{"id":"W3109501393","doi":"10.2196/21750","title":"Family History Information Extraction With Neural Attention and an Enhanced Relation-Side Scheme: Algorithm Development and Validation","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Topic Modeling","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Science and Technology, Taiwan","keywords":"Computer science; Artificial neural network; Artificial intelligence; Relationship extraction; Information extraction; Machine learning; Scheme (mathematics); Task (project management); Inference; Relation (database); Sentence; Algorithm; Data mining","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":[0.003043891,0.00131749,0.001058488,0.001303078,0.000656507,0.0008872696,0.00215212,0.001976339,0.002699847],"category_scores_gemma":[0.006837904,0.0005393616,0.0008174112,0.001009738,0.0004672552,0.001728336,0.001393734,0.002132852,0.0006609342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001989339,"about_ca_system_score_gemma":0.002465667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02592929,"about_ca_topic_score_gemma":0.02188404,"domain_scores_codex":[0.9992076,0.0002102013,0.00007978282,0.0002825858,0.0001211703,0.00009870898],"domain_scores_gemma":[0.9973143,0.001531011,0.0001301773,0.0002528262,0.0006970304,0.00007464339],"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":[0.0008713336,0.0005924767,0.007214554,0.0002557704,0.000227404,0.0002102298,0.0001357261,0.3140067,0.00743898,0.001565605,0.004545049,0.6629362],"study_design_scores_gemma":[0.00003707984,0.00006058225,0.0005458188,0.00001052715,0.00003014969,0.00002777588,0.00001973285,0.9960019,0.002372614,0.0006028012,0.0002848417,0.000006241959],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.327175,0.002581286,0.6530669,0.001173289,0.0002389855,0.001228604,0.001159797,0.008748611,0.00462752],"genre_scores_gemma":[0.5780311,0.0005368612,0.4139312,0.000441717,0.00007084879,0.0007317453,0.002460277,0.000120421,0.003675886],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02592929,"threshold_uncertainty_score":0.05155677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02611977797276242,"score_gpt":0.2653891485497481,"score_spread":0.2392693705769857,"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."}}