{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000811404,0.0002785653,0.0006001053,0.0002044346,0.0006296214,0.00003854984,0.00008104416,0.0000988982,0.00002982553],"category_scores_gemma":[0.00009857122,0.0002732828,0.00002628588,0.0003602919,0.0001561006,0.0002820817,0.0003379369,0.0006116889,0.000001734446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009944176,"about_ca_system_score_gemma":0.002285835,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003267066,"about_ca_topic_score_gemma":0.0001023458,"domain_scores_codex":[0.9967169,0.0004909768,0.0006264841,0.0009740987,0.0006416783,0.0005498184],"domain_scores_gemma":[0.9979296,0.0001000889,0.0003198328,0.0003985215,0.0002601747,0.0009918192],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008108596,0.0004631872,0.941415,0.01108599,0.0001789323,0.0002408852,0.0008135749,0.0000366914,0.0001220169,0.00002996909,0.000003702121,0.04479924],"study_design_scores_gemma":[0.001791761,0.0003229966,0.8853319,0.0004285391,0.0005450296,0.0003208919,0.003217593,0.1071936,0.00001713598,0.00000645197,0.0006661977,0.0001579927],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9651546,0.0321916,0.0001429228,0.0001471508,0.00008776219,0.001967724,0.00008730256,0.0000751378,0.0001458274],"genre_scores_gemma":[0.9949661,0.003344882,0.0005102956,0.00002276954,0.0001412139,0.00009901288,0.0005714466,0.00004511579,0.0002990981],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1071569,"threshold_uncertainty_score":0.9999719,"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."}}