By leveraging scalable computational power, machine learning offers the potential to predict outcomes of a person’s disease from real-world data extracted from electronic medical records.
What is a Digital Twin?
The Department of Ophthalmology at the University of Pittsburgh has invested time and resources into building an artificial intelligence model that would predict the way that a disease, such as macular degeneration, would progress in a patient and allow clinicians to test therapeutic interventions on a virtual patient or a ‘Digital Twin.’
Making an informed choice in your own treatment!
The Digital Twin platform would use big data to show patients what the outcomes would be from the many different treatment options, e.g. drug therapies, surgery, or no intervention, and the associated costs for each option. This, Dr. José-Alain Sahel, Distinguished Professor and Chairman of the Department of Ophthalmology, believes, is the best way for patients to make an informed choice about their care that fits best with their lives and abilities.
Using Artificial Intelligence in Ophthalmology
The team working on artificial intelligence in the Ophthalmology Department was awarded a generous grant from the Shear Family Foundation to continue improving the model, inputting data that has been collected from patients with macular degeneration and comparing the predictions given by the model to the actual disease pathology of the patient.
Dr. Kunal Dansingani, Associate Professor of Ophthalmology, intends to move this model into clinical testing with data from current patients who are being treated for the disease, with the hope that the model can function in real-time as an early detection and decision support tool for physicians, by modeling the potential clinical course of a range of diseases under various treatment conditions and by learning continuously from exposure to new case data. The team is working toward the development of a framework – the ‘Digital Twin’ platform – for an automated personalized medicine system, allowing for more effective treatment based on individual health data paired with predictive analytics.
The Big Data Health Care Revolution
The availability of electronic health records offers an unprecedented opportunity to apply predictive analytics to improve the practice of medicine. By leveraging scalable computational power, machine learning offers the potential to predict outcomes of a person’s disease from real-world data extracted from electronic medical records.
In the long term, as the algorithm is perfected and the model is tested in many different diseases beyond blindness, such as cancer, inflammatory diseases, and others, we hope that the technology will put Pittsburgh on the map for being at the forefront of the Big Data health care revolution.
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