{"id":"W4401625984","doi":"10.20944/preprints202408.0966.v1","title":"Enhancing the Interpretability of Malaria and Typhoid Diagnosis with Explainable AI and Large Language Models","year":2024,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mount Royal University","funders":"","keywords":"Interpretability; Typhoid fever; Malaria; Computer science; Artificial intelligence; Plasmodium falciparum; Natural language processing; Medicine; Virology; Immunology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004640185,0.001299881,0.0005454971,0.001192224,0.000431897,0.003072814,0.001341212,0.001166153,0.003981761],"category_scores_gemma":[0.02798842,0.0004145186,0.001769919,0.0006074306,0.0005835751,0.003326776,0.001856443,0.003092025,0.001077429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001582149,"about_ca_system_score_gemma":0.001519378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01023263,"about_ca_topic_score_gemma":0.01149263,"domain_scores_codex":[0.9978377,0.001421092,0.0001022229,0.0003704532,0.0001636219,0.0001048597],"domain_scores_gemma":[0.9815395,0.0164291,0.0005914956,0.0005557809,0.0006834787,0.0002006203],"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":[0.001320333,0.0004945016,0.03491545,0.0009830999,0.0005768878,0.001186522,0.004432854,0.4912421,0.009608857,0.02241541,0.01931858,0.4135053],"study_design_scores_gemma":[0.00002382169,0.00004916707,0.001347838,0.00004439117,0.00005974898,0.00007008786,0.0001612761,0.9800811,0.001764348,0.0145948,0.001771017,0.00003254223],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2273893,0.001508196,0.7380417,0.009832857,0.0003321309,0.0003561736,0.003441511,0.01314871,0.005949401],"genre_scores_gemma":[0.825575,0.0003894516,0.1676179,0.0009028565,0.0001212294,0.0002009345,0.002801818,0.0003799789,0.002010721],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01023263,"threshold_uncertainty_score":0.02453995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03433271375113809,"score_gpt":0.3200210188595128,"score_spread":0.2856883051083747,"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."}}