{"id":"W3139198151","doi":"10.2196/22591","title":"Acute Exacerbation of a Chronic Obstructive Pulmonary Disease Prediction System Using Wearable Device Data, Machine Learning, and Deep Learning: Development and Cohort Study","year":2021,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Chronic Obstructive Pulmonary Disease (COPD) Research","field":"Medicine","cited_by":148,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Science and Technology, Taiwan","keywords":"Medicine; COPD; Wearable computer; Machine learning; Cohort; Wearable technology; Exacerbation; Emergency medicine; Artificial intelligence; Physical therapy; Internal medicine; Computer science","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":[0.004334857,0.0006920518,0.0005332761,0.00081469,0.0004975956,0.0006705818,0.00068878,0.0005468039,0.000776546],"category_scores_gemma":[0.005639245,0.0003988557,0.001012803,0.0004150688,0.0004007113,0.0007434307,0.001037258,0.001211426,0.000527262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005579721,"about_ca_system_score_gemma":0.0007893317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009617842,"about_ca_topic_score_gemma":0.007595913,"domain_scores_codex":[0.9991392,0.0002883939,0.00006600103,0.000178464,0.0002274092,0.0001006121],"domain_scores_gemma":[0.996269,0.001113995,0.0003253364,0.0006856087,0.001044604,0.0005614259],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00142071,0.004267493,0.9646173,0.00004315445,0.000334819,0.0003538957,0.0003664108,0.001400239,0.001313025,0.000118387,0.001291013,0.02447366],"study_design_scores_gemma":[0.0004182966,0.006980193,0.9476899,0.00004779738,0.0004772876,0.0008460017,0.000677731,0.03894599,0.002135545,0.0001795855,0.001511316,0.00009034263],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979874,0.00004352273,0.001058691,0.00003531805,0.00001406123,0.0001540724,0.0005302196,0.00001986339,0.0001567454],"genre_scores_gemma":[0.9923626,0.000143857,0.00306655,0.00004518887,0.00002633951,0.0003457024,0.00332522,0.00001620888,0.0006682712],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009617842,"threshold_uncertainty_score":0.02292514,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04120957353729882,"score_gpt":0.3578158096254859,"score_spread":0.3166062360881871,"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."}}