Clinerion and Pangaea partner to improve cancer patient lives

Clinerion, provider of the Patient Network Explorer platform which harnesses patient data from a global hospital network, and Pangaea, a machine learning company, partner to improve faster identification of patient cohorts based on phenotypes from electronic health record (EHR) data.

Pangaea’s partnership with Clinerion allows it to apply its machine learning algorithms to de-identified patient EHR data and unstructured doctors’ notes through Clinerion’s federated network of hospitals across 21 countries in Europe, Middle East, South America and Asia Pacific. Pangaea’s and Clinerion’s customers, scientists and clinicians from the Biopharmaceutical industry, will be able to find patients of specific phenotypes from across these different regions at speed and with higher accuracy.

Pangaea Data provides a machine learning based software product to its customers from the biopharmaceutical and healthcare industry for faster identification of patient cohorts based on phenotypes (clinical characteristics and symptoms) from electronic health records (EHRs) and unstructured doctors’ notes. This is critical for detecting patients at risk of diseases, finding genes linked to a phenotype in the context of drug or biomarker discovery, recruiting patients for clinical trials and real-world evidence studies.

Pangaea’s technology is based on the founders’ work over the last 20 years and is proven to be at least 50 times faster and 30% more accurate than alternatives such as rule-based natural language processing (NLP) or keyword searches. The company is in strategic partnerships with public and private EHR data providers who provide it access to more than 20 million de-identified patient EHRs and medical notes.

“Pangaea was founded on the principles of translating complex information from healthcare data into standardised medical concepts so it is faster to query such data and the results are accurate,” explains Dr Vibhor Gupta, CEO of Pangaea. “Our approach is also shown to improve the quality of EHR data, which results in faster identification of patients based on phenotypes, which is critical for drug discovery, clinical trials and real-world evidence studies. Our partnership with Clinerion allows us to apply our machine learning algorithms to de-identified patient EHR data and unstructured doctors’ notes through Clinerion’s federated network of hospitals.”

“By leveraging Pangaea`s Machine Learning algorithms we will be able to identify more patients from unstructured datasets,” says Ian Rentsch, CEO of Clinerion. “This will also allow the Hospital users to dive deeper within their own EMR to identify additional patients for treatment”

About Clinerion

Clinerion accelerates clinical research and medical access to treatments for patients. We use proprietary technologies for analysis of patient data from our global network of partner hospitals. Clinerion’s Patient Network Explorer radically improves the efficiency and effectiveness of clinical trial recruitment by offering data-driven protocol optimization, site feasibility evaluation and real-time patient search and identification to match patients to treatments. Our technology solution provides real-world evidence analytics for medical access. Clinerion facilitates the participation of partner hospitals in leading-edge, industry-sponsored trials and time savings in patient recruitment. We create innovative and disruptive fit-for-purpose solutions which enable pharmaceutical companies to shorten patient recruitment and save costs by streamlining operations and leveraging strategic intelligence. Clinerion’s proprietary Big Data analytics technologies leverage real-time data from electronic health records which comply with international patient privacy and data security regulations. Clinerion is a global data technology service company headquartered in Switzerland.

About Pangaea Data Limited

Pangaea Data provides a machine learning based software product to its customers from the biopharmaceutical and healthcare industry for faster identification of patient cohorts based on phenotypes (clinical characteristics and symptoms) from electronic health records (EHRs) and unstructured doctors’ notes. This is critical for detecting patients at risk of diseases, finding genes linked to a phenotype in the context of drug or biomarker discovery, recruiting patients for clinical trials and real world evidence (RWE) studies.