{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006936953,0.0001915336,0.0003165678,0.0002447283,0.0001087357,0.00004481877,0.0003973885,0.0001022472,0.00002734316],"category_scores_gemma":[0.0006114194,0.0001485666,0.0000966778,0.0005614172,0.00004932983,0.0006102187,0.0001643347,0.0001061326,0.00007285327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007443822,"about_ca_system_score_gemma":0.00009689466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003781413,"about_ca_topic_score_gemma":0.0000301852,"domain_scores_codex":[0.9976258,0.00005349055,0.0004163589,0.0005494994,0.001064018,0.0002908193],"domain_scores_gemma":[0.9971211,0.000154323,0.0003787666,0.0003299627,0.001869154,0.0001467171],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002493738,0.0005558351,0.5625495,0.0002787121,0.0002089682,0.000005500193,0.007670003,0.00001670811,0.0114879,0.005885005,0.01318179,0.3979107],"study_design_scores_gemma":[0.01105693,0.003927512,0.8782129,0.002022005,0.0001536338,0.00002508056,0.0005560688,0.02381195,0.06442848,0.007212186,0.006660804,0.001932492],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9896237,0.00006200165,0.004735494,0.003166965,0.0006432939,0.0006304111,0.00006230202,0.0001675278,0.0009082688],"genre_scores_gemma":[0.9992129,0.00001456687,0.0001769375,0.0001754982,0.0001675683,0.00004442018,0.000008137125,0.0000166335,0.0001833996],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3959782,"threshold_uncertainty_score":0.605837,"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."}}