{"id":"W4380625864","doi":"10.1093/ndt/gfad063c_3815","title":"#3815 A DEEP LEARNING APPROACH TO PERSONALISED ANTI-HYPERTENSIVE MEDICATION TITRATION","year":2023,"lang":"en","type":"article","venue":"Nephrology Dialysis Transplantation","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Western University","funders":"","keywords":"Medicine; Blood pressure; Clinical decision support system; Clinical trial; Placebo; Randomized controlled trial; Stroke (engine); Physical therapy; Emergency medicine; Internal medicine; Decision support system; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001442771,0.0007501306,0.0006509406,0.0005504516,0.0003198735,0.0006597907,0.001375712,0.00111604,0.00627917],"category_scores_gemma":[0.003608186,0.000514724,0.0006794824,0.0004340477,0.0004224014,0.0006796026,0.001272718,0.001708429,0.0008467791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001082803,"about_ca_system_score_gemma":0.001422505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01280029,"about_ca_topic_score_gemma":0.01479783,"domain_scores_codex":[0.9995448,0.0001618477,0.00003409872,0.0001139862,0.00008263331,0.00006274275],"domain_scores_gemma":[0.9989232,0.0006136031,0.00007720639,0.00008105997,0.000240313,0.00006463448],"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":[0.0002324142,0.0002111756,0.001904342,0.000100878,0.0001096766,0.0001157422,0.00008808594,0.6680603,0.001962057,0.003877406,0.006839856,0.3164981],"study_design_scores_gemma":[0.00001127187,0.00001724882,0.00008988011,0.000006152702,0.000005411317,0.000006291789,0.000002593082,0.9976262,0.0002862208,0.001545754,0.0004001607,0.000002714108],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04403337,0.0008133839,0.9439682,0.001446279,0.0001649286,0.0001907164,0.0005537695,0.003567302,0.005262117],"genre_scores_gemma":[0.661275,0.0004012004,0.3249511,0.0008284017,0.0001539263,0.0003725225,0.0007682399,0.0001757054,0.01107378],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01280029,"threshold_uncertainty_score":0.0254516,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01898786435956119,"score_gpt":0.270812173149692,"score_spread":0.2518243087901308,"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."}}