{"doi":"10.1016/j.bja.2021.12.039","title":"Continuous real-time prediction of surgical case duration using a modular artificial neural network","abstract":null,"journal":"British Journal of Anaesthesia","year":2022,"id":652460,"datarank":0.611630616585858,"base_score":4.07753744390572,"endowment":4.07753744390572,"self_citation_contribution":0.611630616585858,"citation_network_contribution":0.0,"self_endowment_contribution":0.611630616585858,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":58,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":916592,"name":"Bing Xue","orcid":"0000-0002-9162-098X","position":1,"is_corresponding":false},{"id":875119,"name":"Chenyang Lu","orcid":"0000-0003-1709-6769","position":2,"is_corresponding":false},{"id":827,"name":"Michael S. Avidan","orcid":"0000-0001-6248-044X","position":3,"is_corresponding":false},{"id":232283,"name":"Thomas Kannampallil","orcid":"0000-0003-4119-4836","position":4,"is_corresponding":false},{"id":1702015,"name":"York Jiao","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Continuous real-time prediction of surgical case duration using a modular artificial neural network","abstract":"<h4>Background</h4>Real-time prediction of surgical duration can inform perioperative decisions and reduce surgical costs. We developed a machine learning approach that continuously incorporates preoperative and intraoperative information for forecasting surgical duration.<h4>Methods</h4>Preoperative (e.g. procedure name) and intraoperative (e.g. medications and vital signs) variables were retrieved from anaesthetic records of surgeries performed between March 1, 2019 and October 31, 2019. A modular artificial neural network was developed and compared with a Bayesian approach and the scheduled surgical duration. Continuous ranked probability score (CRPS) was used as a measure of time error to assess model accuracy. For evaluating clinical performance, accuracy for each approach was assessed in identifying cases that ran beyond 15:00 (commonly scheduled end of shift), thus identifying opportunities to avoid overtime labour costs.<h4>Results</h4>The analysis included 70 826 cases performed at eight hospitals. The modular artificial neural network had the lowest time error (CRPS: mean=13.8; standard deviation=35.4 min), which was significantly better (mean difference=6.4 min [95% confidence interval: 6.3-6.5]; P<0.001) than the Bayesian approach. The modular artificial neural network also had the highest accuracy in identifying operating theatres that would overrun 15:00 (accuracy at 1 h prior=89%) compared with the Bayesian approach (80%) and a naïve approach using the scheduled duration (78%).<h4>Conclusions</h4>A real-time neural network model using preoperative and intraoperative data had significantly better performance than a Bayesian approach or scheduled duration, offering opportunities to avoid overtime labour costs and reduce the cost of surgery by providing superior real-time information for perioperative decision support.","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"35090725","pmcid":"PMC9074795","openalex_id":null,"authors":[],"funders":[],"total_grants":0,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[],"oa_status":"green","license":"http://www.elsevier.com/open-access/userlicense/1.0/","oa_locations":[{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9074795","host_type":"repository"},{"url":"https://api.elsevier.com/content/article/PII:S0007091221008709?httpAccept=text/xml","host_type":"publisher"},{"url":"https://api.elsevier.com/content/article/PII:S0007091221008709?httpAccept=text/plain","host_type":"publisher"}],"fields_of_study":[],"mesh_terms":["Humans","Bayes Theorem","Operating Rooms","Operative Time","Machine Learning","Neural Networks, Computer"],"keywords":["Surgery","Economics","statistical model","Artificial neural Network","Healthcare Costs","Machine Learning","Operating Theatre Efficiency","Procedure Duration"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-10T15:05:06.747647Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}