What is Cognome?

Cognome is an independent corporation I formed in October 2018 to facilitate the adoption of my research and to advance large scale adoption of advanced analytics and digital technology in healthcare. The Pandemic interrupted our journey and we finally signed license agreements with the Montefiore Health System and the University of Texas on May 31st, 2024. 

The corporation's proprietary platform marshals advanced analytics (AI/ML/DL/RL), digital technology, and data integration to re-engineer clinical practice and healthcare operations. Cognome's vision is to exploit information generated from every patient in every context to iteratively transform the healthcare sector into a continuously learning precision experience for patients, clinicians, and the systems that deliver care. At Cognome we believe that opportunities exist to create a net new market for a generation of products and services that combine technical innovation, systems integration, and public-private partnerships to transform how safely and efficiently large integrated systems deliver healthcare.

We were incubated within The Montefiore Health System which is our first customer and strategic partner. Over the past five years Intel^TM, and the Montefiore Health System have partnered in several tracks of technology innovations and integration research to improve the overall performance and quality of the healthcare system. We are preparing to roll out at more large academic medical centers later this year.

Below are several links to papers and publications that highlight our technology. Separately, I will shortly be publishing a comprehensive peer reviewed paper that describes our ambitions more completely.

Peer reviewed papers explaining a selection of our AI/ML models:

• Oct 2018: BMC Critical Care, Multicenter derivation and validation of an early warning score for acute respiratory failure or death in the hospital
• Dec 2022: Journal of Clinical Anesthesia, Incidence and predictors of case cancellation within 24 h in patients scheduled for elective surgical procedures
• Aug 2023: Journal of Clinical Anesthesia, Development and validation of a machine learning ASA-score to identify candidates for comprehensive preoperative screening and risk stratification
• Sept 2023: BMJ Health & Care Informatics, Long short-term memory model identifies ARDS and in-hospital mortality in both non-COVID-19 and COVID-19 cohort
• Nov 2023: Journal of Clinical Anesthesia, Development of an automated, general-purpose prediction tool for postoperative respiratory failure using machine learning: A retrospective cohort study

Other peer-reviewed articles that are germane to our platform include:

• June, 2022: IEEE, Entity Event Knowledge Graph for Powerful Health Informatics
• Oct, 2022: OP Science, OpenFL: the open federated learning library
• Oct 2018: Scopus, Knowledge graph solutions in healthcare for improved clinical outcomes
• Aug, 2018: American Heart Journal, Leveraging electronic health records for clinical research
• Nov 2016: JAMIA, Preserving temporal relations in clinical data while maintaining privacy
• Feb 2016: BMJ Open, Early intervention of patients at risk for acute respiratory failure and prolonged mechanical ventilation with a checklist aimed at the prevention of organ failure: protocol for a pragmatic stepped-wedged cluster trial of PROOFCheck
• Jun 2009: Sage Journals, Public Health Surveillance Meets Translational Informatics: A Desiderata

A complete list of my peer reviewed articles can be found here.

And finally, articles that circulated in other media describing work we are doing:

• Sept 2018: Initial announcement of our platform in Health Tech before the pandemic
• May 2019: Wall Street Journal Article describing our first AI Model to prediction respiratory failure
• June 2019: Technical overview of Cognome’s platform from June 2019 (note: our platform was called PALM back then).