{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00102354,0.001086221,0.0006504373,0.002532885,0.0003251763,0.001052578,0.0008028233,0.0007760685,0.002090812],"category_scores_gemma":[0.004260939,0.0002367501,0.0005867307,0.001444394,0.0001904912,0.001184174,0.0006252805,0.001180889,0.00193889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006816777,"about_ca_system_score_gemma":0.0009188981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003403874,"about_ca_topic_score_gemma":0.007152901,"domain_scores_codex":[0.9992964,0.0001948966,0.00008518488,0.0001887775,0.0001487762,0.00008594482],"domain_scores_gemma":[0.9980935,0.0007496942,0.0002759103,0.000271475,0.0005101254,0.00009924326],"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":[0.000503838,0.0004011698,0.005048401,0.0004362463,0.0001344666,0.0002086769,0.0002876035,0.03564021,0.02854779,0.001954444,0.03804353,0.8887938],"study_design_scores_gemma":[0.00006239754,0.0002631814,0.007038413,0.00007214011,0.00008492549,0.0001026305,0.0001936137,0.9347616,0.03495431,0.008584335,0.01383414,0.00004832332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2731909,0.004448682,0.6426132,0.003841312,0.001184694,0.0006327158,0.02580691,0.04162565,0.006655928],"genre_scores_gemma":[0.6235674,0.001066713,0.3051065,0.0004474997,0.0007916692,0.0003388702,0.05830979,0.0005085638,0.00986299],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003403874,"threshold_uncertainty_score":0.006994486,"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."}}