Observational studyScientific reports2023
Machine learning vs. traditional regression analysis for fluid overload prediction in the ICU.
Observational study in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 21 citations in OpenAlex.
- A Data-Driven Medication Regimen Complexity Score for Critically Ill Patients: MRC-ICU 2.0.Pharmacotherapy · 2026Observational
- Enhancing Early Prediction of Gestational Diabetes Mellitus Through Data Augmentation and Feature Guidance: Model Development and Validation Study.JMIR medical informatics · 2026Article
- Prediction of pharmacist medication interventions using medication regimen complexity.JAMIA open · 2025Article
- PharmacyGPT: exploration of artificial intelligence for medication management in the intensive care unit.BMC medical informatics and decision making · 2025Article
- An interpretable machine learning tool for predicting perioperative cardiac events in patients scheduled for hip fracture surgery: insights from the multicenter LUSHIP study.Journal of anesthesia, analgesia and critical care · 2025Article
- Augmenting Mortality Prediction in Critically Ill Adults With Medication Data and Machine Learning Models.Critical care explorations · 2025Article
- Consensus recommendations for the integration of critical care pharmacists on intensive care unit teams: Endorsed by the American Association of Critical-Care Nurses, American College of Clinical Pharmacy, American Society of Health-System Pharmacists, Institute for Safe Medication Practices, and Society of Critical Care Medicine.Journal of the American College of Clinical Pharmacy : JACCP · 2025Article
- Exploring Shared and Unique Predictors of Positive and Negative Risk-Taking Behaviors Among Chinese Adolescents Through Machine-Learning Approaches: Discovering Gender and Age Variations.Journal of youth and adolescence · 2025Article
- Unsupervised machine learning analysis to identify patterns of ICU medication use for fluid overload prediction.Pharmacotherapy · 2025Article
- Prioritizing Patient Selection in Clinical Trials: A Machine Learning Algorithm for Dynamic Prediction of In-Hospital Mortality for ICU Admitted Patients Using Repeated Measurement Data.Journal of clinical medicine · 2025Article
- In-Hospital Mortality Prediction among Intensive Care Unit Patients with Acute Ischemic Stroke: A Machine Learning Approach.Health data science · 2025Article
- A common data model for the standardization of intensive care unit medication features.JAMIA open · 2024Article
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Authors and funding
10 authors at 7 institutions in 1 country.
Funding
Abstract
Fluid overload, while common in the ICU and associated with serious sequelae, is hard to predict and may be influenced by ICU medication use. Machine learning (ML) approaches may offer advantages over traditional regression techniques to predict it. We compared the ability of traditional regression techniques and different ML-based modeling approaches to identify clinically meaningful fluid overload predictors. This was a retrospective, observational cohort study of adult patients admitted to an ICU ≥ 72 h between 10/1/2015 and 10/31/2020 with available fluid balance data. Models to predict fluid overload (a positive fluid balance ≥ 10% of the admission body weight) in the 48-72 h after ICU admission were created. Potential patient and medication fluid overload predictor variables (n = 28) were collected at either baseline or 24 h after ICU admission. The optimal traditional logistic regression model was created using backward selection. Supervised, classification-based ML models were trained and optimized, including a meta-modeling approach. Area under the receiver operating characteristic (AUROC), positive predictive value (PPV), and negative predictive value (NPV) were compared between the traditional and ML fluid prediction models. A total of 49 of the 391 (12.5%) patients developed fluid overload. Among the ML models, the XGBoost model had the highest performance (AUROC 0.78, PPV 0.27, NPV 0.94) for fluid overload prediction. The XGBoost model performed similarly to the final traditional logistic regression model (AUROC 0.70; PPV 0.20, NPV 0.94). Feature importance analysis revealed severity of illness scores and medication-related data were the most important predictors of fluid overload. In the context of our study, ML and traditional models appear to perform similarly to predict fluid overload in the ICU. Baseline severity of illness and ICU medication regimen complexity are important predictors of fluid overload.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.