This peer-reviewed study was published in the PLOS One. The publication, “Performance analysis of a machine learning flagging system used to identify a group of individuals at a high risk for colorectal cancer,” validates of one of Medial EarlySign’s algorithms* on an Israel-based adult population.
The study found that EarlySign’s model can help identify individuals in the population who would benefit most from CRC screening, including those who are asymptomatic and who lack any clinical signs of CRC. The model used routine CBC data, age, and gender. The study found that the model’s automated interpretation of the CBC report can potentially be used as a tool to identify individuals who are at 10 to 20 times increased risk of harboring an occult CRC and who are candidates for screening colonoscopy.
This peer-reviewed publication gives healthcare systems, physicians, CMIOs, Chief Quality Officers, population health managers, and payors valuable insights from Israel’s second-largest healthcare provider on an adult population (50-75 years-old)
Readers will come away with a better understanding of how machine learning-based healthcare models can use ordinary, routine EHR data to identify patients who were non-compliant for screenings, but were likely to have a high-burden disease like CRC.
Highlights of the study include:
Machine Learning Flagging System Helps Identify Individuals at High Risk for Colorectal Cancer, Yaron Kinar, et al., PLOS ONE | DOI:10.1371/journal.pone.0171759 February 9, 2017
*Note: In the U.S. Medial EarlySign commercializes LGI Flag, which identifies individuals at high risk of having lower GI disorders. The algorithm analyzed in this study, ColonFlag, bears CE mark and is commercialized for use in the EU, UK, Israel.