{"id":"W2980775795","doi":"10.2196/14971","title":"Generating Medical Assessments Using a Neural Network Model: Algorithm Development and Validation","year":2019,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. National Library of Medicine; National Institute on Drug Abuse; National Institute of Mental Health; National Heart, Lung, and Blood Institute","keywords":"Medical diagnosis; Computer science; Machine learning; Artificial intelligence; Feature engineering; Clinical decision support system; Artificial neural network; Expert system; Baseline (sea); Domain (mathematical analysis); Subject-matter expert; Inference; F1 score; Precision and recall; Decision support system; Deep learning; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.002997055,0.001628241,0.00082212,0.0009472161,0.0004125062,0.000901157,0.001828333,0.00206469,0.003144021],"category_scores_gemma":[0.009724443,0.0005526435,0.0006442238,0.0006163333,0.0004706063,0.0009694111,0.001070067,0.002424949,0.0006571737],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002201643,"about_ca_system_score_gemma":0.002094486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02624913,"about_ca_topic_score_gemma":0.02356653,"domain_scores_codex":[0.9992667,0.0002875208,0.00006112589,0.000178515,0.0001325048,0.00007374034],"domain_scores_gemma":[0.9937377,0.004611075,0.0001898873,0.0002736355,0.001070438,0.0001172197],"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":[0.0002060412,0.0002321719,0.003068017,0.0001191591,0.0000829336,0.00008403115,0.00004974922,0.8776577,0.0009351276,0.0009321559,0.002144053,0.1144889],"study_design_scores_gemma":[0.00001300704,0.00002203975,0.0001330584,0.000007843412,0.000006247978,0.000006528969,0.000006638917,0.9988772,0.0004148817,0.0004144143,0.00009527943,0.000002905231],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3552522,0.00273008,0.6213461,0.001830364,0.0002736688,0.001217801,0.001429488,0.007625563,0.008294702],"genre_scores_gemma":[0.7333321,0.0004515052,0.2601108,0.0004523579,0.00004934691,0.0007317325,0.001868266,0.0001387723,0.002865126],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02624913,"threshold_uncertainty_score":0.05219269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03565107294944424,"score_gpt":0.3622599187673303,"score_spread":0.326608845817886,"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."}}