{"id":"W4389834796","doi":"10.1007/978-3-031-46341-9_5","title":"Deep Learning Approaches for End-to-End Modeling of Medical Spatiotemporal Data","year":2023,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Artificial intelligence; Adaptation (eye); Deep learning; Domain (mathematical analysis); Machine learning; Domain adaptation; Data science; Medical imaging","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002631307,0.0003818794,0.0007306256,0.0005222837,0.0002000529,0.00005046201,0.003187437,0.0002790101,0.00002728856],"category_scores_gemma":[0.002663861,0.0003956662,0.0001092303,0.0002707643,0.0002470531,0.0001893444,0.00354131,0.001009665,0.00004518963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001510068,"about_ca_system_score_gemma":0.0004206899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001433383,"about_ca_topic_score_gemma":0.0003911755,"domain_scores_codex":[0.9954058,0.0001111984,0.001204337,0.001215021,0.001681125,0.000382507],"domain_scores_gemma":[0.9946516,0.003483441,0.0004134459,0.0007718323,0.0005422531,0.0001374567],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000070267,0.000008716826,0.00004328179,0.0003249659,0.00007038778,0.00001145115,0.001455287,0.5980769,5.097235e-9,0.3072606,0.00007005494,0.09267139],"study_design_scores_gemma":[0.00005301391,0.000100501,0.00001263642,0.0007012262,0.000009517635,0.000007309487,0.0002454181,0.8191528,3.650179e-7,0.1785789,0.0008722321,0.0002660416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000008908852,0.001942021,0.9898398,0.003114322,0.000893818,0.0005883733,0.00004150711,0.0001488309,0.003422407],"genre_scores_gemma":[0.3396139,0.001945196,0.6432585,0.0005379878,0.001035027,0.0002197354,0.001191167,0.0002200704,0.01197845],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3465813,"threshold_uncertainty_score":0.9998495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4447033388990161,"score_gpt":0.4447555970048605,"score_spread":0.00005225810584441604,"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."}}