Artificial intelligence-driven translational medicine: a machine learning framework for predicting disease outcomes and optimizing patient-centric care

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10260611" target="_blank" >RIV/61989100:27240/25:10260611 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/article/10.1186/s12967-025-06308-6" target="_blank" >https://link.springer.com/article/10.1186/s12967-025-06308-6</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1186/s12967-025-06308-6" target="_blank" >10.1186/s12967-025-06308-6</a>

Result language

angličtina

Original language name

Artificial intelligence-driven translational medicine: a machine learning framework for predicting disease outcomes and optimizing patient-centric care

Original language description

BackgroundAdvancements in artificial intelligence (AI) and machine learning (ML) have revolutionized the medical field and transformed translational medicine. These technologies enable more accurate disease trajectory models while enhancing patient-centered care. However, challenges such as heterogeneous datasets, class imbalance, and scalability remain barriers to achieving optimal predictive performance.MethodsThis study proposes a novel AI-based framework that integrates Gradient Boosting Machines (GBM) and Deep Neural Networks (DNN) to address these challenges. The framework was evaluated using two distinct datasets: MIMIC-IV, a critical care database containing clinical data of critically ill patients, and the UK Biobank, which comprises genetic, clinical, and lifestyle data from 500,000 participants. Key performance metrics, including Accuracy, Precision, Recall, F1-Score, and AUROC, were used to assess the framework against traditional and advanced ML models.ResultsThe proposed framework demonstrated superior performance compared to classical models such as Logistic Regression, Random Forest, Support Vector Machines (SVM), and Neural Networks. For example, on the UK Biobank dataset, the model achieved an AUROC of 0.96, significantly outperforming Neural Networks (0.92). The framework was also efficient, requiring only 32.4 s for training on MIMIC-IV, with low prediction latency, making it suitable for real-time applications.ConclusionsThe proposed AI-based framework effectively addresses critical challenges in translational medicine, offering superior predictive accuracy and efficiency. Its robust performance across diverse datasets highlights its potential for integration into real-time clinical decision support systems, facilitating personalized medicine and improving patient outcomes. Future research will focus on enhancing scalability and interpretability for broader clinical applications.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

30100 - Basic medicine

Project

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Continuities

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of Translational Medicine

ISSN

1479-5876

e-ISSN

1479-5876

Volume of the periodical

23

Issue of the periodical within the volume

1

Country of publishing house

GB - UNITED KINGDOM

Number of pages

18

Pages from-to

nestránkováno

UT code for WoS article

001441439400004

EID of the result in the Scopus database

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