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Decision Support Systems for Healthcare based on Probabilistic Graphical Models: A Survey and Perspective
Published in Kim Phuc Tran, Machine Learning and Probabilistic Graphical Models for Decision Support Systems, 2023
Ali Raza, Kim Phuc Tran, Ludovic Koehl, Shujun Li
In regards to the importance of DSS in healthcare, this chapter reviews some of the research work on healthcare DSS based on Probabilistic Graphical Models (PGMs)8 and machine learning. The rest of the chapter is organized as follows: Section 2 discusses decision support systems in Healthcare. Section 3 presents a review about the application of artificial intelligence in healthcare. Section 4 discusses healthcare DSSs based on PGMs. Section 5 provides perspectives for Healthcare DSSs based on PGMs. Section 6 provides case studies of DSS in healthcare. Section 7 concludes the chapter.
Public Perception toward AI-Driven Healthcare and the Way Forward in the Post-Pandemic Era
Published in Chinmay Chakraborty, Digital Health Transformation with Blockchain and Artificial Intelligence, 2022
Spandan Datta, Nilesh Tejrao Kate, Abhishek Srivastava
The article ‘The rise of artificial intelligence in healthcare applications’ was reviewed and analyzed. The article was published in Artificial Intelligence in Healthcare in 2020. The constructs identified are AI, healthcare applications, machine learning, precision medicine, environmental assisted living, natural language programming and machine vision. It is commonly anticipated, according to the authors, that AI technologies would help, and augment human labour rather than fully substitute the job of doctors and other medical staff. AI can help healthcare personnel with a variety of activities, such as admin work, patient records and patient engagement, as well as specialized assistance in areas like image processing, medical device control, and monitoring patients [35].
The Importance of Intelligent HIT Vendors
Published in Tom Lawry, AI in Health, 2020
“The emergence of artificial intelligence in healthcare provides new opportunities to address key challenges faced by our clients,” says Hain. “In the inpatient space it’s enabling clinicians to better manage traditional risks and improve quality from the moment a patient walks in the door until they are discharged. Examples of this include applying machine learning to predict falls, deterioration, and readmissions. With the shift to value-based care and a more holistic approach to understanding and managing the needs of patients, we’re focusing AI on how to better manage chronic disease and proactively identify high-risk patients.”
Big Data in Healthcare Research: A survey study
Published in Journal of Computer Information Systems, 2022
Shah J Miah, Edwin Camilleri, H. Quan Vu
Prior to 2016, the time series for publications addressing topics allocated to cluster three is both similar in magnitude and volume to the profile provided by cluster one. Nevertheless, the time series for the eleven topics allocated to cluster three were shown to materially increase thereafter, particularly in 2019. Topics allocated to cluster three are both big data-related (e.g cloud computing, real-time processing, predictive analytics, deep learning and artificial intelligence) and healthcare-related (e.g. healthcare outcomes, patient treatment, population health, public policy, medical images and chronic diseases).
Artificial intelligence: a new clinical support tool for stress echocardiography
Published in Expert Review of Medical Devices, 2018
Maryam Alsharqi, Ross Upton, Angela Mumith, Paul Leeson
The first applications of artificial intelligence in healthcare were reported over three decades ago [5]. However it is only in the last few years, as artificial intelligence has become embedded within multiple areas of life, that there has been an exponential growth of interest in whether it can assist in automated diagnosis and personalized patient management. Artificial intelligence includes computational techniques that ‘learn’ from existing data to make future decisions. Deep learning is a method composed of many layers of highly interconnected processing elements, which are able to represent high levels of abstraction. The use of deep learning with imaging data is usually based on convolutional neural networks that mimic, to some extent, how the human ventral stream is structured [6]. These techniques facilitate rapid analysis of massive amounts of data [7]. Less fluid computational approaches are also possible such as support vector machines and random forests [8–10]. However, the objective of all these methods is to learn patterns from existing sets of clinical data, such as clinical notes, blood test results or images, to allow future sets of data to be automatically processed [5]. In medicine, applications of artificial intelligence have been innovative, particularly in medical imaging [11]. Medical images contain large sets of data that require intensive training and experience in order to detect abnormalities [6]. For clinical adoption, it is important the true impact of artificial intelligence systems, compared to operator-led analysis, on patient outcomes, including how changes in workflow and test accuracy impact on health economic costs, needs to be validated in clinical trials. However, machine-assisted interpretation of medical images offers the potential for more consistent decision-making that could improve patient outcome [11].
Current state of artificial intelligence applications in ophthalmology and their potential to influence clinical practice
Published in Cogent Engineering, 2021
Dasharathraj K Shetty, Abhiroop Talasila, Swapna Shanbhag, Vathsala Patil, B.M Zeeshan Hameed, Nithesh Naik, Adithya Raju
A total of 773 articles were identified in the initial search performed. After screening the titles 454 article and abstract 296 articles were excluded, as per inclusion and exclusion criteria. Based on full text accessibility from the mentioned databases in method of literature 23 articles, which met all inclusion criteria were included for the review. Other 32 articles were included after searching bibliographies that highlighted the methods and algorithms of application, challenges and future perspectives of artificial intelligence in healthcare.