Evidence map›Paper›PMID 40976950›Full record

ArticleBasic & clinical pharmacology & toxicology2025

Development of an Artificial Intelligence Powered Medication Risk Score Calculator Application.

Ádám Bertalan, Viola Angyal, Péter Domján, Eva Aggerholm Sædder, Gyula Király, Lóránd Erdélyi, Nóra Gyimesi, Elek Dinya

Abstract read
In one paragraph

Article in Basic & clinical pharmacology & toxicology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Ádám BertalanInstitute of Digital Health Sciences, Health Sciences Division, Doctoral College, Semmelweis University, Budapest, Hungary.ORCID https://orcid.org/0000-0002-1325-0012
Viola AngyalInstitute of Digital Health Sciences, Health Sciences Division, Doctoral College, Semmelweis University, Budapest, Hungary.ORCID https://orcid.org/0000-0002-1749-7338
Péter DomjánInterdisciplinary Applied Health Sciences Program, Health Sciences Division, Doctoral College, Semmelweis University, Budapest, Hungary.ORCID https://orcid.org/0009-0001-4595-7952
Eva Aggerholm SædderDepartment of Biomedicine, Aarhus University, Aarhus, Denmark.ORCID https://orcid.org/0000-0003-4905-1004
Gyula KirályHospitaly Kft., Budapest, Hungary.
Lóránd ErdélyiFaculty of Health and Sport Sciences, Széchenyi István University, Győr, Hungary.ORCID https://orcid.org/0009-0003-6598-8291
Nóra GyimesiDr. Manninger Jenő National Traumatology Center, Budapest, Hungary.
Elek DinyaInstitute of Digital Health Sciences, Health Sciences Division, Doctoral College, Semmelweis University, Budapest, Hungary.ORCID https://orcid.org/0000-0002-2620-8649

Funding

National Research, Development and Innovation Office
6 · The paper itself

Abstract

The publication explores the development of the Augmented Medication Risk Score (AUGMERIS) calculator, a web application supported by artificial intelligence, designed to automate the evaluation of medication therapies with the Danish Medication Risk Score (MERIS) method. It is a tool that assesses drug combinations and kidney function in estimated glomerular filtration rate (eGFR), which helps clinical pharmacists identify high-risk patients. To overcome the problem of processing unstructured electronic health records (EHRs), a hybrid text processing model was created by combining rigorous algorithms and Generative Pre-trained Transformer (GPT) technology, which was integrated into a web application along with an automated risk calculation programme. Our objective was to develop and test a globally accessible calculator application with the validation of performance on poor-quality data. Despite the validation limitations, the text processing function serves the application satisfactorily. The AUGMERIS web app is built with Python 3 and shared globally by Streamlit. Volunteer testers from eight different countries performed a total of 383 trial calculations. The application has the potential to improve global pharmacotherapy by identifying patients requiring medication reviews. Its wider adoption might enhance patient safety and optimize treatments in a variety of healthcare systems.

Indexed as

Artificial IntelligenceDrug-Related Side Effects and Adverse ReactionsAlgorithmsElectronic Health RecordsGlomerular Filtration RateHumansRisk Assessmentartificial intelligenceclinical pharmacydata miningdecision supportmedication risk

Identifiers

PMID40976950
PMCPMC12451093

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

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.