Enhancing paediatric emergency departments management through advanced monitoring technologies and AI

"Introduction:
This project aims to integrate Artificial Intelligence (AI)and Machine Learning (ML) into Paediatric Emergency Departments (EDs) to address challenges such as long waiting times, diagnosis delays, and limited resources. AI can revolutionize patient management by improving triage, diagnosis, and treatment recommendations, and spotting early signs of diseases. Additionally, AI can enhance patient involvement with personalised treatment plans and controls processes like drug delivery (Barth, 2024) and surgeries. This study will investigate ML algorithms to provide new insights into patient management in EDs.
Research Questions:
1. Can AI algorithms effectively recognise critical patients in Paediatric EDs?
2. How can ML models detect early signs of diseases for sensible diagnosis and treatment?
3. What are the advantages of integrating AI and ML in Paediatric EDs, such as improved patient outcomes, shorter waiting times, and better resource use?
Objectives:
1. To design and develop effective continuous monitoring solutions: analyse the most relevant vital signs and physiological parameters to track, incorporating lab results and medical history for full patient analysis.
2. Investigate machine and deep learning approaches in data analysis and detection of anomalies or clinical deterioration.
3. Explore open-source Large Language Models (LLMs) for communication and reporting for clinicians, patients, and carers.
4. Assess the feasibility of effective real-time monitoring post-discharge.
Methodology:
1. Literature review and data analysis: Review and evaluate existing research on AI and ML in paediatric EDs.
2. Data collection and preprocessing: Collect and prepare a large dataset of patient medical records for ML models.
3. Model development and validation: Develop and validate AI systems for triage, early diagnosis, and treatment recommendations using several metrics.
Outcomes:
The proposed project aims to achieve the following outcomes, subdivided...