Diverse Perspectives And State Of The Art Approaches To The Utilization Of Data Driven Clinical Decision Support Systems
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Diverse Perspectives and State-of-the-Art Approaches to the Utilization of Data-Driven Clinical Decision Support Systems
The medical domain is home to many critical challenges that stand to be overcome with the use of data-driven clinical decision support systems (CDSS), and there is a growing set of examples of automated diagnosis, prognosis, drug design, and testing. However, the current state of AI in medicine has been summarized as “high on promise and relatively low on data and proof.” If such problems can be addressed, a data-driven approach will be very important to the future of CDSSs as it simplifies the knowledge acquisition and maintenance process, a process that is time-consuming and requires considerable human effort. Diverse Perspectives and State-of-the-Art Approaches to the Utilization of Data-Driven Clinical Decision Support Systems critically reflects on the challenges that data-driven CDSSs must address to become mainstream healthcare systems rather than a small set of exemplars of what might be possible. It further identifies evidence-based, successful data-driven CDSSs. Covering topics such as automated planning, diagnostic systems, and explainable artificial intelligence, this premier reference source is an excellent resource for medical professionals, healthcare administrators, IT managers, pharmacists, students and faculty of higher education, librarians, researchers, and academicians.
Decision Sciences
This book constitutes the proceedings of the Second Decision Science Alliance International Summer Conference, DSA ISC 2024, held in Valencia, Spain, in June 2024. The 33 full papers and 38 short papers included in this book were carefully reviewed and selected from 101 submissions. At the core of DSA ISC’24 are in-depth discussions and analyses across a spectrum of technological domains. Notably, experts shared their knowledge on areas such as Artificial Intelligence & Machine Learning, Mathematical Optimization, Operational Research & Management Science, Statistics, Simulation, and Decision Processes Analysis. Each of these areas represents a key aspect of decision science, contributing to the interdisciplinary nature of the conference.
Wearable and Implantable Electrocardiography for Early Detection of Cardiovascular Diseases
The field of cardiovascular research and monitoring faces a critical challenge in the early detection of cardiovascular diseases due to limitations in existing monitoring methods. These methods lack accuracy, power efficiency, and compactness, creating a gap in effective intervention and patient outcomes. This pressing problem necessitates advanced solutions that can enhance the capabilities of wearable and implantable electrocardiography (ECG) sensors for accurate and timely detection of conditions like cardiac arrhythmia and heart failure. Wearable and Implantable Electrocardiography for Early Detection of Cardiovascular Diseases presents a comprehensive solution to address these challenges. Written by esteemed scholars with experience in academia and research, this groundbreaking book introduces innovative approaches to enhance ECG sensors. It introduces a novel low-noise and low-power capacitive feedback amplifier based on a current-reused operational transconductance amplifier (OTA), incorporating digitalization techniques and threshold converters to enable the accurate extraction of vital data points. The book emphasizes reduced power consumption and circuit size to ensure energy-efficient and compact monitoring solutions. Targeting academic scholars, researchers, and professionals in the field, this essential resource covers a wide range of topics and equips readers with valuable insights and innovative solutions to overcome existing limitations. By utilizing the knowledge and tools shared in this book, scholars and professionals can drive advancements in the early detection and management of cardiovascular diseases, improving patient care and outcomes.