{"id":"W3201061916","doi":"10.2196/30277","title":"Accurate Prediction of Stroke for Hypertensive Patients Based on Medical Big Data and Machine Learning Algorithms: Retrospective Study","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Chinese Academy of Sciences","keywords":"Machine learning; Artificial intelligence; Algorithm; Stroke (engine); Receiver operating characteristic; Medical record; Medicine; Big data; Boosting (machine learning); Computer science; Gradient boosting; Decision tree; Random forest; Population; Data mining; Internal medicine; Engineering","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00191446,0.0001903378,0.0005274431,0.0001265313,0.0005177511,0.00001152456,0.0003845436,0.0004577369,0.0002960003],"category_scores_gemma":[0.01621811,0.0001528423,0.00004359692,0.0002694801,0.0001656041,0.000206696,0.0004801972,0.001551487,0.00002177944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001377974,"about_ca_system_score_gemma":0.001233112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001505217,"about_ca_topic_score_gemma":0.0004574364,"domain_scores_codex":[0.9953743,0.0004951672,0.001619193,0.0002686986,0.00180515,0.0004374552],"domain_scores_gemma":[0.9952493,0.001858045,0.0005519317,0.0005864237,0.00126673,0.0004875402],"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":[0.000311314,0.001079223,0.857604,0.001222312,0.0001051557,0.00002252694,0.02354072,0.00002185202,0.000001775502,0.00008573889,0.003559906,0.1124455],"study_design_scores_gemma":[0.001883597,0.00148617,0.03604989,0.0007665712,0.00004443123,0.000002024425,0.04465641,0.9110037,0.00002671055,0.00005673649,0.003884146,0.0001396115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9668251,0.00006582547,0.02067885,0.002329095,0.002486366,0.004200922,0.002507969,0.0001568419,0.000748977],"genre_scores_gemma":[0.9948598,0.00008993097,0.0007531836,0.002420111,0.0004491396,0.0002390854,0.001077858,0.00002629627,0.00008462024],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9109818,"threshold_uncertainty_score":0.9920687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1911957114018802,"score_gpt":0.4521485853851026,"score_spread":0.2609528739832224,"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."}}