This paper discusses the machine learning effect on healthcare and the development of an application named "Medicolite" in which various modules have been developed for convenience with health-related problems like issues with diet. It also provides online doctor appointments from home and medication through the phone. A healthcare system is "Smart" when it can decide on its own and can prescribe patients life-saving drugs. Machine learning helps in capturing data that are large and contain sensitive information about the patients, so data security is one of the important aspects of this system. It is a health system that uses trending technologies and mobile internet to connect people and healthcare institutions to make them aware of their health condition by intelligently responding to their questions. It perceives information through machine learning and processes this information using cloud computing. With the new technologies, the system decreases the manual intervention in healthcare. Every single piece of information has been saved in the system and the user can access it any time. Furthermore, users can take appointments at any time without standing in a queue. In this paper, the authors proposed a CNN-based classifier. This CNN-based classifier is faster than SVM-based classifier. When these two classifiers are compared based on training and testing sessions, it has been found that the CNN has taken less time (30 seconds) compared to SVM (58 seconds).
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http://dx.doi.org/10.1155/2022/8109147 | DOI Listing |
Lung Cancer
January 2025
Internal Medicine III, Wakayama Medical University, Wakayama, Japan.
Objectives: The lack of definitive biomarkers presents a significant challenge for chemo-immunotherapy in extensive-stage small-cell lung cancer (ES-SCLC). We aimed to identify key genes associated with chemo-immunotherapy efficacy in ES-SCLC through comprehensive gene expression analysis using machine learning (ML).
Methods: A prospective multicenter cohort of patients with ES-SCLC who received first-line chemo-immunotherapy was analyzed.
J Speech Lang Hear Res
January 2025
Department of Psychology, University of Western Ontario, London, Canada.
Purpose: Recent advances in artificial intelligence provide opportunities to capture and represent complex features of human language in a more automated manner, offering potential means of improving the efficiency of language assessment. This review article presents computerized approaches for the analysis of narrative language and identification of language disorders in children.
Method: We first describe the current barriers to clinicians' use of language sample analysis, narrative language sampling approaches, and the data processing stages that precede analysis.
J Craniofac Surg
October 2024
Department of Biomedical and Surgical and Biomedical Sciences, Catania University, Catania, Italy.
Background: With the use of machine learning algorithms, artificial intelligence (AI) has become a viable diagnostic and treatment tool for oral cancer. AI can assess a variety of information, including histopathology slides and intraoral pictures.
Aim: The purpose of this systematic review is to evaluate the efficacy and accuracy of AI technology in the detection and diagnosis of oral cancer between 2020 and 2024.
Neuroradiol J
January 2025
Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Iran.
Introduction: The prevalence of neurodegenerative diseases has significantly increased, necessitating a deeper understanding of their symptoms, diagnostic processes, and prevention strategies. Frontotemporal dementia (FTD) and Alzheimer's disease (AD) are two prominent neurodegenerative conditions that present diagnostic challenges due to overlapping symptoms. To address these challenges, experts utilize a range of imaging techniques, including magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), functional MRI (fMRI), positron emission tomography (PET), and single-photon emission computed tomography (SPECT).
View Article and Find Full Text PDFAm J Phys Med Rehabil
December 2024
Department of Physical Medicine and Rehabilitation, Medical College of Wisconsin, 8701 W Watertown Plank Rd, Milwaukee, WI, 53226.
Predicting discharge destination for patients at inpatient rehabilitation facilities is important as it facilitates transitions of care and can improve healthcare resource utilization. This study aims to build on previous studies investigating discharges from inpatient rehabilitation by employing machine learning models to predict discharge disposition to home versus non-home and explore related factors. Fifteen machine learning models were tested.
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