{"id":"W4417247349","doi":"10.1142/s0218194025501025","title":"Machine Learning and Constraint Programming for Efficient Healthcare Scheduling","year":2025,"lang":"en","type":"article","venue":"International Journal of Software Engineering and Knowledge Engineering","topic":"Scheduling and Timetabling Solutions","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Constraint programming; Heuristics; Scheduling (production processes); Job shop scheduling; Constraint satisfaction; Workload; Computation; Constraint logic programming","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001989241,0.0001733997,0.0003176675,0.0007385438,0.0001210377,0.0002664208,0.0002862531,0.00008210233,0.000003331499],"category_scores_gemma":[0.01109832,0.0001531949,0.0001288978,0.0003422482,0.00003672325,0.0001119887,0.0001079023,0.0004040012,0.000001282822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008180655,"about_ca_system_score_gemma":0.0001229255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004791283,"about_ca_topic_score_gemma":0.000001359368,"domain_scores_codex":[0.9984553,0.0000245621,0.0006742243,0.0002323515,0.0003607398,0.0002528186],"domain_scores_gemma":[0.9966412,0.001996158,0.0001769329,0.00008394768,0.0009501462,0.0001516372],"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":[0.00006744505,0.00009503512,0.01391433,0.0001909154,0.0004379519,0.00002119399,0.0009943089,0.7294315,0.000967916,0.01507584,0.00004034952,0.2387632],"study_design_scores_gemma":[0.001097537,0.00009189737,0.001558679,0.0008344153,0.00004935673,0.0001849268,0.0002738224,0.9538185,0.0003685204,0.0002963722,0.04119471,0.0002312736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1151057,0.01586708,0.8657969,0.0005889115,0.002395627,0.0001168668,0.00001130304,0.0001020699,0.00001544819],"genre_scores_gemma":[0.8683968,0.0001184178,0.1310787,0.00001285072,0.0002501638,0.000007871477,0.00000230296,0.00001668704,0.0001162852],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.753291,"threshold_uncertainty_score":0.9972316,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02909973679705864,"score_gpt":0.3481407537252765,"score_spread":0.3190410169282178,"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."}}