Machine Learning to Calculate Heparin Dose in COVID-19 Patients with Active Cancer

Abstract

To realize a machine learning (ML) model to estimate the dose of low molecular weight heparin to be administered, preventing thromboembolism events in COVID-19 patients with active cancer. Methods: We used a dataset comprising 131 patients with active cancer and COVID-19. We considered five ML models: logistic regression, decision tree, random forest, support vector machine and Gaussian naive Bayes. We decided to implement the logistic regression model for our study. A model with 19 variables was analyzed. Data were randomly split into training (70%) and testing (30%) sets. Model performance was assessed by confusion matrix metrics on the testing data for each model as positive predictive value, sensitivity and F1-score. Results: We showed that the five selected models outperformed classical statistical methods of predictive validity and logistic regression was the most effective, being able to classify with an accuracy of 81%. The most relevant result was finding a patient-proof where python function was able to obtain the exact dose of low weight molecular heparin to be administered and thereby to prevent the occurrence of VTE. Conclusions: The world of machine learning and artificial intelligence is constantly developing. The identification of a specific LMWH dose for preventing VTE in very high-risk populations, such as the COVID-19 and active cancer population, might improve with the use of new training ML-based algorithms. Larger studies are needed to confirm our exploratory results.

Machine Learning: A New Approach for Dose Individualization

“…After removing duplicated articles, 3,346 studies were screened by the title and/or abstract, 3,175 irrelevant studies were excluded and 171 articles were included for full‐text review. Finally, 64 articles related to precision dosing using ML were included for analysis 11–74 . The PRISMA flow diagram representing the study selection process and review results is presented in Figure .…”

Construction and validation of a predictive risk model for nosocomial infections with MDRO in NICUs: a multicenter observational study

“…With the rapid development of the Internet and the in-depth integration with the medical and health industry, relying on artificial intelligence and machine learning technology, the treatment, nursing and health management of patients gradually tend to be individualized and refined ( 21 , 22 ). In this study, 13 research variables were identified through systematic literature review and expert meeting method.…”

Development, Validation and Comparison of Artificial Neural Network and Logistic Regression Models Predicting Eosinophilic Chronic Rhinosinusitis With Nasal Polyps

“…Artificial neural network (ANN) is a subset of artificial intelligence in the computing system that has been applied in the bio-medical field with splendid results, assisting in the detection and classification of certain types of diseases. 25 26 In the past 5 years, the amount of novel applications of machine learning in the field of otolaryngology has increased sharply; nevertheless, its practical uses in rhinology remain restricted. 27 Here, we aimed to assess the diagnostic accuracy of these 2 methodologies (LR and ANN) in predicting eCRSwNP on the basis of clinical and radiological variables.…”