Machine learning has come of age in public health reporting according to researchers from the Regenstrief Institute and Indiana University School of Informatics and Computing at Indiana University-Purdue University Indianapolis. They have found that existing algorithms and open source machine learning tools were as good as, or better than, human reviewers in detecting cancer cases using data from free-text pathology reports. The computerized approach was also faster and less resource intensive in comparison to human counterparts.
Every state in the United States requires cancer cases to be reported to statewide cancer registries for disease tracking, identification of at-risk populations, and recognition of unusual trends or clusters. Typically, however, busy health care providers submit cancer reports to equally busy public health departments months into the course of a patient’s treatment rather than at the time of initial diagnosis.
This information can be difficult for health officials to interpret, which can further delay health department action, when action is needed. The Regenstrief Institute and IU researchers have demonstrated that machine learning can greatly facilitate the process, by automatically and quickly extracting crucial meaning from plaintext, also known as free-text, pathology reports, and using them for decision-making.
“Towards Better Public Health Reporting Using Existing Off the Shelf Approaches: A Comparison of Alternative Cancer Detection Approaches Using Plaintext Medical Data and Non-dictionary Based Feature Selection” is published in the April 2016 issue of the Journal of Biomedical Informatics.
“We think that its no longer necessary for humans to spend time reviewing text reports to determine if cancer is present or not,” said study senior author Shaun Grannis, M.D., M.S., interim director of the Regenstrief Center of Biomedical Informatics. “We have come to the point in time that technology can handle this. A human’s time is better spent helping other humans by providing them with better clinical care.”
“A lot of the work that we will be doing in informatics in the next few years will be focused on how we can benefit from machine learning and artificial intelligence. Everything — physician practices, health care systems, health information exchanges, insurers, as well as public health departments — are awash in oceans of data. How can we hope to make sense of this deluge of data? Humans can’t do it — but computers can.”
Dr. Grannis, a Regenstrief Institute investigator and an associate professor of family medicine at the IU School of Medicine, is the architect of the Regenstrief syndromic surveillance detector for communicable diseases and led the technical implementation of Indiana’s Public Health Emergency Surveillance System – one of the nation’s largest. Studies over the past decade have shown that this system detects outbreaks of communicable diseases seven to nine days earlier and finds four times as many cases as human reporting while providing more complete data.
“What’s also interesting is that our efforts show significant potential for use in underserved nations, where a majority of clinical data is collected in the form of unstructured free text,” said study first author Suranga N. Kasthurirathne, a doctoral student at School of Informatics and Computing at IUPUI. “Also, in addition to cancer detection, our approach can be adopted for a wide range of other conditions as well.”
The researchers sampled 7,000 free-text pathology reports from over 30 hospitals that participate in the Indiana Health Information Exchange and used open source tools, classification algorithms, and varying feature selection approaches to predict if a report was positive or negative for cancer. The results indicated that a fully automated review yielded results similar or better than those of trained human reviewers, saving both time and money.
“Machine learning can now support ideas and concepts that we have been aware of for decades, such as a basic understanding of medical terms,” said Dr. Grannis. “We found that artificial intelligence was as least as accurate as humans in identifying cancer cases from free-text clinical data. For example the computer ‘learned’ that the word ‘sheet’ or ‘sheets’ signified cancer as ‘sheet’ or ‘sheets of cells’ are used in pathology reports to indicate malignancy.
“This is not an advance in ideas, it’s a major infrastructure advance — we have the technology, we have the data, we have the software from which we saw accurate, rapid review of vast amounts of data without human oversight or supervision.”
Learn more:Â Regenstrief, IU study finds machine learning as good as humans’ in cancer surveillance
The Latest on: Machine learning
via Google News
The Latest on: Machine learning
- Why AI and machine learning are drifting away from the cloudon August 1, 2022 at 11:17 am
Cloud computing isn’t going anywhere, but some companies are shifting their machine learning data and models to their own machines they manage in-house. Adopters are spending less money and getting ...
- Detroit’s Waymark Launches Machine Learning Platform to Speed Video Creationon August 1, 2022 at 4:57 am
Waymark in Detroit announced the next generation of its platform to allow to scripting, production, and customization of videos in minutes.
- Viewpoint: How machine learning is revolutionizing e-commerceon July 29, 2022 at 7:16 am
The retail landscape is rapidly changing due to the consumer’s desire for speed and instant gratification. Machine learning is removing friction from the process.
- Seven Factors To Consider Before Purchasing A Machine Learning Tool For Your Businesson July 28, 2022 at 10:15 am
How Will It Empower The Sales Team?’ First, emphasize the point that machine learning does not replace sales reps; it simply gives them tools and data to maximize results.
- Why Machine Learning In Ad Tech Is Ready For Liftoffon July 27, 2022 at 7:45 am
Learn more about the incredible scale and complexity of machine learning in ad tech with Yunshi Zhao, ML engineer at Liftoff Mobile. Zhao also part of the diversity, equity, and inclusion (DEI) ...
- Study draws new link between dopamine-based reward learning and machine learningon July 27, 2022 at 6:40 am
Past neuroscience and psychology research has repeatedly demonstrated the crucial role of rewards in how humans and other animals acquire behaviors that promote their survival. Dopaminergic neurons, ...
- Could machine learning fuel a reproducibility crisis in science?on July 26, 2022 at 6:28 am
Machine learning is being sold as a tool that researchers can learn in a few hours and use by themselves — and many follow that advice, says Sayash Kapoor, a machine-learning re ...
- SpaceWERX explores machine learning for on-orbit servicing, manufacturingon July 26, 2022 at 5:00 am
The Space Force’s innovation arm, SpaceWERX, has tapped Wallaroo Labs to explore and demonstrate how machine learning models can be deployed to advance multiple efforts associated with on-orbit ...
- A huge wave of acquisitions is expected to hit the machine learning market as giants like Snowflake take advantage of discounted valuationson July 24, 2022 at 5:00 am
Investors and insiders who poured hundreds of millions into machine-learning startups now expect a big wave of acquisitions as the market sours.
- Driving smarter customer experiences with AI and machine learningon July 20, 2022 at 5:50 pm
When AI is integrated into an organization's core product or service and business processes, it’s at its most beneficial. Despite AI's increasing popularity, many businesses still find it difficult to ...
via Bing News