{"id":"W3014923662","doi":"10.36227/techrxiv.12059019.v1","title":"Text Summarization and Classification of Clinical Discharge Summaries using Deep Learning","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Automatic summarization; Convolutional neural network; Computer science; Artificial intelligence; Sample (material); Natural language processing; Artificial neural network; Deep learning; Machine learning; Pattern recognition (psychology); Chemistry","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":[],"consensus_categories":[],"category_scores_codex":[0.0006098403,0.0001601241,0.0003902573,0.00007982578,0.00008268722,0.0001879268,0.0005028304,0.0002535943,0.000009881713],"category_scores_gemma":[0.0004701072,0.0001532248,0.00008554204,0.0001300172,0.00007053704,0.000252283,0.001616728,0.000574526,0.000003891419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002061497,"about_ca_system_score_gemma":0.0001407509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007768042,"about_ca_topic_score_gemma":0.00001843617,"domain_scores_codex":[0.9979218,0.0002192919,0.0007833013,0.0006840223,0.0002506486,0.0001409145],"domain_scores_gemma":[0.9986147,0.0001889733,0.0005114819,0.0004696905,0.0001268127,0.00008832042],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002029456,0.00008323574,0.2544854,0.0005856609,0.0001043471,0.000002989283,0.002428845,0.02040568,0.002411425,0.3851865,0.00004985038,0.3342358],"study_design_scores_gemma":[0.0001093628,0.00001627008,0.02016452,0.00005654039,0.00001691403,8.349385e-7,0.00004948339,0.9740982,0.00008602914,0.004907182,0.0003451471,0.0001495359],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05272016,0.000137938,0.9446353,0.0007505951,0.0004380539,0.000176674,0.000001583673,0.0001253199,0.001014397],"genre_scores_gemma":[0.8007545,0.0001112569,0.1988288,0.00005124714,0.0001343145,0.000003868221,0.00002639142,0.00001059136,0.00007900872],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9536925,"threshold_uncertainty_score":0.6248327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1463046939501471,"score_gpt":0.3589055347598744,"score_spread":0.2126008408097272,"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."}}