{"id":"W4205235146","doi":"10.2196/30587","title":"Selective Prediction With Long Short-term Memory Using Unit-Wise Batch Standardization for Time Series Health Data Sets: Algorithm Development and Validation","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Science and ICT, South Korea; National Research Foundation","keywords":"Computer science; Normalization (sociology); Standardization; Data mining; Time series; Data set; Set (abstract data type); Machine learning; Artificial intelligence; Confidence interval; Algorithm; Statistics; Mathematics","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":[0.001804176,0.0001518336,0.0002194785,0.000145062,0.0008143465,0.0001378807,0.0005395826,0.00006687131,0.00001985991],"category_scores_gemma":[0.00009165225,0.0001384596,0.00001172366,0.0004528788,0.00005510623,0.001470734,0.0006990457,0.0003851769,9.422434e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003551425,"about_ca_system_score_gemma":0.00167681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001606254,"about_ca_topic_score_gemma":0.00001328839,"domain_scores_codex":[0.9975082,0.0001821445,0.0006344376,0.0002293113,0.001146181,0.000299747],"domain_scores_gemma":[0.9987696,0.0001127557,0.0002780019,0.000423555,0.0001837143,0.000232351],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008548105,0.0001154558,0.008629759,0.001430519,0.0001038258,0.000008838332,0.06439568,0.007573843,0.000002153899,0.000292413,0.002274511,0.9150875],"study_design_scores_gemma":[0.0004577799,0.000404637,0.001895843,0.0001218874,0.000007525836,0.0001427626,0.001057444,0.9924861,0.00004399366,0.00003935324,0.003183418,0.0001592693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04316425,0.00003525999,0.9544759,0.0007933122,0.0001607059,0.001005653,0.0001498796,0.0001836485,0.0000313232],"genre_scores_gemma":[0.03827444,0.00003042193,0.956647,0.0009752423,0.000128915,0.000299752,0.003553756,0.00003587719,0.00005459831],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9849122,"threshold_uncertainty_score":0.6263378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03614754148466187,"score_gpt":0.3403826230707668,"score_spread":0.304235081586105,"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."}}