Evidence map›Paper›PMID 41686358›Full record

Observational studyClinical drug investigation2026

ContraIndicator: A Natural Language Processing-Based Approach to Potential Drug-Drug Interaction Detection in Pediatric Intensive Care.

Pradeep Singh, Akshaya Devadiga, Ridam Pal, Pranam R Betrabet, Jhuma Sankar, Tavpritesh Sethi, Rakesh Lodha

Abstract readObservational Study
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Observational study in Clinical drug investigation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

7 authors.

Pradeep Singh *Indraprastha Institute of Information Technology, New Delhi, India.
Akshaya Devadiga *Indraprastha Institute of Information Technology, New Delhi, India.
Ridam PalIndraprastha Institute of Information Technology, New Delhi, India.
Pranam R BetrabetManipal Academy of Higher Education, Manipal, Karnataka, India.
Jhuma SankarAll India Institute of Medical Sciences, New Delhi, India.
Tavpritesh SethiIndraprastha Institute of Information Technology, New Delhi, India.
Rakesh LodhaAll India Institute of Medical Sciences, New Delhi, India. rlodha1661@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

objectivesThe intricate nature of pediatric patients' physiology and the frequent administration of multiple medications in pediatric intensive care units expose hospitalized patients to potential drug-drug interactions. Therefore, this study aimed to examine and characterize clinically relevant drug-drug interactions in pediatric patients admitted to intensive care units, leveraging artificial intelligence to enhance their identification and management.

methodsPrescription data from pediatric intensive care unit admissions were analyzed using ContraIndicator, a natural language processing-based framework developed for the identification of potential drug-drug interactions. Medication information extracted from prescription records was evaluated against the DDInter database, and its clinical relevance was determined using the severity ratings of the database (major, moderate, and minor). Using univariate and multivariate logistic regression, the associations between severity and age, as well as between severity and the number of prescribed medications, were investigated.

resultsThe examination of 8010 prescriptions for 899 hospitalized patients identified 3884 potential drug-drug interactions. The most frequent interacting drug pairs were midazolam + fluconazole, fluconazole + fentanyl, fentanyl + dexamethasone, and vancomycin + piperacillin. Among the 49.6% (446) of pediatric patients who had at least one interaction (major, moderate, or minor), 38.8% (173) had a major potential drug-drug interaction. In this investigation, a higher number of prescribed drugs was significantly linked with a higher incidence of potential drug-drug interactions (odds ratio: 12.79; 95% confidence interval: 8.26-20.6; p < 0.001).

conclusionsThe study suggests that potential drug-drug interactions are frequent in pediatric intensive care units. The majority of these interactions were moderate, followed by major severity. There is a statistically significant association between the quantity of drugs provided and the prevalence of potential drug-drug interactions. The findings of this study could aid in planning and executing future studies, as well as in monitoring and preventing potential drug-drug interactions in patients receiving intensive care. This study introduces ContraIndicator, a graphical user interface that presents empirical findings and promotes awareness regarding potential drug-drug interactions.

Indexed as

Intensive Care Units, PediatricNatural Language ProcessingAdolescentChildChild, PreschoolDatabases, FactualDrug InteractionsFemaleHumansInfantMale

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.