Observational studyThe Annals of pharmacotherapy2021
Development of Machine Learning Models to Validate a Medication Regimen Complexity Scoring Tool for Critically Ill Patients.
Observational study in The Annals of pharmacotherapy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- A Data-Driven Medication Regimen Complexity Score for Critically Ill Patients: MRC-ICU 2.0.Pharmacotherapy · 2026Observational
- 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
- Augmenting Mortality Prediction in Critically Ill Adults With Medication Data and Machine Learning Models.Critical care explorations · 2025Article
- A common data model for the standardization of intensive care unit medication features.JAMIA open · 2024Article
- Unsupervised machine learning analysis to identify patterns of ICU medication use for fluid overload prediction.medRxiv : the preprint server for health sciences · 2024Article
- The use of electronic health record embedded MRC-ICU as a metric for critical care pharmacist workload.JAMIA open · 2023Article
- Medication regimen complexity (MRC-ICU) for in-hospital mortality prediction in COVID-19 patients.Hospital pharmacy · 2023Article
- An Evaluation of the Relationship Between Medication Regimen Complexity as Measured by the MRC-ICU to Medication Errors in Critically Ill Patients.Hospital pharmacy · 2023Article
- Machine learning vs. traditional regression analysis for fluid overload prediction in the ICU.Scientific reports · 2023Observational
- Cluster analysis driven by unsupervised latent feature learning of medications to identify novel pharmacophenotypes of critically ill patients.Scientific reports · 2023Article
- Evaluation of medication regimen complexity as a predictor for mortality.Scientific reports · 2023Observational
- Pharmacophenotype identification of intensive care unit medications using unsupervised cluster analysis of the ICURx common data model.Critical care (London, England) · 2023Observational
- Medication Regimen Complexity Index Score at Admission as a Predictor of Inpatient Outcomes: A Machine Learning Approach.International journal of environmental research and public health · 2023Article
- Evaluating the Medication Regimen Complexity Score as a Predictor of Clinical Outcomes in the Critically Ill.Journal of clinical medicine · 2022Article
- A descriptive report of the rapid implementation of automated MRC-ICU calculations in the EMR of an academic medical center.American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists · 2022Article
- Medication regimen complexity vs patient acuity for predicting critical care pharmacist interventions.American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists · 2022Observational
- Optimization of critical care pharmacy clinical services: A gap analysis approach.American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists · 2021Article
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Authors and funding
3 authors.
Funding
Abstract
introductionThe Medication Regimen Complexity -Intensive Care Unit (MRC-ICU) is the first tool for measuring medication regimen complexity in critically ill patients. This study tested machine learning (ML) models to investigate the relationship between medication regimen complexity and patient outcomes.
methodsThis study was a single-center, retrospective observational evaluation of 130 adults admitted to the medical ICU. The MRC-ICU score was utilized to improve the inpatient model's prediction accuracy. Three models were proposed: model I, demographic data without medication data; model II, demographic data and medication regimen complexity variables; and model III: demographic data and the MRC-ICU score. A total of 6 ML classifiers was developed: k-nearest neighbor (KNN), naïve Bayes (NB), random forest, support vector machine, neural network, and logistic classifier (LC). They were developed and tested using electronic health record data to predict inpatient mortality.
resultsThe results demonstrated that adding medication regimen complexity variables (model II) and the MRC-ICU score (model III) improved inpatient mortality prediction.. The LC outperformed the other classifiers (KNN and NB), with an overall accuracy of 83%, sensitivity (Se) of 87%, specificity of 67%, positive predictive value of 93%, and negative predictive value of 46%. The APACHE III score and the MRC-ICU score at the 24-hour interval were the 2 most important variables. CONCLUSION AND RELEVANCE: Inclusion of the MRC-ICU score improved the prediction of patient outcomes on the previously established APACHE III score. This novel, proof-of-concept methodology shows promise for future application of the MRC-ICU scoring tool for patient outcome predictions.
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