{"id":"W2564294371","doi":"10.2196/iproc.6105","title":"Philips Lifeline CareSage Analytics Engine: Retrospective Evaluation on Patients of Partners Healthcare at Home","year":2016,"lang":"en","type":"article","venue":"Iproceedings","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medical emergency; Health care; Service (business); Analytics; Emergency department; Healthcare service; Population; Medicine; Ambulance service; Health services; Computer science; Business; Nursing; Data science; Environmental health; Marketing","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":[0.002590054,0.000446041,0.0005793135,0.001684343,0.0002896415,0.001184143,0.0005475121,0.0003345824,0.001862086],"category_scores_gemma":[0.01198664,0.0002370921,0.0006828165,0.002194168,0.0002487416,0.0007859036,0.001089978,0.0005434752,0.0006334984],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008802208,"about_ca_system_score_gemma":0.000918808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01482727,"about_ca_topic_score_gemma":0.01126024,"domain_scores_codex":[0.9980574,0.0005164235,0.0003926997,0.0004095425,0.0004384775,0.0001854473],"domain_scores_gemma":[0.9914241,0.002587953,0.00234255,0.0007405914,0.002162067,0.0007426934],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002350391,0.00008269784,0.9935125,0.00005181854,0.00008649335,0.0001593298,0.0001597333,0.0002068802,0.00006507403,0.00004146709,0.001952232,0.003446866],"study_design_scores_gemma":[0.00005287273,0.0003392541,0.9902403,0.00007785897,0.0001416815,0.0006024065,0.001056382,0.005037555,0.0003065821,0.00007172438,0.00205257,0.00002080373],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9851756,0.0002158334,0.0003099293,0.0001371233,0.000008623082,0.0000857854,0.01335482,0.00004359183,0.0006687431],"genre_scores_gemma":[0.9812307,0.0001672271,0.0004891933,0.0001059184,0.00002007195,0.00009629862,0.01770826,0.00001524357,0.0001670559],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01482727,"threshold_uncertainty_score":0.02948189,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04496018487466857,"score_gpt":0.3036925947039952,"score_spread":0.2587324098293266,"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."}}