ArticleNeurosurgery2019
Predicting Inpatient Length of Stay After Brain Tumor Surgery: Developing Machine Learning Ensembles to Improve Predictive Performance.
Article in Neurosurgery, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 3 of them syntheses that pooled it.
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Who cites it
41 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- A systematic literature review of predicting patient discharges using statistical methods and machine learning.Health care management science · 2024Pooled it
- Overinterpretation of findings in machine learning prediction model studies in oncology: a systematic review.Journal of clinical epidemiology · 2023Pooled it
- Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review.BMC medical research methodology · 2022Pooled it
- Modelling the number of ICU beds, ICU length of stay, and ICU admission rates in Australia and New Zealand through to 2040: The PrognostICate Study.Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine · 2026Article
- Machine learning based prediction of length of stay in acute ischaemic stroke of the anterior circulation in patients treated with thrombectomy.Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences · 2026Article
- Predicting Geriatric Rehabilitation Stays of ≤4 Weeks After Hip Fracture Surgery: Machine Learning Approach Using Physical Activity and Patient Data.JMIR rehabilitation and assistive technologies · 2026Article
- A machine learning model for cancer screening in dogs using comprehensive circulating microRNA profiles.Journal of veterinary internal medicine · 2026Article
- Machine learning models incorporating somatic and mental comorbidities for prolonged length-of-stay prediction in a maximum care university hospital.BMC medical informatics and decision making · 2025Article
- Development of Multiservice Machine Learning Models to Predict Postsurgical Length of Stay and Discharge Disposition at the Time of Case Posting.Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2025Article
- Modelling in-hospital length of stay: A comparison of linear and ensemble models for competing risk analysis.PloS one · 2025Article
- Natural language processing augments comorbidity documentation in neurosurgical inpatient admissions.PloS one · 2024Article
- Development of a machine learning model to estimate length of stay in coronary artery bypass grafting.Revista de saude publica · 2024Article
- PSO-XnB: a proposed model for predicting hospital stay of CAD patients.Frontiers in artificial intelligence · 2024Article
- Predictive Models Based on Molecular Images and Molecular Descriptors for Drug Screening.ACS omega · 2023Article
- Association between preoperative serum sodium and postoperative 30-day mortality in adult patients with tumor craniotomy.BMC neurology · 2023Article
- Machine Learning for Benchmarking Critical Care Outcomes.Healthcare informatics research · 2023Article
- Predictors of extended length of stay related to craniotomy for tumor resection.World neurosurgery: X · 2023Article
- Ensemble machine learning algorithm for predicting acute kidney injury in patients admitted to the neurointensive care unit following brain surgery.Scientific reports · 2023Article
- Artificial Intelligence for Neurosurgery : Current State and Future Directions.Journal of Korean Neurosurgical Society · 2023Article
- Developing Predictive Models to Anticipate Shunt Complications in 33,248 Pediatric Patients with Shunted Hydrocephalus Utilizing Machine Learning.Pediatric neurosurgery · 2023Article
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4 authors.
Funding
Abstract
backgroundCurrent outcomes prediction tools are largely based on and limited by regression methods. Utilization of machine learning (ML) methods that can handle multiple diverse inputs could strengthen predictive abilities and improve patient outcomes. Inpatient length of stay (LOS) is one such outcome that serves as a surrogate for patient disease severity and resource utilization.
objectiveTo develop a novel method to systematically rank, select, and combine ML algorithms to build a model that predicts LOS following craniotomy for brain tumor.
methodsA training dataset of 41 222 patients who underwent craniotomy for brain tumor was created from the National Inpatient Sample. Twenty-nine ML algorithms were trained on 26 preoperative variables to predict LOS. Trained algorithms were ranked by calculating the root mean square logarithmic error (RMSLE) and top performing algorithms combined to form an ensemble. The ensemble was externally validated using a dataset of 4592 patients from the National Surgical Quality Improvement Program. Additional analyses identified variables that most strongly influence the ensemble model predictions.
resultsThe ensemble model predicted LOS with RMSLE of .555 (95% confidence interval, .553-.557) on internal validation and .631 on external validation. Nonelective surgery, preoperative pneumonia, sodium abnormality, or weight loss, and non-White race were the strongest predictors of increased LOS.
conclusionAn ML ensemble model predicts LOS with good performance on internal and external validation, and yields clinical insights that may potentially improve patient outcomes. This systematic ML method can be applied to a broad range of clinical problems to improve patient care.
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