{"id":"W7062358473","doi":"","title":"Towards Automated Clinical Diagnostic Decision Support using Machine Learning","year":2022,"lang":"en","type":"dissertation","venue":"mediaTUM  (Technical University of Munich)","topic":"Advanced Power Generation Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Deutschen Schwindel- und Gleichgewichtszentrum; Servier; Pfizer; Novartis Pharmaceuticals Corporation; Eisai; U.S. Department of Defense; Meso Scale Diagnostics; Bundesministerium für Bildung und Forschung; Biogen; Bristol-Myers Squibb; Eli Lilly and Company; BioClinica; Alzheimer's Drug Discovery Foundation","keywords":"Decision support system; Clinical decision support system; Clinical Practice; Training set; Clinical decision making; Support vector machine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004810263,0.0003577137,0.0007510118,0.0004318338,0.0002370671,0.00001303327,0.001027434,0.0008449684,0.001406731],"category_scores_gemma":[0.001840406,0.0004541217,0.000302029,0.0005451594,0.0001554187,0.0001523937,0.0003885129,0.002015486,0.00003751933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003347168,"about_ca_system_score_gemma":0.0001491385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009141304,"about_ca_topic_score_gemma":0.0005066202,"domain_scores_codex":[0.9979013,0.0001264681,0.0006326996,0.0004397545,0.0005583855,0.000341416],"domain_scores_gemma":[0.9980242,0.0007914773,0.0003139155,0.0006228418,0.0001190373,0.0001285401],"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":[0.001450405,0.001432314,0.007376319,0.002001315,0.001458194,0.002321612,0.003154716,0.5519547,0.02350221,0.001764241,0.07193352,0.3316505],"study_design_scores_gemma":[0.004745532,0.001551549,0.01408492,0.000764166,0.001338174,0.00007241763,0.006379041,0.6273248,0.006691671,0.001219998,0.3323961,0.003431681],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9275652,0.005793322,0.03092464,0.0000965951,0.007185769,0.001409405,0.0004399882,0.01618554,0.01039954],"genre_scores_gemma":[0.8826256,0.01606407,0.09168959,0.00002800007,0.0001342192,0.000005244695,0.00601763,0.000276759,0.003158912],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3282188,"threshold_uncertainty_score":0.999791,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01528581635396725,"score_gpt":0.2834786523147879,"score_spread":0.2681928359608207,"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."}}