{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005464129,0.001033664,0.001348012,0.002327021,0.0006377101,0.003695101,0.001494473,0.001917421,0.002800597],"category_scores_gemma":[0.01972698,0.0005824438,0.001074304,0.002141615,0.0008579072,0.002410773,0.00273985,0.003808644,0.001930674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009158463,"about_ca_system_score_gemma":0.002460457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001393973,"about_ca_topic_score_gemma":0.001543859,"domain_scores_codex":[0.9954547,0.002402822,0.000349581,0.000516286,0.001067446,0.0002091919],"domain_scores_gemma":[0.9847112,0.01125497,0.0007046227,0.001080039,0.001932107,0.0003170252],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002863681,0.0003754979,0.003088878,0.0005460563,0.0001506058,0.0002722573,0.0002393606,0.07921261,0.004381979,0.0229867,0.0145246,0.8739352],"study_design_scores_gemma":[0.00006330809,0.0001146376,0.001085455,0.000214856,0.00005620999,0.0002684932,0.0001169369,0.8644093,0.004314112,0.1140253,0.01528724,0.00004407574],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01145819,0.004058517,0.9744855,0.005514616,0.000202953,0.0001880621,0.0004168589,0.001127742,0.002547604],"genre_scores_gemma":[0.2363755,0.003071745,0.7552424,0.001000092,0.0006134443,0.0002183578,0.00137657,0.0001403996,0.00196153],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005464129,"threshold_uncertainty_score":0.0288974,"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."}}