Acquired bacterial resistance is one of the causes of mortality and morbidity from infectious diseases. Mathematical modeling allows us to predict the spread of resistance and to some extent to control its dynamics. The purpose of this review was to examine existing mathematical models in order to understand the pros and cons of currently used approaches and to build our own model. During the analysis, seven articles on mathematical approaches to studying resistance that satisfied the inclusion/exclusion criteria were selected. All models were classified according to the approach used to study resistance in the presence of an antibiotic and were analyzed in terms of our research. Some models require modifications due to the specifics of the research. The plan for further work on model building is as follows: modify some models, according to our research, check all obtained models against our data, and select the optimal model or models with the best quality of prediction. After that we would be able to build a model for the development of resistance using the obtained results.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4413671PMC
http://dx.doi.org/10.3389/fmicb.2015.00352DOI Listing

Publication Analysis

Top Keywords

build model
8
models
6
model
5
resistance
5
considered decide
4
decide build
4
mathematical
4
build mathematical
4
mathematical model
4
model predicting
4

Similar Publications

Objective: We aimed to develop a highly interpretable and effective, machine-learning based risk prediction algorithm to predict in-hospital mortality, intubation and adverse cardiovascular events in patients hospitalised with COVID-19 in Australia (AUS-COVID Score).

Materials And Methods: This prospective study across 21 hospitals included 1714 consecutive patients aged ≥ 18 in their index hospitalization with COVID-19. The dataset was separated into training (80%) and test sets (20%).

View Article and Find Full Text PDF

Objective: The objective of this research was to devise and authenticate a predictive model that employs CT radiomics and deep learning methodologies for the accurate prediction of synchronous distant metastasis (SDM) in clear cell renal cell carcinoma (ccRCC).

Methods: A total of 143 ccRCC patients were included in the training cohort, and 62 ccRCC patients were included in the validation cohort. The CT images from all patients were normalized, and the tumor regions were manually segmented via ITK-SNAP software.

View Article and Find Full Text PDF

How Outcome Prediction Could Aid Clinical Practice.

Br J Hosp Med (Lond)

January 2025

Department of Surgery & Cancer, Imperial College London, London, UK.

Predictive algorithms have myriad potential clinical decision-making implications from prognostic counselling to improving clinical trial efficiency. Large observational (or "real world") cohorts are a common data source for the development and evaluation of such tools. There is significant optimism regarding the benefits and use cases for risk-based care, but there is a notable disparity between the volume of clinical prediction models published and implementation into healthcare systems that drive and realise patient benefit.

View Article and Find Full Text PDF

The background for establishing and verifying a dehydration prediction model for elderly patients with post-stroke dysphagia (PSD) based on General Utility for Latent Process (GULP) is as follows: For elderly patients with PSD, GULP technology is utilized to build a dehydration prediction model. This aims to improve the accuracy of dehydration risk assessment and provide clinical intervention, thereby offering a scientific basis and enhancing patient prognosis. This research highlights the innovative application of GULP technology in constructing complex medical prediction models and addresses the special health needs of elderly stroke patients.

View Article and Find Full Text PDF

The deposition of monosodium urate (MSU) crystals within joint spaces produces a painful inflammatory condition known as gout, a specific form of arthritis. The condition calls for a combined curative and preventive management model. A new development in the approach to gout is that of NLRP3-targeted biologic agents, such as monoclonal therapies, to provide more accurate treatment by blocking specific pro-inflammatory cytokines.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!