{"id":"W3202634829","doi":"10.2196/28039","title":"Ensemble Learning-Based Pulse Signal Recognition: Classification Model Development Study","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Science and Technology Commission of Shanghai Municipality; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Ensemble learning; Convolutional neural network; Support vector machine; Deep learning; Machine learning; Pattern recognition (psychology); Artificial neural network; Classifier (UML); Time domain; Feature extraction; Data mining; Computer vision","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":[0.003405181,0.0008275006,0.00115241,0.001135349,0.000396685,0.0008990651,0.0008804003,0.0008772137,0.001162724],"category_scores_gemma":[0.006312441,0.0002851928,0.001061641,0.00109699,0.0002551253,0.00150346,0.0006473397,0.001604106,0.0003252786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007512079,"about_ca_system_score_gemma":0.0008420295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008423,"about_ca_topic_score_gemma":0.003867438,"domain_scores_codex":[0.9990445,0.0002847123,0.0000693532,0.0002379833,0.0002741594,0.00008929722],"domain_scores_gemma":[0.9956408,0.002343031,0.0002573249,0.0003040452,0.0013587,0.0000960595],"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":[0.000223755,0.0003031909,0.02243881,0.0001494886,0.0004121128,0.0001797923,0.0002559992,0.6546187,0.003085286,0.008439385,0.002568208,0.3073252],"study_design_scores_gemma":[0.000001213947,0.00003198492,0.0006854103,0.000004992431,0.00001626054,0.00002296174,0.000007739622,0.9981608,0.0003577652,0.0005323234,0.000174388,0.000004254419],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1806532,0.002681392,0.8107334,0.0007629266,0.0001685307,0.000113953,0.00022393,0.000642008,0.004020669],"genre_scores_gemma":[0.9171084,0.001426009,0.07824259,0.0001043908,0.0001143639,0.0001319402,0.0003975082,0.00004890423,0.002426025],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008423,"threshold_uncertainty_score":0.01800853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0397244823006255,"score_gpt":0.2719995522638969,"score_spread":0.2322750699632714,"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."}}