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

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. We used a dataset comprising 131 patients with active cancer and COVID-19. We considered five ML models: logist...

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Bibliographic Details
Published in:Journal of clinical medicine 2021-12, Vol.11 (1), p.219
Main Authors: Imbalzano, Egidio, Orlando, Luana, Sciacqua, Angela, Nato, Giuseppe, Dentali, Francesco, Nassisi, Veronica, Russo, Vincenzo, Camporese, Giuseppe, Bagnato, Gianluca, Cicero, Arrigo F G, Dattilo, Giuseppe, Vatrano, Marco, Versace, Antonio Giovanni, Squadrito, Giovanni, Di Micco, Pierpaolo
Format: Article
Language:English
Subjects:
Algorithms
Anticoagulants
Artificial intelligence
Cancer therapies
Cardiovascular disease
Chemotherapy
Clinical medicine
Coronaviruses
COVID-19
Datasets
Machine learning
Molecular weight
Mortality
Pneumonia
Pulmonary embolisms
Severe acute respiratory syndrome coronavirus 2
Thromboembolism
Thrombosis
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Summary: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. 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. 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. 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.
ISSN:2077-0383
2077-0383
DOI:10.3390/jcm11010219