{"id":"W4399070329","doi":"10.1038/s41598-024-62567-1","title":"ClinicalGAN: powering patient monitoring in clinical trials with patient digital twins","year":2024,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Novartis Pharmaceuticals Corporation; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; National Institute on Aging; Alzheimer's Association","keywords":"Computer science; Key (lock); Generative model; Clinical trial; Machine learning; Artificial intelligence; Generative grammar; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.01125016,0.0002473235,0.0005982107,0.0003936674,0.0001981663,0.002542272,0.0004749239,0.0001346443,0.00001848831],"category_scores_gemma":[0.004128001,0.0001844355,0.0002628542,0.001409534,0.0001722383,0.0009367572,0.0005119365,0.0007954149,0.00006120346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001413411,"about_ca_system_score_gemma":0.0005945987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004264807,"about_ca_topic_score_gemma":0.00001016679,"domain_scores_codex":[0.9927499,0.0006051272,0.003011596,0.00188671,0.001152882,0.0005937566],"domain_scores_gemma":[0.9958349,0.001302878,0.0006329232,0.001705551,0.000177962,0.000345822],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002415102,0.0002254091,0.2750893,0.0001160912,0.00003677252,0.007673817,0.003143043,0.001363346,0.00007475853,0.0005387864,0.00136393,0.7103506],"study_design_scores_gemma":[0.001496309,0.004618939,0.1080032,0.007612313,0.00008370828,0.004141357,0.001378518,0.1537392,0.003837517,0.02333697,0.6882311,0.003520842],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9478002,0.0006938151,0.009027124,0.000728777,0.039151,0.0007362933,0.000002207628,0.0005339651,0.001326613],"genre_scores_gemma":[0.9938325,0.000006103337,0.005582153,0.00003644162,0.000230768,0.00004277494,0.000007329917,0.00002615286,0.0002357804],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7068297,"threshold_uncertainty_score":0.9984932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08468100225353671,"score_gpt":0.4142625346760938,"score_spread":0.3295815324225571,"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."}}