An Artificial Intelligence Approach to Support Detection of Neonatal Adverse Drug Reactions Based on Severity and Probability Scores: A New Risk Score as Web-Tool

Abstract

Background: Critically ill neonates are at greater risk for adverse drug reactions (ADRs). The differentiation of ADRs from reactions associated with organ dysfunction/immaturity or genetic variability is difficult. Methods: In this prospective cohort study, each ADR was assessed using newborn-specific severity and probability scales by the clinical pharmacist. Subsequently, a machine learning-based risk score was designed to predict ADR presence in neonates. Results: In 98/412 (23.8%) of (56.3%; male) neonates included, 187 ADRs (0.42 ADR/patient) were determined related to 49 different drugs (37.12%). Drugs identified as high risk were enoxaparin, dexmedetomidine, vinblastine, dornase alfa, etoposide/carboplatin and prednisolone. The independent variables included in the risk score to predict ADR presence, according to the random forest importance criterion, were: systemic hormones (2 points), cardiovascular drugs (3 points), diseases of the circulatory system (1 point), nervous system drugs (1 point), and parenteral nutrition treatment (1 point), (cut-off value: 3 points). This risk score correctly classified 91.1% of the observations in the test set (c-index: 0.914). Conclusions: Using the high-performing risk score specific to neonates, it is expected that high-risk neonatal ADRs can be determined and prevented before they occur. Moreover, the awareness of clinicians of these drugs can be improved with this web-tool, and mitigation strategies (change of drug, dose, treatment duration, etc.) can be considered, based on a benefit-harm relationship for suspected drugs with a newborn-centered approach.

Artificial intelligence in the field of pharmacy practice: A literature review

“…For allergic, renal, CNS, and hepatic ADRs, the models had high predictive accuracies (78.9–90.2%). 11 , 12 …”

Personalized Web Services Composition Using Artificial Intelligence

“…We compare our approach in Table 2 here with several works that are worked on maching learning: 89.40 Yalçın N 2022 [20] 90. 20 Wang Z [21] 91.20 Van Laere S [22] 94.79 Yalçın N 2023 [23] 91.90 Corny J [24] 81.00 Kessler, S [25] 83.65 Dos Santos HD [26] 87.1 We compare our approach here with several works that are worked on maching learning: 3 details the comparison of our best results with those obtained by other authors in the web servises composition using Artificial Intelligence. Figure 6 compares the proposed Drug-AI model with other benchmark models in terms of accuracy, and it can be seen that the accuracy of Drug-AI is higher than that of the other models.…”

Development and validation of a predictive tool for adverse drug reactions in neonates under intensive care

“…Yalçın et al 26 have also devised a comparable tool for neonates. They identified diagnoses of cardiopathy, parenteral administration and prescription of drugs affecting the endocrine, cardiovascular and nervous systems as triggers for ADR investigation.…”

“…To the best of our knowledge, this study stands out as 1 of the first to identify independent risk factors for ADR in NICU and construct a valid predictive model for risk stratification in this population. Yalçın et al 26 . have also devised a comparable tool for neonates.…”