{"id":"W4389300832","doi":"10.2196/52200","title":"Patient Phenotyping for Atopic Dermatitis With Transformers and Machine Learning: Algorithm Development and Validation Study","year":2023,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Dermatology and Skin Diseases","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Arthritis and Musculoskeletal and Skin Diseases; National Institutes of Health; University of Pennsylvania","keywords":"Atopic dermatitis; Machine learning; Medicine; Artificial intelligence; Recall; Boosting (machine learning); Electronic health record; Precision medicine; Classifier (UML); Computer science; Health care; Psychology; Pathology","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":[0.01099217,0.001086535,0.001066117,0.001450434,0.0005666858,0.00107219,0.001428869,0.00170913,0.001565717],"category_scores_gemma":[0.01869721,0.000295506,0.001095777,0.000905966,0.0004547659,0.0005704821,0.00101525,0.001577773,0.0005086578],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001193649,"about_ca_system_score_gemma":0.002411365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00762735,"about_ca_topic_score_gemma":0.005238099,"domain_scores_codex":[0.9963974,0.002135784,0.000291131,0.0005014696,0.0004348691,0.0002393015],"domain_scores_gemma":[0.9866166,0.00984697,0.0004353029,0.0008289674,0.002011956,0.0002602494],"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":[0.002173664,0.003071604,0.165996,0.0003580453,0.0009941449,0.0003656748,0.0002301169,0.3470428,0.002193445,0.001177911,0.008679186,0.4677175],"study_design_scores_gemma":[0.0001996322,0.0003739368,0.009092229,0.00003604267,0.00008662075,0.0001650811,0.00005097409,0.9876245,0.00112595,0.0005561494,0.0006764888,0.0000123731],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8352557,0.002483577,0.1538513,0.001141912,0.000206018,0.001225825,0.001350243,0.002257366,0.002227925],"genre_scores_gemma":[0.8608859,0.0003628825,0.1340704,0.0002979089,0.00004863884,0.0006002253,0.002743575,0.00004899381,0.0009414656],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01099217,"threshold_uncertainty_score":0.05813283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05062764757974563,"score_gpt":0.3684195719325032,"score_spread":0.3177919243527576,"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."}}