Practical Data Analytics for Innovation in Medicine: Building Real Predictive and Prescriptive Models in Personalized Healthcare and Medical Research Using AI, ML, and Related Technologies

Practical Data Analytics for Innovation in Medicine: Building Real Predictive and Prescriptive Models in Personalized Healthcare and Medical Research Using AI,ML,and Related Technologies,Second Edition discusses the needs of healthcare and medicine in the 21st century,explaining how data analytics play an important and revolutionary role. It provides step-by-step tutorials and case studies online,and uses exercises based on real-world examples of successful predictive and prescriptive tools and systems.

Format: Hardback
Length: 576 pages
Publication date: 06 April 2023
Publisher: Elsevier Science & Technology

Practical Data Analytics for Innovation in Medicine: Building Real Predictive and Prescriptive Models in Personalized Healthcare and Medical Research Using AI, ML, and Related Technologies, Second Edition delves into the pressing needs of healthcare and medicine in the twenty-first century, highlighting the transformative role of data analytics. As healthcare effectiveness and economics face mounting challenges, there is a growing movement to enhance medical treatment and administration by harnessing the predictive power of big data, including predictive analytics. This approach can bolster patient care, reduce costs, and drive greater efficiencies across various operational functions. The book offers a historical perspective, underscoring the significance of predictive analytics in addressing health crises such as the COVID-19 pandemic. It provides practical step-by-step tutorials and case studies online, accompanied by exercises based on real-world examples of successful predictive and prescriptive tools and systems. The final section of the book focuses on specific technical operations related to quality, cost-effective medical and nursing care delivery and administration facilitated by practical predictive analytics.

Introduction:
In the era of rapid technological advancements, healthcare and medicine are undergoing a profound transformation. The demand for personalized healthcare and medical research has never been greater, as patients seek tailored solutions that meet their unique needs. At the same time, healthcare organizations and medical research institutions are faced with the challenge of managing vast amounts of data, extracting valuable insights, and making informed decisions. This is where practical data analytics comes into play.

The Need for Data Analytics in Healthcare:
The healthcare industry is characterized by its complexity, with a vast amount of data generated daily. This data includes patient records, medical imaging, clinical trials, and administrative information. However, the sheer volume and diversity of this data make it challenging to analyze and interpret effectively. Traditional data analysis methods, such as manual sorting and filtering, are time-consuming and prone to errors. Moreover, they often fail to capture the full range of relationships and patterns within the data, limiting their ability to provide actionable insights.

The Role of Predictive Analytics:
Predictive analytics is a powerful tool that leverages machine learning algorithms and statistical models to analyze large datasets and make predictions. By leveraging the power of big data, healthcare organizations can gain a deeper understanding of patient behavior, disease patterns, and healthcare outcomes. This information can be used to develop targeted interventions, improve patient care, and optimize healthcare operations.

Examples of Predictive Analytics in Healthcare:
Predictive analytics has been applied in various areas of healthcare, including disease diagnosis, patient risk assessment, medication management, and healthcare resource allocation. For example, healthcare organizations can use predictive analytics to identify patients who are at high risk of developing certain diseases, such as heart disease or diabetes. By analyzing patient data, healthcare providers can develop personalized treatment plans and interventions to prevent or manage these diseases.

The Role of Prescriptive Analytics:
Prescriptive analytics goes beyond prediction and focuses on making recommendations and taking actions based on the analyzed data. By leveraging machine learning algorithms and optimization techniques, prescriptive analytics can help healthcare organizations optimize healthcare resource allocation, reduce costs, and improve patient outcomes. For example, prescriptive analytics can help healthcare organizations optimize the scheduling of appointments, reduce wait times, and improve patient flow through the healthcare system.

Challenges and Opportunities:
While predictive and prescriptive analytics offer significant benefits, there are also challenges to be addressed. One of the main challenges is the lack of standardized data formats and the absence of a common data infrastructure. This makes it difficult to share and integrate data across different healthcare organizations and systems. Additionally, there are concerns about the privacy and security of patient data, particularly in the context of big data analytics.

Conclusion:
In conclusion, practical data analytics plays a crucial role in innovation in healthcare and medicine. By leveraging the power of big data, healthcare organizations can gain a deeper understanding of patient behavior, disease patterns, and healthcare outcomes. Predictive and prescriptive analytics can be used to develop targeted interventions, improve patient care, and optimize healthcare operations. However, there are challenges to be addressed, such as the lack of standardized data formats and the need for robust data security measures. With continued investment and innovation, practical data analytics will continue to play a vital role in shaping the future of healthcare and medicine.

Weight: 1798g
Dimension: 224 x 286 x 38 (mm)
ISBN-13: 9780323952743
Edition number: 2 ed