{"id":"W4407445136","doi":"10.1038/s41746-025-01498-1","title":"Personalized decision making for coronary artery disease treatment using offline reinforcement learning","year":2025,"lang":"en","type":"article","venue":"npj Digital Medicine","topic":"Coronary Interventions and Diagnostics","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Libin Cardiovascular Institute of Alberta; Foothills Medical Centre; Alberta Health Services; University of Calgary","funders":"Canadian Institutes of Health Research; Alberta Innovates; Government of Canada","keywords":"Reinforcement learning; Coronary artery disease; Reinforcement; Medicine; Disease; Internal medicine; Cardiology; Computer science; Artificial intelligence; Psychology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003361844,0.0008449712,0.0008705591,0.0005187411,0.0002507195,0.001021158,0.0009158716,0.0007245274,0.003006423],"category_scores_gemma":[0.01840906,0.0003716328,0.0004628859,0.0002614091,0.0005566857,0.0007242882,0.000911954,0.001638452,0.0003540703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001058117,"about_ca_system_score_gemma":0.002003782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006309223,"about_ca_topic_score_gemma":0.007302509,"domain_scores_codex":[0.9986381,0.0008522132,0.00006197538,0.0002347172,0.0001173772,0.00009554831],"domain_scores_gemma":[0.988452,0.009897969,0.0006672339,0.0003098269,0.0003493698,0.0003236268],"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":[0.0003810457,0.0002248662,0.006658877,0.00007378884,0.00007904777,0.00007845363,0.00008289715,0.9420249,0.0005818673,0.00217729,0.0008416281,0.04679533],"study_design_scores_gemma":[0.00003986759,0.00005287096,0.0002962808,0.00000857501,0.000008754642,0.000008708469,0.000008928401,0.9970871,0.000260214,0.002043964,0.0001789158,0.000005903351],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1969589,0.0004379885,0.7947428,0.001355348,0.0001011355,0.0003497333,0.0005010924,0.001830642,0.00372231],"genre_scores_gemma":[0.9347229,0.00007954115,0.06389403,0.0002370779,0.00003248524,0.0001672166,0.000231834,0.00004670207,0.0005882566],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006309223,"threshold_uncertainty_score":0.01777929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04214656177938435,"score_gpt":0.3598822776269549,"score_spread":0.3177357158475705,"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."}}