Prognosis modelling of adverse events for post-PCI treated AMI patients based on inflammation and nutrition indexes DOI Creative Commons
Yang Liu, Li Du, Yuanyuan Ge

et al.

BMC Cardiovascular Disorders, Journal Year: 2025, Volume and Issue: 25(1)

Published: Jan. 23, 2025

This study aimed to evaluate the predictive performance of inflammatory and nutritional indices for adverse cardiovascular events (ACE) in patients with acute myocardial infarction (AMI) after percutaneous coronary intervention (PCI) using a machine learning (ML) algorithm. AMI who underwent PCI were recruited randomly divided into non/ACE groups. Inflammatory graded according laboratory examination reports. Logistic Regression was used screen factors that significant ML model establishment. The performances algorithms evaluated terms accuracy, kappa, F1, receiver operating characteristic, precision recall curve, etc. Age, LVEF%, Killip Grade, heart rate, creatinine, albumin, neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte (PLR), prognostic index (PNI) significantly correlated ACE by regression analysis (P < 0.05). These nine employed establish stepwise (SR), random forest (RF), naïve Bayes (NB), decision trees (DT), artificial neutron network (ANN), whose accuracy tree greater than other trees. area under curves highest ANN compared models. had an advantage over based on age, NLR, PLR, PNI.

Language: Английский

The Role of Artificial Intelligence and Machine Learning in Predicting and Combating Antimicrobial Resistance DOI Creative Commons
Hazrat Bilal, Muhammad Nadeem Khan, Sabir Khan

et al.

Computational and Structural Biotechnology Journal, Journal Year: 2025, Volume and Issue: 27, P. 423 - 439

Published: Jan. 1, 2025

Antimicrobial resistance (AMR) is a major threat to global public health. The current review synthesizes address the possible role of Artificial Intelligence and Machine Learning (AI/ML) in mitigating AMR. Supervised learning, unsupervised deep reinforcement natural language processing are some main tools used this domain. AI/ML models can use various data sources, such as clinical information, genomic sequences, microbiome insights, epidemiological for predicting AMR outbreaks. Although relatively new fields, numerous case studies offer substantial evidence their successful application outbreaks with greater accuracy. These provide insights into discovery novel antimicrobials, repurposing existing drugs, combination therapy through analysis molecular structures. In addition, AI-based decision support systems real-time guide healthcare professionals improve prescribing antibiotics. also outlines how AI surveillance, analyze trends, enable early outbreak identification. Challenges, ethical considerations, privacy, model biases exist, however, continuous development methodologies enables play significant combating

Language: Английский

Citations

5
Photodegradation of Remazol red dye using strontium oxide nanoparticles synthesized by Stevia rebaudiana via co-precipitation method with its antimicrobial and antifungal applications DOI
Abdul Shakoor Chaudhry, Tahira Jabeen, Myrtil L. Kahn

et al.

Inorganic Chemistry Communications, Journal Year: 2025, Volume and Issue: unknown, P. 114258 - 114258

Published: March 1, 2025

Language: Английский

Citations

0
Prognosis modelling of adverse events for post-PCI treated AMI patients based on inflammation and nutrition indexes DOI Creative Commons
Yang Liu, Li Du, Yuanyuan Ge

et al.

BMC Cardiovascular Disorders, Journal Year: 2025, Volume and Issue: 25(1)

Published: Jan. 23, 2025

This study aimed to evaluate the predictive performance of inflammatory and nutritional indices for adverse cardiovascular events (ACE) in patients with acute myocardial infarction (AMI) after percutaneous coronary intervention (PCI) using a machine learning (ML) algorithm. AMI who underwent PCI were recruited randomly divided into non/ACE groups. Inflammatory graded according laboratory examination reports. Logistic Regression was used screen factors that significant ML model establishment. The performances algorithms evaluated terms accuracy, kappa, F1, receiver operating characteristic, precision recall curve, etc. Age, LVEF%, Killip Grade, heart rate, creatinine, albumin, neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte (PLR), prognostic index (PNI) significantly correlated ACE by regression analysis (P < 0.05). These nine employed establish stepwise (SR), random forest (RF), naïve Bayes (NB), decision trees (DT), artificial neutron network (ANN), whose accuracy tree greater than other trees. area under curves highest ANN compared models. had an advantage over based on age, NLR, PLR, PNI.

Language: Английский

Citations

0