Abstract
BackgroundHeparin is one of the most commonly used medications in intensive care units. In clinical practice, the use of a weight-based heparin dosing nomogram is standard practice for the treatment of thrombosis. Recently, machine learning techniques have dramatically improved the ability of computers to provide clinical decision support and have allowed for the possibility of computer generated, algorithm-based heparin dosing recommendations.ObjectiveThe objective of this study was to predict the effects of heparin treatment using machine learning methods to optimize heparin dosing in intensive care units based on the predictions. Patient state predictions were based upon activated partial thromboplastin time in 3 different ranges: subtherapeutic, normal therapeutic, and supratherapeutic, respectively.MethodsRetrospective data from 2 intensive care unit research databases (Multiparameter Intelligent Monitoring in Intensive Care III, MIMIC-III; e–Intensive Care Unit Collaborative Research Database, eICU) were used for the analysis. Candidate machine learning models (random forest, support vector machine, adaptive boosting, extreme gradient boosting, and shallow neural network) were compared in 3 patient groups to evaluate the classification performance for predicting the subtherapeutic, normal therapeutic, and supratherapeutic patient states. The model results were evaluated using precision, recall, F1 score, and accuracy.ResultsData from the MIMIC-III database (n=2789 patients) and from the eICU database (n=575 patients) were used. In 3-class classification, the shallow neural network algorithm performed the best (F1 scores of 87.26%, 85.98%, and 87.55% for data set 1, 2, and 3, respectively). The shallow neural network algorithm achieved the highest F1 scores within the patient therapeutic state groups: subtherapeutic (data set 1: 79.35%; data set 2: 83.67%; data set 3: 83.33%), normal therapeutic (data set 1: 93.15%; data set 2: 87.76%; data set 3: 84.62%), and supratherapeutic (data set 1: 88.00%; data set 2: 86.54%; data set 3: 95.45%) therapeutic ranges, respectively.ConclusionsThe most appropriate model for predicting the effects of heparin treatment was found by comparing multiple machine learning models and can be used to further guide optimal heparin dosing. Using multicenter intensive care unit data, our study demonstrates the feasibility of predicting the outcomes of heparin treatment using data-driven methods, and thus, how machine learning–based models can be used to optimize and personalize heparin dosing to improve patient safety. Manual analysis and validation suggested that the model outperformed standard practice heparin treatment dosing.
Highlights
In hospitals, intensive care units are unique in that vast amounts of information are collected and displayed by computerized systems, and that the diagnostic and treatment accuracy can profoundly affect quality of care and patient outcomes [1]
The most appropriate model for predicting the effects of heparin treatment was found by comparing multiple machine learning models and can be used to further guide optimal heparin dosing
Using multicenter intensive care unit data, our study demonstrates the feasibility of predicting the outcomes of heparin treatment using data-driven methods, and how http://medinform.jmir.org/2020/6/e17648/
Summary
Intensive care units are unique in that vast amounts of information are collected and displayed by computerized systems, and that the diagnostic and treatment accuracy can profoundly affect quality of care and patient outcomes [1]. Data-driven clinical decision support systems have the potential to help clinicians optimize treatment and medication in an intensive care unit to maximize the medical effect for each individual patient [2]. Heparin is one of the most commonly used medications in intensive care units, and intravenous unfractionated heparin is a fundamental method of anticoagulant therapy. High initial rates of infusion, advanced age, and being female have been reported to be associated with supratherapeutic activated partial thromboplastin time [9,10]. Heparin administration guidelines regarding initial loading dose, maintenance dose and rate, and the activated partial thromboplastin time measurement intervals vary widely among institutions. The use of a weight-based heparin dosing nomogram is standard practice for the treatment of thrombosis. Machine learning techniques have dramatically improved the ability of computers to provide clinical decision support and have allowed for the possibility of computer generated, algorithm-based heparin dosing recommendations
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