Machine learning is generating a lot of excitement amongst healthcare providers, but what are some of the top use cases for these advanced analytics tools?
Here are some of the top initiatives and most intriguing research projects that are currently harnessing these tools.
Imaging analytics and pathology
Improving imaging analytics and pathology with machine learning is of particular interest to healthcare organizations, who would otherwise be leaving a great deal of big data on the table.
Machine learning can supplement the skills of human radiologists by identifying subtler changes in imaging scans more quickly, potentially leading to earlier and more accurate diagnoses.
Natural language processing and free-text data
From the EHR to the MRI, unstructured data is everywhere in the healthcare industry – either intentionally or otherwise.
Clinical decision support and predictive analytics
The ability to extract meaning from large volumes of free text is also critical for clinical decision support and predictive analytics – another area where machine learning is starting to shine.
Identifying and addressing risks quickly can significantly improve outcomes for patients with any number of serious conditions, both clinical and behavioral.
As machine learning becomes more advanced and reliable, healthcare organizations will find no shortage of potential applications and use cases for this new generation of big data analytics technology.
This article originally appeared on HealthITanalytics.com. To read the full article, click here.