{"id":"W3188218986","doi":"10.2196/28028","title":"Semisupervised Deep Learning Techniques for Predicting Acute Respiratory Distress Syndrome From Time-Series Clinical Data: Model Development and Validation Study","year":2021,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Sepsis Diagnosis and Treatment","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Science Foundation","keywords":"ARDS; Receiver operating characteristic; Artificial intelligence; Acute respiratory distress; Machine learning; Data set; Medicine; Test set; Deep learning; Computer science; Artificial neural network; Area under the curve; Internal medicine; Lung","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":[0.009210911,0.001544006,0.001007494,0.0009176051,0.000448861,0.0007223592,0.001402199,0.001281176,0.0007460639],"category_scores_gemma":[0.0153714,0.0004334329,0.001250073,0.0005770362,0.0006521135,0.000952296,0.001096307,0.0020046,0.0002869654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001478357,"about_ca_system_score_gemma":0.001883956,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01586532,"about_ca_topic_score_gemma":0.009990199,"domain_scores_codex":[0.9976172,0.001476997,0.0001746173,0.0003231311,0.0002509748,0.0001571636],"domain_scores_gemma":[0.9842487,0.01071677,0.0009409826,0.000987716,0.002862014,0.0002437038],"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":[0.0004651953,0.0009644946,0.02713278,0.0001675677,0.0004647031,0.0001275768,0.0001339559,0.8807898,0.00130352,0.0005689569,0.001618589,0.08626284],"study_design_scores_gemma":[0.0000108512,0.00008894091,0.001086722,0.000009169417,0.000012099,0.00001259575,0.000009594028,0.9982213,0.0003203649,0.000161555,0.00006200514,0.000004797103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8251184,0.002341289,0.1676372,0.0007988936,0.0001030152,0.0004025384,0.00078488,0.001113967,0.001699846],"genre_scores_gemma":[0.9614681,0.0002880175,0.03619555,0.0001253252,0.0000232709,0.000196415,0.001032995,0.00003013058,0.0006402141],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01586532,"threshold_uncertainty_score":0.04871255,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2841591100139797,"score_gpt":0.5046501617124768,"score_spread":0.2204910516984971,"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."}}