{"id":"W4291012446","doi":"10.2196/37770","title":"Identifying the Perceived Severity of Patient-Generated Telemedical Queries Regarding COVID: Developing and Evaluating a Transfer Learning–Based Solution","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Triage; Transfer of learning; Word2vec; Artificial intelligence; Word embedding; Sentence; Deep learning; Task (project management); Autoencoder; Natural language processing; Machine learning; Context (archaeology); Encoder; Information retrieval; Embedding; Medicine; Medical emergency","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002126738,0.001570472,0.0005472226,0.00109348,0.0003391155,0.0007712208,0.00145908,0.001702958,0.001831252],"category_scores_gemma":[0.007404184,0.0001883851,0.0005920691,0.000566187,0.0004387276,0.001501742,0.001339689,0.00142875,0.0009715285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001392045,"about_ca_system_score_gemma":0.001146996,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007496079,"about_ca_topic_score_gemma":0.005223968,"domain_scores_codex":[0.9985821,0.0003633088,0.0001120275,0.0005829525,0.0002132715,0.0001462359],"domain_scores_gemma":[0.99698,0.001596754,0.0002775808,0.0002733957,0.0006806165,0.0001915742],"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":[0.001575037,0.002840253,0.0219957,0.0005567183,0.0001699483,0.0003642824,0.0006227462,0.1116653,0.02975415,0.0006730898,0.0125745,0.8172083],"study_design_scores_gemma":[0.00007392589,0.0007008421,0.005397023,0.00002047434,0.00005221479,0.0001272601,0.000356805,0.9787924,0.01226291,0.00124505,0.0009441664,0.00002686332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7735369,0.0008558773,0.2066455,0.001201747,0.0002416511,0.0009620052,0.001904254,0.01149011,0.003161802],"genre_scores_gemma":[0.9056801,0.0001789123,0.08797721,0.0003439818,0.0000768258,0.0003075568,0.003067465,0.0001144157,0.00225348],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007496079,"threshold_uncertainty_score":0.01490492,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04399922489542519,"score_gpt":0.344203826778934,"score_spread":0.3002046018835088,"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."}}