Personalized drug concentration predictions with machine learning: an exploratory study

Abstract

Background: The dose individualization by therapeutic drug monitoring (TDM) can be improved if population-based reference ranges are available, as there is large inter- and intrapatient variability. If these ranges are not available, dose individualization may not be optimal. Machine learning can help achieve accurate drug dose settings and predict the resultant levels.Methods: Two random forest models, a multi-class classifier to predict dose and a regression model to predict blood drug level were trained on 320 patients’ data, consisting of their age, sex, dose and blood drug level. The classifier consisted of 1000 estimators (decision trees) and the regression model consisted of 1300 estimators. The model was evaluated on randomly split test set having 10% of the total dataset size. The regression model was compared against k-Nearest neighbor and linear regression models. The classifier was evaluated using accuracy, precision, and F1 Score; the regression model was evaluated using R2, Root mean squared error, and mean absolute error.Results: The classifier had an out-of-sample accuracy of 68.75%, average precision of 0.7567, and an average F1 score of 0.6907. The regression model had an out-of-sample R2 value of 0.2183, root mean squared value of 3.7359, and a mean absolute error of 2.5156. These values signify an average classification performance, and a below-average regression performance due to small dataset.Conclusions: It is possible for machine learning algorithms to be used in therapeutic drug monitoring. With a well-structured, rich, and large dataset, a very accurate model can be built.

Possibilities of Obtaining and Valorizing Dietary Fibers in the Context of the Circular Bioeconomy

“…Like all fruits contains a large amount of free moisture and soluble carbohydrates, a small amount of fats and proteins. The vitamin composition in quince is different and is represented by both water-soluble (group B and vitamin C) and life-giving vitamins (A, E, K) [27]. To mineral substances can be attributed potassium, phosphorus, calcium, magnesium, a little sodium, iron and zinc.…”

“…To mineral substances can be attributed potassium, phosphorus, calcium, magnesium, a little sodium, iron and zinc. In addition to the listed components of quince fruit, they also include organic acids, polyphenolic compounds, and also certain types of amino acids (aspartic acid, asparagine and glycine) [27]. The active ingredients in the fruit are the tannin in the seeds, but it should be noted that consuming the seeds in large quantities can be toxic.…”

“…Other active ingredients include pectin, mucilage, sugars and organic acids found directly in the fruit. Dietary fibers also have their specific weight in this fruit and are present in both forms: soluble mainly in the form of pectin and insoluble -cellulose and lignin [27].…”

Development and evaluation of uncertainty quantifying machine learning models to predict piperacillin plasma concentrations in critically ill patients

“…Earlier works already explored the idea of using various ML models, such as support vector machines, gradient boosting trees, XGBoost, and neural networks, to predict drug concentrations for tacrolimus, remifentanil, gentamicin, risperidone, teicoplanin, phenytoin, and warfarin [22][23][24][25][26][27][28][29]. A recent study explained and validated the predictions of teicoplanin trough concentrations using Shapley values while combining the best models into a single ensemble [28,30].…”

Development and Implementation of the Data Science Learning Platform for Research Physician

“…These tools will be furthermore useful to identify sub-groups of patients in a given population. Due to the large amounts of data, medical doctors are facing challenges to recognize symptoms and to identify disease in early stage as well as to choose optimal treatment [1].…”