Real-Time AI Dashboards for ICU Monitoring and Alerting

The use of AI in developing real-time dashboards to track vital signs in Intensive Care Unit (ICU) patients is a great achievement in the medical field. It combines big data and machine learning with IoT to monitor a patient's status and provide alerts for clinicians to act before their condition worsens. By integrating data from the various sensors used in the ICU, the system presents signs that may warn clinicians of an expected clinical change, enabling the clinicians to prevent the occurrence of the event. As evidenced by the pilot testing, the system efficiently cuts response time and minimises adverse events, thus enhancing patient outcomes. The use of CNNs and LSTMs has led to a reduction of critical incidents by 25% and an enhanced response time by 30%. Nonetheless, future studies are needed to fine-tune the system so that it can be adopted in more healthcare organisations. In summary, the described AI-powered dashboard system has great potential for improving the management of ICUs and assisting clinicians in making better decisions that could improve the quality of care provided to patients in intensive care environments.

Authors

Institutions

Publication Details

Journal
European Journal of Computer Science and Information Technology
Published
2025-04-15
DOI
https://doi.org/10.37745/ejcsit.2013/vol13n121523
Citations
1
Primary Topic
Healthcare Technology and Patient Monitoring
Type
article
Field-Weighted Citation Impact
2.01
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Real-Time AI Dashboards for ICU Monitoring and Alerting

Rajesh Aakula
1 citations
European Journal of Computer Science and Information Technology
Healthcare Technology and Patient Monitoring
2.01
article

Real-Time AI Dashboards for ICU Monitoring and Alerting

Rajesh Aakula
article en
1 citations

Abstract

The use of AI in developing real-time dashboards to track vital signs in Intensive Care Unit (ICU) patients is a great achievement in the medical field. It combines big data and machine learning with IoT to monitor a patient's status and provide alerts for clinicians to act before their condition worsens. By integrating data from the various sensors used in the ICU, the system presents signs that may warn clinicians of an expected clinical change, enabling the clinicians to prevent the occurrence of the event. As evidenced by the pilot testing, the system efficiently cuts response time and minimises adverse events, thus enhancing patient outcomes. The use of CNNs and LSTMs has led to a reduction of critical incidents by 25% and an enhanced response time by 30%. Nonetheless, future studies are needed to fine-tune the system so that it can be adopted in more healthcare organisations. In summary, the described AI-powered dashboard system has great potential for improving the management of ICUs and assisting clinicians in making better decisions that could improve the quality of care provided to patients in intensive care environments.

European Journal of Computer Science and Information TechnologyVol. 13(12)
English Heritage (GB)
Openalex Percentile: Top 16%
Healthcare Technology and Patient Monitoring
2.01
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Real-Time AI Dashboards for ICU Monitoring and Alerting — Rajesh Aakula · European Journal of Computer Science and Information Technology (2025) | TGRS Research Map | TGRS