Development of prediction models for perioperative opioid needs in laparoscopic cholecystectomy patients: A machine-learning approach.

Saved in:
Bibliographic Details
Title: Development of prediction models for perioperative opioid needs in laparoscopic cholecystectomy patients: A machine-learning approach.
Authors: Huang Y; Department of Obstetrics and Gynecology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY., Li G; Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY.; Department of Anesthesiology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY., Martins SS; Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY., Mauro PM; Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY.; Center for Pharmacoepidemiology and Treatment Science, Rutgers Institute for Health, Health Care Policy and Aging Research, New Brunswick, NJ.; Department of Biostatistics and Epidemiology, Rutgers School of Public Health, Piscataway, NJ., Tergas AI; Division of Gynecologic Oncology, Department of Obstetrics, Gynecology, and Reproductive Sciences, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ., Hou J; Department of Obstetrics and Gynecology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY., Xu X; Department of Obstetrics and Gynecology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY., Elkin EB; Department of Health Policy and Management, Columbia University Mailman School of Public Health, New York, NY., Jacobson JS; Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY., Wright JD; Department of Obstetrics and Gynecology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY.
Source: Surgery open science [Surg Open Sci] 2026 Jan 19; Vol. 30, pp. 14-22. Date of Electronic Publication: 2026 Jan 19 (Print Publication: 2026).
Publication Type: Journal Article
Journal Info: Publisher: Elsevier Inc Country of Publication: United States NLM ID: 101768812 Publication Model: eCollection Cited Medium: Internet ISSN: 2589-8450 (Electronic) Linking ISSN: 25898450 NLM ISO Abbreviation: Surg Open Sci Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2589-8450
DOI:10.1016/j.sopen.2026.01.005