{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001358392,0.0001640702,0.000230733,0.0000710408,0.0001941232,0.0001741936,0.00059967,0.0002274536,0.0000631336],"category_scores_gemma":[0.0001146134,0.0001403509,0.00002597551,0.0002499551,0.0000370898,0.0006547429,0.0006546589,0.0006347615,0.00002423266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008699647,"about_ca_system_score_gemma":0.0007538059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001064006,"about_ca_topic_score_gemma":0.000002205687,"domain_scores_codex":[0.9966965,0.0001053208,0.0007765575,0.0001740071,0.001819368,0.0004282201],"domain_scores_gemma":[0.9987779,0.000139236,0.0002311916,0.0002926683,0.0000821042,0.0004769384],"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":[0.000002237857,0.00003562304,0.00650809,0.0003076629,0.0000224496,0.00001524926,0.005750337,0.107047,0.000001555173,0.002764602,0.0003503377,0.8771949],"study_design_scores_gemma":[0.0003220754,0.00003702508,0.0001919396,0.0001569855,0.000001943846,0.0000742335,0.00007190165,0.9981574,0.000006231505,0.0001088778,0.000712789,0.0001585552],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3868363,0.00001920327,0.6120353,0.0003024918,0.0003029098,0.0002015988,3.141295e-7,0.0001015031,0.0002003902],"genre_scores_gemma":[0.1011372,0.000007045386,0.8954548,0.003127725,0.0001884715,0.00002130918,0.00001827,0.00001205746,0.00003312772],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8911104,"threshold_uncertainty_score":0.5723343,"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."}}