Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Antibiotic Selection00:57

Antibiotic Selection

52.3K
Overview
52.3K
Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

5.6K
The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
5.6K
Combined Effects of Drugs: Synergism01:27

Combined Effects of Drugs: Synergism

3.7K
Synergism is a useful mechanism where combining two or more drugs is more effective than each constituent used alone. Such combinations are also called supra-additive interactions. The drugs collectively enhance the final therapeutic effect by acting on different targets. Another advantage is that the low dose of each constituent drug is sufficient to achieve the desired effect. This helps reduce the duration of therapy and lower the adverse effects of these drugs.
Such synergistic combinations...
3.7K

You might also read

Related Articles

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

Sort by
Same author

Counterfactual AI for Dynamic Dose Optimization with Side-Effect Constraints.

IEEE journal of biomedical and health informatics·2026
Same author

Quantifying replication stress in cancer without proliferation confounding.

Cell stress·2025
Same author

Novel definition of time range and risk factors of pregnant women with gestational diabetes mellitus detected early in pregnancy a cluster analysis using clinical data of the German GestDiab cohort.

Diabetology & metabolic syndrome·2025
Same author

Editorial: AI and inverse methods for building digital twins in neuroscience.

Frontiers in computational neuroscience·2025
Same author

Identification of antibody-resistant SARS-CoV-2 mutants via N4-Hydroxycytidine mutagenesis.

Antiviral research·2024
Same author

Profiling Numerical and Structural Chromosomal Instability in Different Cancer Types.

Methods in molecular biology (Clifton, N.J.)·2024
Same journal

Explainable foundation model for dementia screening and risk stratification using retinal fundus images.

NPJ digital medicine·2026
Same journal

LLM research on public biosignals data is needed to protect patients.

NPJ digital medicine·2026
Same journal

Conversational artificial intelligence for pre-procedural patient preparation: implementation, validation and patient satisfaction.

NPJ digital medicine·2026
Same journal

Whole body CT attenuation and volume charts from routine clinical scans via LLM report filtering.

NPJ digital medicine·2026
Same journal

Fast information and slow evidence in the large language models era.

NPJ digital medicine·2026
Same journal

Predicting response to neoadjuvant therapy using artificial intelligence on digitized histopathology slides: a systematic review.

NPJ digital medicine·2026
See all related articles

Related Experiment Video

Updated: Jun 6, 2025

Design of Cecal Ligation and Puncture and Intranasal Infection Dual Model of Sepsis-Induced Immunosuppression
07:30

Design of Cecal Ligation and Puncture and Intranasal Infection Dual Model of Sepsis-Induced Immunosuppression

Published on: June 15, 2019

9.9K

An optimal antibiotic selection framework for Sepsis patients using Artificial Intelligence.

Philipp Wendland1, Christof Schenkel-Häger2, Ingobert Wenningmann3

  • 1University of Applied Sciences Koblenz, Department of Mathematics and Technology, Remagen, 53424, Germany.

NPJ Digital Medicine
|November 29, 2024
PubMed
Summary
This summary is machine-generated.

OptAB is a novel AI model for sepsis patients, optimizing antibiotic selection to reduce organ failure and side effects. This data-driven approach shows faster treatment efficacy compared to standard methods.

More Related Videos

A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

147
Multiplex Therapeutic Drug Monitoring by Isotope-dilution HPLC-MS/MS of Antibiotics in Critical Illnesses
11:17

Multiplex Therapeutic Drug Monitoring by Isotope-dilution HPLC-MS/MS of Antibiotics in Critical Illnesses

Published on: August 30, 2018

12.8K

Related Experiment Videos

Last Updated: Jun 6, 2025

Design of Cecal Ligation and Puncture and Intranasal Infection Dual Model of Sepsis-Induced Immunosuppression
07:30

Design of Cecal Ligation and Puncture and Intranasal Infection Dual Model of Sepsis-Induced Immunosuppression

Published on: June 15, 2019

9.9K
A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

147
Multiplex Therapeutic Drug Monitoring by Isotope-dilution HPLC-MS/MS of Antibiotics in Critical Illnesses
11:17

Multiplex Therapeutic Drug Monitoring by Isotope-dilution HPLC-MS/MS of Antibiotics in Critical Illnesses

Published on: August 30, 2018

12.8K

Area of Science:

  • Artificial Intelligence in Medicine
  • Pharmacology and Therapeutics
  • Critical Care Medicine

Background:

  • Sepsis management requires timely and effective antibiotic selection.
  • Antibiotic side effects like nephrotoxicity and hepatotoxicity complicate treatment.
  • Current models often lack real-time adaptability and comprehensive side-effect consideration.

Purpose of the Study:

  • To introduce OptAB, a data-driven, AI-powered antibiotic selection model for sepsis.
  • To optimize antibiotic treatment by minimizing the Sepsis-related Organ Failure Assessment (SOFA) score.
  • To integrate the prediction and mitigation of antibiotic-induced nephrotoxicity and hepatotoxicity.

Main Methods:

  • Development of a hybrid neural network differential equation algorithm.
  • Utilizing a completely data-driven, online-updateable approach for real-world patient data.
  • Forecasting disease progression and laboratory values (creatinine, bilirubin, alanine transaminase) to identify side effects.

Main Results:

  • OptAB demonstrates faster efficacy with its selected optimal antibiotics compared to administered ones.
  • The model effectively handles complex patient data characteristics, including irregular measurements and missing values.
  • OptAB provides disease progression forecasts and learns treatment influences on SOFA score and key laboratory values.

Conclusions:

  • OptAB represents a significant advancement in AI-driven sepsis treatment.
  • The model offers a personalized and adaptive approach to antibiotic selection, improving patient outcomes.
  • OptAB's ability to account for side effects enhances its clinical applicability and safety profile.