{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001208993,0.0004002542,0.0004787972,0.0005497694,0.0002837401,0.0001869199,0.0005925368,0.0002842346,0.00002923631],"category_scores_gemma":[0.0001006477,0.0004005376,0.0001021162,0.0007769628,0.0001039207,0.002175296,0.0003894713,0.001519289,0.000006903602],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005946969,"about_ca_system_score_gemma":0.0003746191,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01997145,"about_ca_topic_score_gemma":0.000581529,"domain_scores_codex":[0.9977545,0.0003489919,0.0002434769,0.0009032777,0.0004319405,0.000317834],"domain_scores_gemma":[0.9983583,0.0004124085,0.0004813268,0.0004057082,0.0001523071,0.0001899119],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000383475,0.0001453792,0.9666529,0.002905584,0.000516002,0.0003182097,0.01305964,0.004782622,0.00001168382,0.005890439,0.001466702,0.003867302],"study_design_scores_gemma":[0.001615861,0.003138889,0.5581643,0.008388203,0.001513134,0.000009679948,0.006310682,0.4099068,0.001063141,0.004419935,0.003610112,0.001859257],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9417144,0.0008119495,0.03403464,0.003582727,0.0005760106,0.001232199,0.0009419061,0.001770615,0.01533554],"genre_scores_gemma":[0.96308,0.0007964525,0.002910784,0.00001917034,0.0000345081,0.000002773224,0.0006455032,0.00005916434,0.03245167],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4084887,"threshold_uncertainty_score":0.9998447,"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."}}