SAN FRANCISCO, May 27, 2022 (GLOBE NEWSWIRE) -- Syapse®, a leading real-world evidence company dedicated to extinguishing the fear and burden of serious diseases by advancing real-world care, today announced two new studies focused on how the use of machine learning on real-world data can be used to power precision medicine solutions. Syapse will be presenting at the American Society for Clinical Oncology (ASCO) Annual Meeting being held June 3-7, 2022 in Chicago.
“This year’s ASCO is centered on a theme of innovation to make cancer care more equitable, convenient and efficient. Two studies that we are presenting align well with this objective, with a focus on how machine learning can be applied to real-world data to better bring identification of patient characteristics, and specific patient cohorts of interest, to scale,” said Thomas Brown, MD, chief medical officer of Syapse. “The transformational effort to pursue more personalized, targeted treatments for patients with cancer can be empowered by leveraging real-world data to produce insights in the form of real world evidence, as a complement to classical clinical trials.”
Unveiled at ASCO, the Syapse studies include:
- Using Machine Learning on Real-World Data to Predict Metastatic Status
One of the biggest challenges in using real-world data for precision oncology is determining clinically-relevant phenotypes at scale. This research demonstrates an accurate and scalable cancer metastasis classification method by leveraging machine learning models using EHR data. Systematic generation of accurate clinical variables such as metastatic status is important for realizing the value of large healthcare-generated data. These cutting-edge data science methods with high volume health care data facilitates the delivery of precision oncology at scale.
- Machine Learning Application to find Lower-Risk Myelodysplastic Syndrome Patients from Real-World Data
Identifying patients with myelodysplastic syndrome using electronic health records is labor-intensive and error-prone due to the complex heterogeneity of the disease phenotypes. This study demonstrates the power of machine learning models in determining patients with lower-risk myelodysplastic syndrome with significantly improved efficiency and accuracy, suggesting their broad applicability in cancer patient discovery and real-world evidence generation.
In addition to presenting this research at ASCO, Syapse has created an online ASCO hub with more information about its research, its interactive booth experience and how its work with real-world evidence is transforming data into answers that improve care for patients everywhere. For ASCO attendees, please visit Syapse at booth #18143 during the show.
Syapse is a company dedicated to extinguishing the fear and burden of oncology and other serious diseases by advancing real-world care. By marrying clinical expertise with smart technologies, we transform data into evidence—and then into experience—in collaboration with our network of partners, who are committed to improving patients’ lives through community health systems. Together, we connect comprehensive patient insights to our network, to empower our partners in driving real impact and improving access to high-quality care.
Christian Edgington, Media & Engagement firstname.lastname@example.org