0.7682849,0.001752397,0.006742802,0.002923444,0.160053],"study_design_scores_gemma":[0.0000128865,0.00007210939,0.003621869,0.0000333332,0.00002810722,0.00002816357,0.00003005422,0.9854138,0.000477485,0.009621057,0.0006454401,0.00001569016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2888602,0.001115469,0.7006564,0.00338914,0.0001259508,0.0002572256,0.00153657,0.001660786,0.00239821],"genre_scores_gemma":[0.8404968,0.0004546287,0.1558922,0.0003570631,0.00008514462,0.0001933683,0.001569023,0.00005290966,0.0008988326],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004461663,"threshold_uncertainty_score":0.02186459,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01078584357836686,"score_gpt":0.2115304854595774,"score_spread":0.2007446418812106,"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."}}