{"id":"W4406493073","doi":"10.1136/bmjpo-2025-gosh.76","title":"140 Exploring dimensionality reduction to facilitate visualisation and analysis of GOSH EHR data","year":2025,"lang":"en","type":"article","venue":"","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institute of Infection and Immunity","funders":"NIHR Great Ormond Street Hospital Biomedical Research Centre; Great Ormond Street Hospital Charity; National Institute for Health and Care Research","keywords":"Dimensionality reduction; Computer science; Visualization; Data visualization; Reduction (mathematics); Data reduction; Data mining; Data science; Artificial intelligence; Mathematics","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":[0.001584179,0.0005781256,0.0004541592,0.003046144,0.0007396198,0.004209557,0.0004856426,0.0005258231,0.01045705],"category_scores_gemma":[0.008270061,0.0002321939,0.0008569974,0.00316773,0.000556967,0.001657529,0.002153128,0.000933598,0.001568342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005769259,"about_ca_system_score_gemma":0.001387397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006163992,"about_ca_topic_score_gemma":0.008163894,"domain_scores_codex":[0.9993334,0.00028073,0.00006034719,0.00007120136,0.0001968603,0.00005744059],"domain_scores_gemma":[0.9971438,0.00178387,0.000150804,0.0003352416,0.0004931962,0.00009302399],"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":[0.0007178164,0.0003606803,0.02186339,0.001952135,0.0002209754,0.001210332,0.01149351,0.03034514,0.03108756,0.09325743,0.1121239,0.6953671],"study_design_scores_gemma":[0.0002159074,0.0002771216,0.04319681,0.001252013,0.0001766303,0.001284943,0.0102722,0.4109399,0.03251418,0.2235712,0.2759457,0.0003533976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2049784,0.002124515,0.7092689,0.01079007,0.0006237658,0.0006892997,0.02436563,0.0166139,0.03054565],"genre_scores_gemma":[0.4146144,0.001407891,0.5641958,0.000459825,0.0001402879,0.000343481,0.01272858,0.001129744,0.00498003],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01045705,"threshold_uncertainty_score":0.03498226,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2852965222016761,"score_gpt":0.3970342995365717,"score_spread":0.1117377773348956,"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."}}