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Solutions Using Machine Learning for Diabetes
Published in Punit Gupta, Dinesh Kumar Saini, Rohit Verma, Healthcare Solutions Using Machine Learning and Informatics, 2023
Jabar H. Yousif, Kashif Zia, Durgesh Srivastava
A learning algorithm is a computation method that applies specific functions to recognize and translate input data sets into the desired output. Machine learning is a branch of artificial intelligence that emphasizes the use of data and algorithms to emulate humans, learning and thinking to improve its accuracy. It involves providing machines with the ability to learn, process data, and make decisions and future forecasts. The main learning methods, depending upon the input data and feedback, are supervised, unsupervised, semi-supervised, and reinforcement techniques (Figure 3.3).
Big data and public health
Published in Sridhar Venkatapuram, Alex Broadbent, The Routledge Handbook of Philosophy of Public Health, 2023
The analysis of massive data sets, now a common practice in public health, would be infeasible without the aid of computational tools. Computerized algorithms that detect patterns present within large data sets are designed to facilitate this process. Some algorithms can make predictions when provided with new data based on their ability to detect patterns, and they are incorporated into automated systems used for public health research and surveillance. These algorithms are therefore crucial for the big data approach, as noted by Ziad Obermeyer and Ezekiel Emanuel: By now, it’s almost old news: big data will transform medicine. It’s essential to remember, however, that data by themselves are useless. To be useful, data must be analyzed, interpreted, and acted on. Thus, it is algorithms—not data sets—that will prove transformative. We believe, therefore, that attention has to shift to new statistical tools from the field of machine learning that will be critical for anyone practicing medicine in the 21st century. […] Machine learning will dramatically improve the ability of health professionals to establish a prognosis [… and] will improve diagnostic accuracy.(2016: 1216–1218)
Medication Errors
Published in Salvatore Volpe, Health Informatics, 2022
Jitendra Barmecha, Z. Last, A. Zaman
Machine Learning is an application of AI providing systems the ability to automatically learn and improve from experience without being explicitly programmed, focusing on the development of computer programs that can access data and use it to learn for themselves.22
Machine learning model for classification of predominantly allergic and non-allergic asthma among preschool children with asthma hospitalization
Published in Journal of Asthma, 2023
Piyush Bhardwaj, Ashish Tyagi, Shashank Tyagi, Joana Antão, Qichen Deng
Recent advances in machine learning and its application in the medical sector have made it possible to use significant amounts of medical data for disease diagnosis, management, and to provide personalized treatments. Machine learning is a field of artificial intelligence and is widely used for disease classification and other objectives (8). Several studies have been conducted to understand the complexity of asthma and other chronic airway diseases. Alizadeh and colleagues used an artificial neural network (ANN) in their study to predict the asthma status in 254 subjects using 13 characteristics; a 100% accuracy was achieved (9). Thereafter, latent class analysis (LCA) was implemented by Couto et al. and two types of asthma phenotype were discovered among asthmatic athletes, healthy athletes, and athletes with pathological conditions (10).
A step towards the application of an artificial intelligence model in the prediction of intradialytic complications
Published in Alexandria Journal of Medicine, 2022
Ahmed Mustafa Elbasha, Yasmine Salah Naga, Mai Othman, Nancy Diaa Moussa, Hala Sadik Elwakil
Artificial intelligence (AI) is a field of science and engineering concerned with the computational information of what’s generally referred to as intelligent behavior, and with the development of objects that exhibit such behavior [12]. Machine learning (ML) is one of the prime branches of AI. Machine learning can be described as a collection of algorithms that have the capability of learning and improving from experience, without being specifically programmed for a particular task. Random forest, support vector machine and artificial neural network (ANN) are examples of machine learning algorithms [13]. Many artificial intelligence-based algorithms have been accepted by the Food and Drug Administration (FDA) to be used in clinical practice, they do not replace the role of physician but they may complement it in a wide range of applications in medicine. This ranges from predicting patient outcomes such as diagnosis and treatment efficacy, to discovering patterns in large databases and to understanding disease pathogenesis.
Machine Learning Applications in Pediatric Ophthalmology
Published in Seminars in Ophthalmology, 2021
Machine learning is a branch of artificial intelligence that provides an array of tools and methods to identify complex patterns in medical data.1,2 The ability to develop models and generate predictions from large clinical datasets is quickly transforming the way we provide and deliver eye care.1,3 Machine learning not only offers opportunities for ocular disease screening and diagnosis, but also insight into treatment response, which can guide clinical decision making.4,5 Most ophthalmic applications to date have focused on disease processes affecting adults, with an overwhelming interest in pathology affecting the optic nerve and retina.6–13 In pediatric ophthalmology, there has also been emphasis on retinal diseases with recent advances in fundus photography-based classification of retinopathy of prematurity.14 Relatively fewer studies have explored applications of machine learning in other domains of pediatric ophthalmology and strabismus.15 Machine learning has the potential to revolutionize the way we approach vision screening, with new ways to predict which children are at greatest risk of developing amblyogenic risk factors such as high refractive error and strabismus.16 Furthermore, the ability to predict treatment response will allow for increasingly individualized interventions. The goal of this review is to explore recent advances in machine learning geared towards pediatric eye care providers with an emphasis on the screening, diagnosis, and treatment of conditions affecting visual development.