“Smart” search programs can ease the process of systematically reviewing new medical research, a key step in getting the practices from laboratories and doctor’s offices, U.S researcher says.
The Institute of Medicine says clinical practice guidelines should be based on a systematical review of the evidence, lead author Dr. Paul Shekelle from RAND Corporation in Santa Monica, California told by email.
“We know that the clinical practice guidelines go out of date over time, as new evidence accumulates. An impediment to the regular updating of clinical practice guidelines is the time and resources needed to update the systematic review,” he said.
Typically, researchers and their assistants perform computer searches to identify anywhere from a few to thousands of new research studies, then they determine which ones are relevant and assemble the information into updated guidelines and recommendations.
Shekelle and colleagues thought machines could do more of the job and do it faster, so they compared machine-learning methods with the standard search methods for identifying new information.
They tested the idea on three health conditions: gout, low bone, and osteoarthritis of the knee. The smart search program “learned” which key terms to look for by analyzing words from studies that were included in prior reviews on each topic.
In all three cases, computers- provided only with the titles and summaries of articles included in previous reviews- reduced the number of articles researchers had to screen further by 67 to 83 percent, according to the result in Annals of Internal Medicine.
The machine-learning method missed only two articles that humans would have identified, for an overall accuracy of 96% percent. And neither of all these articles would have changed the ultimate evidence reviews, Shekelle’s team concludes.
Machine learning methods are very promising as a way to reduce the amount of time and effort for the literature research, which in turn should make it easier to update the systematic review, which in turn can facilitate keeping clinical practice guidelines up to date”, Shekelle said.
“In the near future, artificial intelligence will also be used to match to individual need with the best available health care intervention- one necessary step to this is proper classification on existing and newly generated knowledge”.loreo said.