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Updated: May 9, 2026

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis

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Published on: September 26, 2018

Can Machine Learning Personalize Cardiovascular Therapy in Sepsis?

Finneas J R Catling^1,2, Myura Nagendran^2,3, Paul Festor^3,4

¹Institute of Healthcare Engineering, University College London, London, United Kingdom.

Critical Care Explorations

|May 6, 2024

Summary

This summary is machine-generated.

Machine learning may personalize sepsis treatment by addressing patient heterogeneity. However, current systems need more clinical trials and better data for safe, effective use in critical care.

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A Data-Driven Approach to Quantifying Immune States in Sepsis

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

Area of Science:

Critical Care Medicine
Biomedical Informatics
Cardiovascular Physiology

Background:

Sepsis treatment trials often fail due to patient heterogeneity, particularly in cardiovascular responses during septic shock.
Personalized medicine approaches are needed to improve outcomes in sepsis resuscitation.

Purpose of the Study:

To explore the potential of machine learning (ML) systems for personalizing cardiovascular resuscitation in sepsis.
To identify current limitations and propose solutions for translating ML into clinical practice for sepsis management.

Main Methods:

Review of existing literature on ML applications in sepsis resuscitation.
Discussion of technological readiness, data requirements, and clinical validation challenges.
Analysis of barriers to clinical translation, including data quality and model generalization.

Main Results:

Numerous ML "proofs of concept" exist, but technological readiness is low.
Current electronic health record data lacks the resolution for actionable ML-driven treatment suggestions.
Significant challenges remain regarding confounding, generalizability, and clinical validation.

Conclusions:

Translating ML for sepsis resuscitation requires addressing technical hurdles and implementation barriers.
Recommendations include improving data quality, using causal models, ensuring safety, and conducting clinical trials.
A concerted effort is needed to bridge the gap between ML development and clinical application in sepsis care.