{"id":"W4386958496","doi":"10.17975/sfj-2023-007","title":"Leveraging open data analytics and machine learning to improve diagnosis of diseases, patients’ care, and support: Proceedings from the 2023 Inter-University Big Data and AI Challenge","year":2023,"lang":"en","type":"article","venue":"STEM Fellowship Journal","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Big data; Computer science; Health care; Data science; Artificial intelligence; Repurposing; Analytics; Experiential learning; Learning analytics; Knowledge management; Psychology; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.03072401,0.001371789,0.0007287199,0.001350485,0.004977946,0.01422074,0.003081728,0.004023032,0.009176141],"category_scores_gemma":[0.02059511,0.0004656656,0.0012856,0.001129796,0.004656089,0.005802907,0.01110066,0.01336868,0.004308508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003578161,"about_ca_system_score_gemma":0.01888107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006970242,"about_ca_topic_score_gemma":0.01522398,"domain_scores_codex":[0.9897286,0.003142647,0.0002512293,0.0007263739,0.004102187,0.002049034],"domain_scores_gemma":[0.9669346,0.006097518,0.0004269271,0.00124709,0.008001369,0.01729245],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001107324,0.0006028832,0.0009959927,0.0002022218,0.0000391258,0.0003379312,0.00260317,0.0007108061,0.0008376617,0.01404391,0.9078388,0.07167666],"study_design_scores_gemma":[0.0000878581,0.0002408818,0.002970952,0.0004733383,0.00003062188,0.0001889599,0.006714367,0.002910626,0.001767986,0.0262685,0.9582109,0.0001348485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.02658311,0.006691616,0.02337329,0.8157254,0.07452393,0.0007013589,0.001499642,0.001534857,0.0493668],"genre_scores_gemma":[0.2646725,0.03559932,0.1041659,0.1837886,0.07458101,0.002319354,0.01105667,0.003782277,0.3200344],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03072401,"threshold_uncertainty_score":0.162486,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07453576370796039,"score_gpt":0.2942421833955232,"score_spread":0.2197064196875628,"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."}}