{"id":"W4225772450","doi":"10.1007/978-981-19-9865-2_10","title":"Machine Learning for Multimodal Electronic Health Records-Based Research: Challenges and Perspectives","year":2023,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Health records; Modalities; Unstructured data; Electronic health record; Machine learning; Artificial intelligence; Digital library; Data science; Information retrieval; Data mining; Big data; Health care","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":[0.01946171,0.0006796398,0.001553421,0.00258198,0.0008713047,0.009325982,0.002915335,0.003795902,0.006123011],"category_scores_gemma":[0.01964924,0.0005115421,0.0005768604,0.004460757,0.004476841,0.01285633,0.003272903,0.004989944,0.001975724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002234323,"about_ca_system_score_gemma":0.002335201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002052696,"about_ca_topic_score_gemma":0.002487703,"domain_scores_codex":[0.9952796,0.003109089,0.0001918546,0.0002985637,0.0009315949,0.000189412],"domain_scores_gemma":[0.9578341,0.03737855,0.000585401,0.001156234,0.002523692,0.0005221342],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004106904,0.0001379268,0.000922158,0.0008986169,0.00006236284,0.0001169447,0.0004315694,0.004145376,0.0003497903,0.4975121,0.05479538,0.4405867],"study_design_scores_gemma":[0.00001189916,0.0000526782,0.00068026,0.001768515,0.00002091637,0.0002765824,0.0007595111,0.03763682,0.0004924929,0.7976651,0.1605743,0.00006089474],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.004327837,0.4054824,0.2242202,0.3136211,0.003122699,0.00008850815,0.0003381861,0.0004053584,0.04839363],"genre_scores_gemma":[0.1511592,0.4379566,0.3339738,0.02452238,0.0191879,0.0004366283,0.0007786109,0.0002896715,0.03169514],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01946171,"threshold_uncertainty_score":0.1029246,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2531785037105655,"score_gpt":0.4162408136605928,"score_spread":0.1630623099500273,"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."}}