Optimizing Patient Outcomes Through Multi Source Data Analysis In

Optimizing Patient Outcomes Through Multi-Source Data Analysis in Healthcare

The landscape of healthcare is transformed by the integration of advanced data analytics, especially in the realm of multi-source data analysis. By combining diverse datasets, such as electronic health records (EHRs), genetic information, wearable device data, and patient-reported outcomes, healthcare providers can gain a comprehensive understanding of a patient's health status. This approach creates more personalized treatment plans, enhances diagnostic accuracy, and supports early detection of potential health issues. Communication between various data sources allows for the identification of hidden trends and patterns, improving predictive capabilities and optimizing patient outcomes. As healthcare systems adopt this data-driven process, it is crucial to address challenges related to data privacy, integration, and the interpretation of complex datasets, ensuring the potential benefits of multi-source data analysis are realized in ethical and effective ways. Optimizing Patient Outcomes Through Multi-Source Data Analysis in Healthcare explores the transformative potential of big data and AI in healthcare, focusing on informed decision-making. It delves into the integration of vast, diverse datasets, analyzed through AI algorithms to enhance patient outcomes and operational efficiency. This book covers topics such as automation, machine learning, and neural networks, and is a useful resource for healthcare professionals, computer engineers, business owners, academicians, researchers, and data scientists.

OPTIMIZING PATIENT OUTCOMES THROUGH MULTI-SOURCE DATA ANALYSIS IN.

INTERDISCIPLINARY WORK OF SCIENCE AND TECHNOLOGY IN MATERNAL AND CHILD CARE

Artificial Intelligence (AI) is revolutionizing healthcare by enhancing predictive capabilities, particularly in managing pregnancy and delivery complications. This paper explores how AI, leveraging machine learning (ML) and deep learning (DL) techniques, can forecast potential complications during pregnancy and childbirth. Through an extensive review of existing literature and analysis of various AI methodologies, the paper evaluates AI's effectiveness in predicting complications such as preeclampsia, gestational diabetes, fetal distress, and postpartum haemorrhage. It discusses the methodologies used, presents results from recent studies, and highlights practical challenges including data quality, model interpretability, and clinical integration. The paper concludes with recommendations for future research and practical implementations to maximize AI's potential in obstetrics.