Source: Health IT Analytics
Author: Jennifer Bresnick
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?
As healthcare providers and vendors start to show off more mature big data analytics skills, machine learning and artificial intelligence have quickly rocketed to the top of the industry’s buzzword list.
The possibility of using intelligent algorithms to mine enormous stores of structured and unstructured data for innovative insights has long tantalized the provider community, but a fragmented health IT landscape and sluggish analytics development have thus far kept that reality at bay.
However, changing financial pressures are starting to incentivize predictive, preventive population health management, which has led in turn to an industry-wide effort to break down data silos and open up the doors to large-scale analytics.
And as a new crop of data science breakthroughs ripen in the field of machine learning, healthcare now has the opportunity to seize upon a slew of revolutionary tools that use natural language processing, pattern recognition, and deep learning to support better care.
The industry still has a great deal of work to do before the real-world applications of machine learning and artificial intelligence match the frenzied hype, but some organizations have started putting their supercomputing prowess to work on a number of exciting use cases.
From clinical decision support and imaging analytics to security and precision medicine, machine learning is already putting its stamp on the healthcare big data analytics environment. 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.
A number of technology industry stalwarts have already started to invest heavily in imaging analytics and pathology projects.
IBM Watson is rolling out a clinical imaging review service to help identify aortic stenosis, while Microsoft is targeting imaging biomarker phenotyping to supplement its cancer research efforts.
Academic institutions are also getting in on the ground floor of advanced pattern recognition. At Indiana University-Purdue University Indianapolis, researchers are turning machine learning algorithms loose on pathology slides to predict relapse rates for acute myelogenous leukemia. In a small study published earlier this year, one algorithm was able to identify patients who would relapse with 100 percent accuracy.
And at Stanford University, machine learning tools performed better than human pathologists when distinguishing between two types of lung cancer. The computer also bested its human counterparts at predicting patient survival times.
Meanwhile, Google researchers have already exceeded the accuracy of human pathologists examining images of metastasized breast cancer tissue, reducing false negatives to one-quarter of the human clinical rate.
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.
PDF images of faxed lab reports, voice recordings of consumer interactions, and free-text EHR inputs all pose significant challenges for traditional analytics tools, but machine learning offers a novel way to extract usable meaning from these data sources.