{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04054016,0.0005246886,0.0006001888,0.001506495,0.004986787,0.004338633,0.001727275,0.001413789,0.001691703],"category_scores_gemma":[0.06535489,0.0005006844,0.000570655,0.0008768193,0.01077413,0.005042985,0.005139072,0.002506844,0.0002560919],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005947578,"about_ca_system_score_gemma":0.007028186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004461026,"about_ca_topic_score_gemma":0.004346179,"domain_scores_codex":[0.9663698,0.02830488,0.0008404409,0.0008840223,0.002151492,0.001449359],"domain_scores_gemma":[0.9116284,0.07611172,0.003392047,0.001325725,0.005142135,0.00240004],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00004553164,0.0000330047,0.004561124,0.0002265123,0.00000590586,0.0003034738,0.98626,0.00009289517,0.0009112487,0.001401093,0.0004195581,0.005739651],"study_design_scores_gemma":[0.000004991064,0.00008011761,0.0018252,0.0002336326,0.000005269709,0.0001799798,0.9923935,0.0003118004,0.0004601993,0.0006059423,0.003878837,0.00002051254],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9768911,0.0004108232,0.01119677,0.005430671,0.000077269,0.0003900778,0.0001762653,0.00003574551,0.005391181],"genre_scores_gemma":[0.99435,0.0003442754,0.003331081,0.0006172837,0.00001383243,0.0003162708,0.00004967708,0.00002570625,0.0009518628],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04054016,"threshold_uncertainty_score":0.2143994,"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."}}