{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000951692,0.000900974,0.0009348204,0.0008917694,0.0003645574,0.0007023431,0.001733658,0.001219054,0.001899314],"category_scores_gemma":[0.002611613,0.000249743,0.000833755,0.0006258455,0.0002246823,0.0006135617,0.0008012738,0.001277743,0.0011799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006473727,"about_ca_system_score_gemma":0.0007369347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008916738,"about_ca_topic_score_gemma":0.007228166,"domain_scores_codex":[0.9994586,0.0001442811,0.00003225533,0.0001517764,0.0001645153,0.00004852185],"domain_scores_gemma":[0.9993063,0.0002835595,0.00004234735,0.0000904687,0.0002435519,0.00003388955],"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":[0.0007936237,0.0005116368,0.01594098,0.0002968107,0.0003549674,0.0003873682,0.00008542298,0.3174296,0.007941844,0.003650655,0.03133952,0.6212676],"study_design_scores_gemma":[0.00002747464,0.0001196809,0.001415628,0.0000140627,0.00002482098,0.0001126084,0.000009801113,0.9897096,0.001957948,0.001779641,0.004808834,0.00001986787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05567341,0.002602828,0.9255231,0.001016522,0.0006730315,0.0003282652,0.003637787,0.007760813,0.002784187],"genre_scores_gemma":[0.5684429,0.001567284,0.4128476,0.0009042053,0.0003486125,0.0008849254,0.006715188,0.0003103455,0.007978816],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008916738,"threshold_uncertainty_score":0.01772964,"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."}}