Related papers: KIRETT: Smart Integration of Vital Signs Data for Intelligent Decision Support in Rescue Scenarios

KIRETT: Smart Integration of Vital Signs Data for Intelligent Decision Support in Rescue Scenarios

URL: http://arxiv.org/abs/2509.25923v1
Date: Tue, 30 Sep 2025 08:20:42 GMT
Title: KIRETT: Smart Integration of Vital Signs Data for Intelligent Decision Support in Rescue Scenarios
Authors: Mubaris Nadeem, Johannes Zenkert, Christian Weber, Lisa Bender, Madjid Fathi,
Abstract summary: The KIRETT project serves to provide treatment recommendations and situation detection, combined on a wrist-worn wearable for rescue operations.<n>This paper aims to present the significant role of vital signs in the improvement of decision-making during rescue operations and show their impact on health professionals and patients in need.
Score: 0.0
License: http://creativecommons.org/licenses/by/4.0/
Abstract: The integration of vital signs in healthcare has witnessed a steady rise, promising health professionals to assist in their daily tasks to improve patient treatment. In life-threatening situations, like rescue operations, crucial decisions need to be made in the shortest possible amount of time to ensure that excellent treatment is provided during life-saving measurements. The integration of vital signs in the treatment holds the potential to improve time utilization for rescuers in such critical situations. They furthermore serve to support health professionals during the treatment with useful information and suggestions. To achieve such a goal, the KIRETT project serves to provide treatment recommendations and situation detection, combined on a wrist-worn wearable for rescue operations.This paper aims to present the significant role of vital signs in the improvement of decision-making during rescue operations and show their impact on health professionals and patients in need.

Related papers

Federated Data Analytics for Cancer Immunotherapy: A Privacy-Preserving Collaborative Platform for Patient Management [0.03262230127283451]
Connected health is a multidisciplinary approach focused on health management.<n>Data analytics can provide critical insights for informed decision-making and health co-creation.<n>This paper contributes with a collaborative digital framework integrating stakeholders across the care continuum.
arXiv Detail & Related papers (2025-10-10T08:57:41Z)
Quantitative Evaluation of KIRETT Wearable Demonstrator for Rescue Operations [0.0]
Rescue services need to provide fast, reliable treatments for the patient in need.<n>With the help of modern technologies, like treatment recommendations, real-time vitals-monitoring, and situation detection through artificial intelligence (AI) a situation can be analyzed and supported.<n>In KIRETT, a wearable device is developed to support in such scenarios and presents a way to provide treatment recommendation in rescue services.
arXiv Detail & Related papers (2025-09-30T08:21:09Z)
KIRETT: Knowledge-Graph-Based Smart Treatment Assistant for Intelligent Rescue Operations [0.0]
The need for rescue operations throughout the world has increased rapidly.<n> Demographic changes and the resulting risk of injury or health disorders form the basis for emergency calls.<n>First responders must be able to provide personalized and optimized healthcare in the shortest possible time.
arXiv Detail & Related papers (2025-08-11T10:39:15Z)
Designing AI Tools for Clinical Care Teams to Support Serious Illness Conversations with Older Adults in the Emergency Department [53.52248484568777]
The work contributes empirical understanding of ED-based serious illness conversations and provides design considerations for AI in high-stakes clinical environments.<n>We conducted interviews with two domain experts and nine ED clinical care team members.<n>We characterized a four-phase serious illness conversation workflow (identification, preparation, conduction, documentation) and identified key needs and challenges at each stage.<n>We present design guidelines for AI tools supporting SIC that fit within existing clinical practices.
arXiv Detail & Related papers (2025-05-30T21:15:57Z)
Developing clinical informatics to support direct care and population health management: the VIEWER story [1.978460175773454]
VIEWER is a clinical informatics platform designed to enhance direct patient care and population health management.<n>We describe the development and proof-of-concept implementation of VIEWER within a large UK mental health National Health Service Foundation Trust.
arXiv Detail & Related papers (2025-05-21T12:39:58Z)
IoT Integration Protocol for Enhanced Hospital Care [0.0]
This paper introduces the "IoT Integration Protocol for Enhanced Hospital Care"<n>The protocol aims to harness the potential of IoT devices to optimize patient monitoring, enable remote care, and support clinical decision-making.<n>By integrating IoT seamlessly into nursing and patient care plans, hospitals can achieve higher levels of patient-centric care and real-time data insights.
arXiv Detail & Related papers (2025-03-05T10:07:48Z)
Deep Reinforcement Learning Empowered Activity-Aware Dynamic Health Monitoring Systems [69.41229290253605]
Existing monitoring approaches were designed on the premise that medical devices track several health metrics concurrently. This means that they report all relevant health values within that scope, which can result in excess resource use and the gathering of extraneous data. We propose Dynamic Activity-Aware Health Monitoring strategy (DActAHM) for striking a balance between optimal monitoring performance and cost efficiency.
arXiv Detail & Related papers (2024-01-19T16:26:35Z)
Safe and Interpretable Estimation of Optimal Treatment Regimes [54.257304443780434]
We operationalize a safe and interpretable framework to identify optimal treatment regimes. Our findings support personalized treatment strategies based on a patient's medical history and pharmacological features.
arXiv Detail & Related papers (2023-10-23T19:59:10Z)
Pruning the Way to Reliable Policies: A Multi-Objective Deep Q-Learning Approach to Critical Care [46.2482873419289]
We introduce a deep Q-learning approach to obtain more reliable critical care policies. We evaluate our method in off-policy and offline settings using simulated environments and real health records from intensive care units.
arXiv Detail & Related papers (2023-06-13T18:02:57Z)
SPeC: A Soft Prompt-Based Calibration on Performance Variability of Large Language Model in Clinical Notes Summarization [50.01382938451978]
We introduce a model-agnostic pipeline that employs soft prompts to diminish variance while preserving the advantages of prompt-based summarization. Experimental findings indicate that our method not only bolsters performance but also effectively curbs variance for various language models.
arXiv Detail & Related papers (2023-03-23T04:47:46Z)
Optimal discharge of patients from intensive care via a data-driven policy learning framework [58.720142291102135]
It is important that the patient discharge task addresses the nuanced trade-off between decreasing a patient's length of stay and the risk of readmission or even death following the discharge decision. This work introduces an end-to-end general framework for capturing this trade-off to recommend optimal discharge timing decisions. A data-driven approach is used to derive a parsimonious, discrete state space representation that captures a patient's physiological condition.
arXiv Detail & Related papers (2021-12-17T04:39:33Z)
Reinforcement Learning with Efficient Active Feature Acquisition [59.91808801541007]
In real-life, information acquisition might correspond to performing a medical test on a patient. We propose a model-based reinforcement learning framework that learns an active feature acquisition policy. Key to the success is a novel sequential variational auto-encoder that learns high-quality representations from partially observed states.
arXiv Detail & Related papers (2020-11-02T08:46:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.