Magoulas, G.D., Prentza, A.: Machine learning in medical applications. In: Paliouras, G., Karkaletsis, V., Spyropoulos, C.D. (eds.) ACAI 1999. LNCS (LNAI), vol. 2049, pp. 300–307. Springer, Heidelberg (2001)G. D. Magoulas and A. Prentza, "Machine learning in medical ...
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We are Machine Learning in Medical Imaging and Diagnostics, an interdisciplinary Research Lab based in Dublin. We investigate novel machine learning concepts in the medical field.Our research is supported by funding from Enterprise Ireland (EI), Science Foundation Ireland (SFI), Health Research Board...
Machine learning, the cornerstone of today’s artificial intelligence (AI) revolution, brings new promises to clinical practice with medical images1,2,3. For example, to diagnose various conditions from medical images, machine learning has been shown to perform on par with medical experts4. Softwar...
The course will not make you a bioinformatician but will introduce the main concepts, tools, algorithms, and databases in this [...] Biomedical Genomics Biomedical Informatics Bioinformatics Next Generation Sequencing Biological Data Medical Machine Learning Machine Learning Artificial Intelligence Health ...
Clinical medicine offers a promising arena for applying Machine Learning (ML) models. However, despite numerous studies employing ML in medical data analysis, only a fraction have impacted clinical care. This article underscores the importance of utilisi
Deep-learning models trained on external eye photographs can detect diabetic retinopathy, diabetic macular oedema and poor blood glucose control more accurately than models relying on demographic and medical history data. Boris Babenko Akinori Mitani ...
Google Health is among the machine learning in healthcare examples. It’s a division of Google that focuses on designing and applying machine learning to healthcare. One of their most significant projects is the development of machine-learning algorithms for medical imaging. Google Health has releas...
Machine learning is a fast-growing trend in the health care industry, thanks to the advent of wearable devices and sensors that can use data to assess a patient's health in real time. The technology can also help medical experts analyze data to identify trends or red flags that may lead ...
The C_IME_RPT is developed based on Self Organizing Maps (SOM) and a machine learning process. Both systems have been evaluated using Independent Medical Examination (IME) reports provided by medical professionals. The proposed system MD_NER_NCL made a significant improvement over the well-known ...