Artificial Intelligence and Deep Learning Lab
The Machine Learning (ML) group in the Steele Institute for Health Innovation is an interdisciplinary team that unites clinicians with engineers and computer scientists, going beyond Big Data to solve some of healthcare’s most pressing issues and improve outcomes for our patients.
Who we are
Here at Geisinger, we’ve introduced artificial intelligence, commonly called machine learning, using technology to process data without pre-determined rules into our regular clinical workflow.
Our Artificial Intelligence and Deep Learning Lab is focused on the smart use of machine learning technology using Big Data to aid medical providers in delivering better and faster care, especially in these areas where time is critical.
What we do
Our ML team leverages Geisinger’s robust, 20+ year history of electronic health record (EHR), imaging and genomic databases to develop high-impact, clinically actionable predictive technology. This use of intelligent computer assistance is necessary to sustain and improve medical care, and Geisinger is proud to be at the forefront of the development and clinical application of these emerging technologies.
Our Artificial Intelligence (AI) work fits into seven categories:
- Disease management
- Financial health
- Imaging sciences
- New markets
- Patient experience
- Patient safety
- Population health
Interested in working with us? Email us at email@example.com, and let’s talk.
What's the difference between AI and predictive analytics?
Predictive analytics (PA) and machine learning (ML) have similar goals. Machine learning is a branch of predictive analytics, with only the methods rather than the aims differing. Both PA and ML attempt to increase value by unlocking patterns hidden in vast amounts of a company’s data.
Both PA and ML have been described in the past as “Artificial Intelligence,” commonly called AI. Each methodology uses a blend of statistics and rule deduction to perform tasks that the user believes requires intelligence.
Predictive analytics can generally accomplish its results with less data. The reason for this is because human experts develop rule sets based on their expertise in the subject and validate their results based on the human programmers’ confidence that the rules from the present will persist for a reasonable time into the future. If this assumption fails, the humans re-assess the rules and essentially hard-wire the computer a new ‘brain’ with new rules.
Machine learning is a technique where algorithms are given data and asked to process it without predetermined rules. ML accomplishes its goals by simply being trained on additional data, without any review of its internal logic. The computer learns to ‘see better’ with more experience. The ‘brain’ remains the same.
ML algorithms use what they learn from their mistakes to improve future performance without the need to be reprogrammed on a periodic basis. Data feeds ML; the results are most accurate when the machine has access to massive amounts of it to refine its algorithm.
Early detection of sepsis
Health plan high-dollar value claims
Patient notes index
The team offers the ability to do keyword searches on unstructured patient notes on all Epic HNO and RAD notes. For instance, the keyword “coumadin” would list all patient notes which included “coumadin.” The result set can be further pared down using factors such as date/time filters, note type and facility location.
This feature has been used extensively by our physicians, researchers and even analysts from different service lines for a myriad of use cases.
In one example, a department moved to a new building. After the move, however, staff members discovered they had lost the records of patients who should have been rebooked for a follow-up appointment. A fellow physician used the tool to search for specific keywords and date ranges, generating a list of medical record numbers (MRNs) of patients at a particular facility who were not booked for a follow-up appointment.