{"id":"W1648897910","doi":"10.1016/j.procs.2015.08.357","title":"M4CVD: Mobile Machine Learning Model for Monitoring Cardiovascular Disease","year":2015,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Wearable computer; Support vector machine; Machine learning; Artificial intelligence; Vital signs; Raw data; Wearable technology; Real-time computing; Human–computer interaction; Embedded system; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.002797737,0.0001670639,0.0002686113,0.0001587629,0.001734564,0.00003121564,0.0005493718,0.00006999902,0.000003140212],"category_scores_gemma":[0.0003922144,0.0001529956,0.0000979114,0.0005906108,0.0001353063,0.0003253985,0.0002828357,0.0004313001,0.00008742148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002869527,"about_ca_system_score_gemma":0.003572565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006350618,"about_ca_topic_score_gemma":0.000003260146,"domain_scores_codex":[0.9971416,0.0000793198,0.0004499741,0.0006934106,0.0006424032,0.0009933132],"domain_scores_gemma":[0.9967194,0.0001847545,0.0001492448,0.0005699532,0.0007915934,0.001585076],"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":[0.0001729763,0.0002113672,0.1009822,0.002059596,0.0000357826,0.000003485357,0.008247692,0.5333695,0.00006404985,0.004936127,0.003840339,0.3460769],"study_design_scores_gemma":[0.0006772404,0.00006579674,0.0008795118,0.00006429961,0.00002052493,9.359983e-7,0.00007001856,0.9283228,0.00001169223,0.00106461,0.06867443,0.0001481631],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03351618,0.003233053,0.9548647,0.0004719798,0.001750332,0.005468428,0.00002302472,0.0003973437,0.0002749203],"genre_scores_gemma":[0.866358,0.0003087284,0.1063699,0.0007185852,0.001771663,0.02388772,0.00002004918,0.00004213714,0.0005231826],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8484949,"threshold_uncertainty_score":0.9995651,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.101154893458847,"score_gpt":0.412445200886745,"score_spread":0.311290307427898,"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."}}