Neural Network in Healthcare sector
Artificial neural networks, usually simply called neural networks, are computing systems vaguely inspired by the biological neural networks that constitute animal brains.
Neural networks learn by processing examples, each of which contains a known “input” and “result,” forming probability-weighted associations between the two, which are stored within the data structure of the net itself. The training of a neural network from a given example is usually conducted by determining the difference between the processed output of the network and a target output. This is the error. The network then adjusts its weighted associations according to a learning rule and using this error value.
Machine Learning in Health Care!!
Machine learning in healthcare is becoming more widely used and is helping patients and clinicians in many different ways. The most common healthcare use cases for machine learning are automating medical billing, clinical decision support and the development of clinical care guidelines. There are many notable examples of machine learning and healthcare concepts being applied in medicine. In radiology, deep learning in healthcare identifies complex patterns automatically, and helps radiologists make intelligent decisions reviewing images such as conventional radiographs, CT, MRI, PET images and radiology reports. The performance of machine learning-based automatic detection and diagnosis systems has shown to be equivalent to that of an experienced radiologist. Google’s machine learning applications in healthcare were trained to detect breast cancer and achieved 89 percent accuracy, on par or better than radiologists. These are just a few of examples of the many uses of machine learning in healthcare.
Deep learning is assisting medical professionals and researchers to discover the hidden opportunities in data and to serve the healthcare industry better. Deep learning in healthcare provides doctors the analysis of any disease accurately and helps them treat them better, thus resulting in better medical decisions.
1)Drug discovery
Deep learning in healthcare helps in discovery of medicines and their development. The technology analyzes the patient’s medical history and provides the best treatment for them. Moreover, this technology is gaining insights from patient symptoms and tests.
2)Medical imaging
Medical imaging techniques such as MRI scans, CT scans, ECG, are used to diagnose dreadful diseases such as heart disease, cancer, brain tumor. Hence, deep learning helps doctors to analyze the disease better and provide patients with the best treatment.
3)Insurance fraud
Deep learning is used to analyze the medical insurance fraud claims. With predictive analytics, it can predict fraud claims that are likely to happen in the future. Moreover, deep learning helps insurance industry to send out discounts and offers to their target patients.
Not only, in Healthcare sector, its is used in every field such as education, cyber security etc. It helps many firms to grow in terms of usage and reputation.
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