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Machine Learning Usecases 03- Healthcare

Machine learning algorithms can detect patterns associated with diseases and health conditions by studying thousands of healthcare records and other patient data.
Machine Learning Usecases 03- Healthcare

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. At MD Anderson, researchers have developed the first medical machine learning algorithm to predict acute toxicities in patients receiving radiation therapy for head and neck cancers. In radiology, deep learning in healthcare identifies complex patterns automatically and helps radiologists make intelligent decisions reviewing images such as conventional radiography, 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 examples of the many uses of machine learning in healthcare.

Unstructured healthcare data for machine learning represents almost 80% of the information held or “locked” in electronic health record systems. These are not data elements but documents or text files which in the past could not be analyzed without a human reading through the material. Human language, or “natural language,” is very complex, lacking uniformity, and incorporates an enormous amount of ambiguity, jargon, and vagueness. In order to convert these documents into more useful and analyzable data, machine learning in healthcare often relies on natural language processing (NLP) programs. Most deep learning in healthcare applications that use NLP requires some form of medical machine learning.

Healthcare use cases for machine learning are many. For example, the same NLP technology that is used to determine creditworthiness for a consumer or sentiment analysis of someone’s social media post can now be used to read a patient’s chart to extract important data elements like the patient’s medications, treatment plans, and medical conditions.

Probyto AI allow organizations to build and manage such usecases and track the benefits of AI adoption in their businesses. Currently, FREE 60 minutes AI Consultation is being provided to the registered users in the Probyto AI Demo platform. If you haven't registered yet, register now to Probyto AI Demo.

Source: https://www.foreseemed.com/blog/machine-learning-in-healthcare

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