ArticleIntensive care medicine2014
A data-driven approach to optimized medication dosing: a focus on heparin.
Article in Intensive care medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Analysis of aPTT predictors after unfractionated heparin administration in intensive care units using machine learning models.PloS one · 2025Article
- Clinical decision making under uncertainty: a bootstrapped counterfactual inference approach.BMC medical informatics and decision making · 2024Article
- Medical Informatics Operating Room Vitals and Events Repository (MOVER): a public-access operating room database.JAMIA open · 2023Article
- A Recurrent Neural Network Model for Predicting Activated Partial Thromboplastin Time After Treatment With Heparin: Retrospective Study.JMIR medical informatics · 2022Article
- Predicting Therapeutic Response to Unfractionated Heparin Therapy: Machine Learning Approach.Interactive journal of medical research · 2022Article
- Early warning of citric acid overdose and timely adjustment of regional citrate anticoagulation based on machine learning methods.BMC medical informatics and decision making · 2021Article
- A Clinical Prediction Model to Predict Heparin Treatment Outcomes and Provide Dosage Recommendations: Development and Validation Study.Journal of medical Internet research · 2021Article
- Convergence of Precision Medicine and Public Health Into Precision Public Health: Toward a Big Data Perspective.Frontiers in public health · 2021Review
- Generalizable deep temporal models for predicting episodes of sudden hypotension in critically ill patients: a personalized approach.Scientific reports · 2020Article
- Toward Optimal Heparin Dosing by Comparing Multiple Machine Learning Methods: Retrospective Study.JMIR medical informatics · 2020Article
- Using Self Organizing Maps to Compare Sepsis Patients from the Neonatal and Adult Intensive Care Unit.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
- Dynamic multi-outcome prediction after injury: Applying adaptive machine learning for precision medicine in trauma.PloS one · 2019Article
- A Deep Deterministic Policy Gradient Approach to Medication Dosing and Surveillance in the ICU.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2018Article
- Non-antiarrhythmic interventions in new onset and paroxysmal sepsis-related atrial fibrillation.Intensive care medicine · 2018Article
- Focus on transfusion, bleeding and thrombosis.Intensive care medicine · 2016Article
- Transforming big data into computational models for personalized medicine and health care.Dialogues in clinical neuroscience · 2016Review
- Are Randomized Controlled Trials the (G)old Standard? From Clinical Intelligence to Prescriptive Analytics.Journal of medical Internet research · 2016Article
- Machine Learning and Decision Support in Critical Care.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2016Article
- Article
- A Visualization of Evolving Clinical Sentiment Using Vector Representations of Clinical Notes.Computing in cardiology · 2015Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
purposeTo demonstrate a novel method that utilizes retrospective data to develop statistically optimal dosing strategies for medications with sensitive therapeutic windows. We illustrate our approach on intravenous unfractionated heparin, a medication which typically considers only patient weight and is frequently misdosed.
methodsWe identified available clinical features which impact patient response to heparin and extracted 1,511 patients from the multi-parameter intelligent monitoring in intensive care II database which met our inclusion criteria. These were used to develop two multivariate logistic regressions, modeling sub- and supra-therapeutic activated partial thromboplastin time (aPTT) as a function of clinical features. We combined information from these models to estimate an initial heparin dose that would, on a per-patient basis, maximize the probability of a therapeutic aPTT within 4-8 h of the initial infusion. We tested our model's ability to classifying therapeutic outcomes on a withheld dataset and compared performance to a weight-alone alternative using volume under surface (VUS) (a multiclass version of AUC).
resultsWe observed statistically significant associations between sub- and supra-therapeutic aPTT, race, ICU type, gender, heparin dose, age and Sequential Organ Failure Assessment scores with mean validation AUC of 0.78 and 0.79 respectively. Our final model improved outcome classification over the weight-alone alternative, with VUS values of 0.48 vs. 0.42.
conclusionsThis work represents an important step in the secondary use of health data in developing models to optimize drug dosing. The next step would be evaluating whether this approach indeed achieves target aPTT more reliably than the current weight-based heparin dosing in a randomized controlled trial.
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