{"id":"W4285324645","doi":"10.2196/35396","title":"Diagnosis of Atrial Fibrillation Using Machine Learning With Wearable Devices After Cardiac Surgery: Algorithm Development Study","year":2022,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Atrial Fibrillation Management and Outcomes","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Japan Society for the Promotion of Science","keywords":"Photoplethysmogram; Atrial fibrillation; Medicine; Wearable computer; Cardiology; Pulse (music); Internal medicine; Algorithm; Computer science; Computer vision; Telecommunications; Embedded system","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":[0.002683526,0.0001380889,0.0004715828,0.0006314263,0.0005669256,0.00004003833,0.00006952989,0.00002852727,0.0004373938],"category_scores_gemma":[0.00007599,0.000110292,0.0001610846,0.001094724,0.00005439879,0.0002799948,0.0003177719,0.0003326489,0.00001375179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002943908,"about_ca_system_score_gemma":0.0002374792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002061539,"about_ca_topic_score_gemma":0.0000139084,"domain_scores_codex":[0.9967248,0.0007675682,0.0004342396,0.0002161697,0.001515065,0.000342191],"domain_scores_gemma":[0.9986577,0.0006543481,0.0001782229,0.0001598099,0.000270643,0.00007929933],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009210953,0.00002520933,0.9433153,0.0001100648,0.0003383992,0.000011284,0.005284693,0.0006674544,0.000008697582,0.00000230783,0.00007007881,0.04924545],"study_design_scores_gemma":[0.001960948,0.001419495,0.9013335,0.0001804136,0.0001235727,0.000005569065,0.01183701,0.01039657,0.0005778956,0.000008435032,0.07188068,0.0002758928],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968846,0.001002382,0.00009489163,0.0001489886,0.0001145387,0.001557255,0.00001595795,0.00003315682,0.0001482365],"genre_scores_gemma":[0.9982384,0.00008135376,0.0008832527,0.000005028132,0.0002667166,0.00007083262,0.00005514625,0.00002370299,0.0003755768],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0718106,"threshold_uncertainty_score":0.4789156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1033392325715109,"score_gpt":0.4037106860148498,"score_spread":0.3003714534433389,"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."}}