How AI is Currently Being Used in Nephrology
The presence of chronic kidney disease (CKD) and other forms of kidney disease that fall under the branch of nephrology make up a significant portion of the total healthcare costs in the United States. In fact, it is estimated that just treating CKD and end-stage renal disease (ESRD) amounts to over $120 billion every year. But by shining a better spotlight on early detection of kidney issues, we can help to significantly lower this economic and medical burden.
Through the incorporation of artificial intelligence (AI) in nephrology, we have been able to make significant strides in helping to reduce the strain that kidney diseases put on the medical community. Here is how AI is starting to be used in nephrology.
Better Immunoglobulin A Nephropathy Prediction Models and Online Calculators
A lack of consistently accurate prediction models is one of the biggest challenges currently facing nephrology specialists. Luckily, there is a team of researchers who recently made a machine learning prediction model with far more accurate results. This AI-powered prediction model was aimed at calculating the risk for immunoglobulin A nephropathy (IgAN) in patients.
By using the eXtreme Gradient Boosting (XGBoost) approach, it was able to deliver a C statistic of 0.84 during its prediction of end-stage kidney disease. It was also given the ability to handle missing patient variables and still accurately calculate a simple scoring scale that is easily understood by any medical professional.
This machine learning prediction model was also incorporated into an online calculator that could be used to calculate the 5-year prognosis of each individual. By having such a simple way to incorporate into practical healthcare settings, it allows nephrology specialists to know which patients need more preventative care as compared to others.
Better Acute Kidney Injury Prediction Models
There are many other areas of nephrology that are being assisted through the use of machine learning prediction models. One of the most recent was the work of researchers who were trying to improve the accuracy of predicting the likelihood that patients would experience acute kidney injuries.
Their study used a machine learning model that was fed the information of over 703,000 patients. It then worked through the database to have an accurate prediction rate of 90.2 percent in all acute kidney injuries that were in need of additional dialysis treatment.
This can allow medical professionals to become aware of when someone is at major risk of suffering an acute kidney injury as much as 48 hours sooner than they otherwise would be able to. This allows for earlier treatments that can help save countless lives and thousands or even millions of dollars in healthcare costs associated with patients deteriorating from acute kidney injuries.
Although there is still some work that is needed, these AI-aided diagnostic tools can result in billions of dollars in savings for the healthcare industry when it comes to the treatment of kidney diseases. In order to get other current news regarding AI and machine learning use in healthcare, make sure to follow our blog at RediMinds today.