Search research articles

Related Concept Videos

Steps in Outbreak Investigation

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Effect of emergence profile and space gap size on excess cement in cement-retained implant reconstructions.

Journal of advanced periodontology & implant dentistry·2025

Same author

Effectiveness of virtual reality and computerized training programs for enhancing emotion recognition in people with autism spectrum disorder: a systematic review and meta-analysis.

International journal of developmental disabilities·2024

Same author

Central Auditory Processing Impairment in Renal Failure.

Indian journal of otolaryngology and head and neck surgery : official publication of the Association of Otolaryngologists of India·2024

Same author

Parkinson's disease tremor prediction using EEG data analysis-A preliminary and feasibility study.

BMC neurology·2023

Same author

Association of vitamin A and its organic compounds with stroke - a systematic review and meta-analysis.

Nutritional neuroscience·2022

Same author

Reply to letter to the Editor.

International archives of occupational and environmental health·2022

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Jun 3, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Prediction of urinary tract infection using machine learning methods: a study for finding the most-informative

Sajjad Farashi^1,2, Hossein Emad Momtaz^3,4

¹Neurophysiology Research Center, Institute of Neuroscience and Mental Health, Avicenna Health Research Institute, Hamadan University of Medical Sciences, Hamadan, Iran. sajjad_farashi@yahoo.com.

BMC Medical Informatics and Decision Making

|January 9, 2025

Summary

Machine learning accurately predicts urinary tract infections (UTIs) using urine, blood, and demographic data. This approach offers a faster, reliable alternative to traditional urine cultures, aiding antibiotic stewardship.

Keywords:

Feature extraction Machine learning Prediction Urinary tract infection

More Related Videos

Urinary Tract Infection in a Small Animal Model: Transurethral Catheterization of Male and Female Mice

Urinary Tract Infection in a Small Animal Model: Transurethral Catheterization of Male and Female Mice

Published on: December 1, 2017

Isolation of Single Intracellular Bacterial Communities Generated from a Murine Model of Urinary Tract Infection for Downstream Single-cell Analysis

Isolation of Single Intracellular Bacterial Communities Generated from a Murine Model of Urinary Tract Infection for Downstream Single-cell Analysis

Published on: April 16, 2019

Related Experiment Videos

Last Updated: Jun 3, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Urinary Tract Infection in a Small Animal Model: Transurethral Catheterization of Male and Female Mice

Urinary Tract Infection in a Small Animal Model: Transurethral Catheterization of Male and Female Mice

Published on: December 1, 2017

Isolation of Single Intracellular Bacterial Communities Generated from a Murine Model of Urinary Tract Infection for Downstream Single-cell Analysis

Isolation of Single Intracellular Bacterial Communities Generated from a Murine Model of Urinary Tract Infection for Downstream Single-cell Analysis

Published on: April 16, 2019

Area of Science:

Medical Informatics
Computational Biology
Clinical Diagnostics

Background:

Urinary tract infections (UTIs) are common and can lead to serious health issues.
Current diagnostic methods like urine culture are slow and prone to errors.
There is a need for rapid and reliable UTI diagnostic tools to prevent antibiotic resistance.

Purpose of the Study:

To identify key predictive variables for urinary tract infection (UTI) using machine learning.
To evaluate the efficacy of various machine learning models in UTI prediction.
To establish a more efficient diagnostic approach for UTIs.

Main Methods:

Employed diverse machine learning algorithms, including classical and deep learning models.
Analyzed a dataset incorporating urine test results, blood test parameters, and demographic information.
Utilized an ensemble model combining XGBoost, decision tree, and light gradient boosting with a voting mechanism.

Main Results:

Identified 18 informative features from urine (e.g., WBC, nitrite), blood (e.g., MPV, lymphocyte), and demographics (age, gender).
The ensemble model achieved high accuracy (85.64%) and AUC (88.53%) in UTI prediction.
Significance of gender and age as crucial factors in UTI prediction was demonstrated.

Conclusions:

Machine learning models show significant potential for accurate and efficient UTI prediction.
This approach can complement traditional methods, improving diagnostic speed and accuracy.
The findings support the integration of machine learning in clinical decision-making for UTIs.