{"id":"W3113296611","doi":"10.2196/22397","title":"Using General-purpose Sentiment Lexicons for Suicide Risk Assessment in Electronic Health Records: Corpus-Based Analysis","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Mental Health via Writing","field":"Psychology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Academy of Medical Sciences; Medical Research Council; National Institute for Health and Care Research; UK Research and Innovation","keywords":"Health records; Natural language processing; Computer science; Electronic health record; Artificial intelligence; Medical emergency; Medicine; Health care","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002041141,0.0002589897,0.0007258144,0.0003376986,0.0001898232,0.00004156739,0.0003155352,0.000222982,0.001061056],"category_scores_gemma":[0.0001499645,0.0002544883,0.0002250743,0.001094702,0.00007827205,0.0001390069,0.00007943757,0.0009326198,0.00004934353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009161035,"about_ca_system_score_gemma":0.001315543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008331368,"about_ca_topic_score_gemma":0.0005527329,"domain_scores_codex":[0.9955553,0.0003703227,0.001922879,0.0002700966,0.0007494466,0.001131917],"domain_scores_gemma":[0.9974353,0.0004712723,0.0008462691,0.0003508052,0.00006247165,0.0008339267],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001407676,0.003611789,0.5353982,0.005439078,0.00292893,0.0001034078,0.04199096,0.009835714,0.00006784544,0.01928729,0.02733521,0.3525939],"study_design_scores_gemma":[0.003832721,0.0008906062,0.006191402,0.0001534216,0.0001703669,0.000008108654,0.003045667,0.9762235,0.00004233013,0.0001336167,0.008978248,0.0003299579],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7680979,0.0003340575,0.2217043,0.005520144,0.0004095602,0.002662935,0.0001485591,0.0001491457,0.0009733535],"genre_scores_gemma":[0.8926806,0.0001477878,0.06810872,0.03723486,0.0003772419,0.00074563,0.0005765827,0.00006181105,0.00006672566],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9663879,"threshold_uncertainty_score":0.9999908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07971806615987065,"score_gpt":0.4636556540803959,"score_spread":0.3839375879205252,"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."}}