{"id":"W3205360896","doi":"10.2196/23440","title":"Predicting Risk of Stroke From Lab Tests Using Machine Learning Algorithms: Development and Evaluation of Prediction Models","year":2021,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Machine learning; Resampling; Random forest; Artificial intelligence; Computer science; Predictive modelling; Stroke (engine); Test data; Algorithm; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01385432,0.001439937,0.001127537,0.002457328,0.0004072927,0.001170906,0.001404558,0.001266978,0.0005415278],"category_scores_gemma":[0.03733021,0.0003754796,0.001187032,0.001437839,0.0004623221,0.001453846,0.0007099203,0.001821382,0.000199438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001220894,"about_ca_system_score_gemma":0.001807072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008287388,"about_ca_topic_score_gemma":0.003062876,"domain_scores_codex":[0.9962379,0.002024847,0.0003818274,0.0004461427,0.0007568536,0.0001523863],"domain_scores_gemma":[0.9624798,0.03130294,0.001176139,0.0008901074,0.003843251,0.0003078025],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005572825,0.0009845725,0.1101465,0.0002484547,0.0005922253,0.0001552782,0.0001496591,0.6967239,0.0008576369,0.001275132,0.001921116,0.1863882],"study_design_scores_gemma":[0.00002304232,0.0001306614,0.004142663,0.00002727811,0.00004871807,0.00003223939,0.00001734349,0.994283,0.0005766106,0.0005789095,0.0001278195,0.00001155088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6626683,0.002816757,0.328331,0.00159477,0.0001781172,0.0006987836,0.0008838146,0.001049734,0.00177865],"genre_scores_gemma":[0.8330679,0.0008042987,0.1639863,0.0001361929,0.0000835061,0.0005589308,0.000991714,0.00003342151,0.0003377427],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01385432,"threshold_uncertainty_score":0.07326955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1038170853717056,"score_gpt":0.4040394159712638,"score_spread":0.3002223305995582,"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."}}