{"id":"W2889161337","doi":"10.2196/11895","title":"Value and Acceptability of a Novel Machine Learning Technology for Heart Failure Readmission Reduction: Qualitative Analysis of Clinical Roles and Workflows","year":2018,"lang":"en","type":"article","venue":"Iproceedings","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Workflow; Reliability (semiconductor); Computer science; Machine learning; Predictive value; Reduction (mathematics); Cost reduction; Health care; Value (mathematics); Artificial intelligence; Risk analysis (engineering); Reliability engineering; Data science; Medicine; Engineering; Business; Database; 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":[0.002425331,0.0001329199,0.0005958694,0.0004182588,0.0001778064,0.00002825855,0.0002744562,0.0002012034,0.00001263354],"category_scores_gemma":[0.003001728,0.0001172505,0.0001019149,0.001378503,0.0004383885,0.0002304073,0.0002715655,0.0003388939,2.013822e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001450017,"about_ca_system_score_gemma":0.00005741301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001569664,"about_ca_topic_score_gemma":0.00002590991,"domain_scores_codex":[0.9982703,0.0000910151,0.0006656683,0.000579261,0.000200853,0.0001928528],"domain_scores_gemma":[0.9981573,0.0004512499,0.0004612526,0.0002214416,0.0006035651,0.000105197],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002423176,0.0002116898,0.8236739,0.0005127593,0.0003699382,1.662151e-7,0.03595897,0.0001068885,0.006521876,0.0408933,0.0001472581,0.09136095],"study_design_scores_gemma":[0.001838147,0.003295788,0.1864115,0.0002151925,0.0004054502,0.00003859058,0.01193451,0.7755424,0.004553781,0.008505587,0.006768781,0.0004903123],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9532467,0.0002608559,0.03933022,0.006723688,0.00007164694,0.0002279293,0.000006722577,0.00009051905,0.00004170728],"genre_scores_gemma":[0.7590053,0.00001070195,0.2408639,0.00002372825,0.00005583273,0.00001054872,0.000001936667,0.000006562625,0.00002143964],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7754355,"threshold_uncertainty_score":0.4781336,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05226311872581452,"score_gpt":0.4317109150173061,"score_spread":0.3794477962914916,"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."}}