A global oncology real-world data and analytics company, COTA, Inc., recently collaborated with Google Cloud. The collaboration aimed to develop algorithms that will extract and interpret unstructured data from electronic health records (EHRs). Both companies will use machine learning (ML) and natural language processing (NLP) technologies to curate text fields such as clinician notes, transforming them into structured fields that can be used for research and analytics.
COTA and Google Cloud have formed a technology alliance to address this challenge head-on, with the goal of catalyzing a new era of transformative innovation in oncology research and cancer patient treatment.
Miruna Sasu, President and CEO at COTA, Inc., said, “Imagine a scenario where we can be alerted, in real-time, to new diseases or receive signals from geographies where patients are experiencing better outcomes, or poorer outcomes so that we can take action quickly. In order for this to become our reality, we must leverage technologies to ingest healthcare data responsibly, accurately, and expeditiously. We are delighted to partner with Google Cloud to combine our respective strengths in technology and data science with the ultimate goal of improving care for patients.”
COTA will work with Google Cloud to supplement manual, human-led abstraction with technology-first abstraction and curation best practices. Over time, using this method will give access to even more sophisticated data elements that might be hidden in unstructured notes.
Shweta Maniar, Director of Life Sciences Industry Solutions at Google Cloud, said, “We are collaborating with COTA to build a series of new natural language processing models tailored specifically to unstructured oncology data, including emerging data such as genomic sequencing. By training these algorithms specifically on oncology information, we will partner with COTA in generating a much more complete understanding of what is happening in the cancer care setting and how a patient’s unique clinical history may impact their response to treatment.”
