How Healthcare Can Use ML/AI

Author:- Careers of Tomorrow

Many healthcare providers are excited about Machine Learning. What use will these advanced analytics tools be used for? Professionals in the healthcare industry are turning to machine learning certification to adopt machine learning and artificial intelligence.

The potential of intelligent algorithms to extract enormous amounts of data has long tantalized the community. However, the pressure of starting to motivate health management has led in opening up to large-scale analytics.

The healthcare industry has the opportunity to support better care using revolutionary tools that use deep learning, pattern recognition.  They have a great deal of work to do, but some organizations have started to put machine learning and artificial intelligence to use. From support in clinical decision to security and precision in medicines, the industry has been using machine learning. One of the highest demands for artificial intelligence certification is coming from healthcare professionals.

Here are some research projects and initiatives that are harnessing machine learning and artificial intelligence.


Imaging Analytics/Pathology

With machine learning, imaging analytics and pathology can be improved. It is gaining particular interest from healthcare organizations who would otherwise be dealing with a great deal of big data.

Using machine learning, human radiologists are in a better position in imaging scans more quickly. The technology can supplement human skills making diagnoses more accurate and faster. Some organizations have started to make investments in imaging analytics and pathology projects.

IBM Watson is working on clinical imaging review service to determine a quick identification of aortic stenosis. Microsoft is endeavoring towards cancer research by targeting imaging biomarker phenotyping.

Academic institutions are researching machine learning algorithms. Researchers from Indiana University-Purdue University, Indianapolis, are researching from machine learning algorithms to calculating relapse rates for acute myelogenous leukemia. According to a small study published, one algorithm was 100% successful to identify patients who would relapse.


Natural Language Processing

Unstructured data is plenty in the healthcare industry. From Electronic Health Record to MRI, there is data everywhere. Traditional analytical tools are incapable of handling PDF images of lab reports, free-text EHR inputs or even voice recordings of consumers. Machine learning is a new way to extract meaning from plentiful data from such type of data sources. Machine learning algorithms can convert images of text into documents using natural language processing. This allows for an easier extract of meaning from those documents or process textual search queries to achieve accurate results.

Anne Arundel Medical Center is enabling users to access data using a natural language interface. Natural Language Processing can be utilized on collections of free-text like patient surveys, unstructured clinical notes, and any other narrative data.

In one project researched in the UK, natural language processing tools were found to agree to human assessments 98% of the time. It could help providers to interact with consumers and business partners without spending time on processing data afterward.


Clinical Decision Support

To provide better clinical support, it is critical to be able to extract meaning from huge amounts of free text. This is where machine learning is coming into play. The ability to identify risks quickly will help in addressing risk quickly. It can significantly increase positive outcomes for patients with serious conditions.

Beacon Health Options, a behavioral health company, uses machine learning to clear vague diagnosis processes and prevent increasing complications. They are using machine learning to make adjustments to its risk stratification capabilities. This allows case managers to coordinate with high-risk patients more proactively.

Icahn School of Medicine at Mount Sinai (ISMMS) has researched algorithms to help differentiate between two heart conditions that are quite similar.

Center for Digital Health Innovation (University of California) and GE Healthcare are making a collection of predictive analytics algorithms that can quicken the delivery of critical care.

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