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SymphonyAI Announces Enterprise AI Research Partnership with Semmelweis University Heart and Vascular Center to Predict Healthcare Outcomes

EurekaAI helps predict heart attacks through AI analysis of patient data

SymphonyAI, a leader in high-value enterprise AI solutions for key vertical sectors, announced the expansion of a partnership with Semmelweis University Heart and Vascular Center, a leading research institution in East-Central Europe.

Under the partnership, Semmelweis University researchers use SymphonyAI’s EurekaAI platform to detect higher cardiovascular risk and predict patient outcomes through AI-based analysis of Semmelweis’ historical patient data sets.

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The EurekaAI enterprise platform finds patterns and delivers insights through a unique combination of topological data analysis (TDA) and supervised, semi-supervised, and unsupervised. It provides extensive data preparation, automated feature engineering, enrichment, segmentation, model selection and training, and exceptional auditing capabilities for transparency.

Semmelweis researchers use EurekaAI to analyze historical patient data to predict major adverse cardiac events in current patients. To do this, the researchers use EurekaAI unsupervised ML techniques to integrate conventional patient characteristic data and cardiovascular imaging features of heart structure and function into a patient similarity network. This work allows physicians to compare individual patients to similar patients to predict the likelihood of a heart attack.

A breakthrough in clinical insights

Semmelweis’ team successfully processed and analyzed vast numbers of dimensions that make patients very different from each other and similar enough in the most critical ways to predict outcomes better and make better clinical decisions.

“Patient similarity analysis is an evolving paradigm for precision medicine,” said Márton Tokodi, Ph.D. Fellow at Semmelweis University Heart and Vascular Center. “In this data analytic approach, patients are grouped based on their similarities in multiple clinical features. We expect to achieve breakthroughs in prognostication through integrating multiple features of cardiac function and developing a phenotypic network in which patients can be mapped to specific locations associated with distinct disease stages and clinical outcomes.”

“The use of patient similarity analysis enables the automated staging of cardiac disease severity, personalized prediction of prognosis, and monitoring progression or response to therapies,” said Dr. Dávid Becker, Ph.D., Deputy Director of the Heart and Vascular Center, Semmelweis University. “Thus, it can significantly improve the utilization of real-world data in the search for therapies, which will lead to enhanced real-time clinical insights and decision-making.”

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Researchers will expand the use of EurekaAI to all medical research projects and patient data sets of Semmelweis University. Semmelweis is also a partner institution of the Hungarian Artificial Intelligence National Laboratory (MILAB), an umbrella project for the collaboration of all major research centers, universities, and large-scale national programs in Hungary. MILAB also funnels business needs and international research relations by leveraging the existing relationship of the partner institutions.

“Semmelweis University researchers are at the forefront of using AI in the life sciences and healthcare fields,” said Brennan Murphy, vice president at SymphonyAI Government Solutions, a division of SymphonyAI. “Variations in experience, data, talent, and process have created outsized gains for some organizations and frustrating failures for too many others. The researchers at Semmelweis, and the work they undertake globally, are emblematic of what teams can accomplish when technologists focus on speed to value for users, rather than forcing users to battle to get to that value.”

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