Colin Hill is CEO, president, chairman and co-founder of GNS Healthcare in Cambridge, Massachusetts. His company developed the REFS (Reverse Engineering and Forward Simulation) technique, which combines big data, supercomputing and modeling to enhance drug development, patient stratification and other healthcare challenges.
Recently, he discussed this topic with Scientific American Custom Media (SACM).
Questions…and links directly to the answers.
EVP and co-founder Iya Khalil was named to the PharmaVOICE 100 list of the most inspiring people in the life-sciences industry. She was recognized for her ability to build bridges across the life-science and healthcare industries, bringing people together to harness the power of predictive modeling to change the lives of patients. Along with being a brilliant physicist and co-inventor of GNS’s machine-learning platform, Iya was recognized for her warmth and compassion, which, fueled by her vision for transforming healthcare, …
Sickle cell disease drug developer, AesRx, on whose board CEO and co-founder Colin Hill served, was acquired by Baxter International.
Read the Baxter press release here.…
Analyses of biomedical data from nearly 37,000 volunteer employees of a large company insured under Aetna shows a success rate of 80 percent to 88 percent in predicting risk of metabolic syndrome, which can cause chronic disease.
Metabolic syndrome means an individual has at least three of five biological characteristics that are out of normal range–waist circumference, blood pressure, elevated triglycerides, low high-density lipoproteins and increased insulin resistance–according to a report on the findings in The American Journal of Managed …
Health insurers can use big data to predict which individuals are more likely to develop metabolic syndrome and create personalized programs to help prevent the syndrome from developing, according to new research published in the American Journal of Managed Care.
The study analyzed 37,000 Aetna members with employer-based coverage, finding that predictive modeling tools can forecast the risk of metabolic syndrome down to the specific risk factor.
Targeting metabolic syndrome’s primary risk factors–large waist size, high blood pressure, high triglycerides, …
Aetna and a data analytics firm looked at medical claims of 37,000 policyholders to find the likelihood that those patients are at a risk of metabolic syndrome, according to research published last week in the American Journal of Managed Care.
Metabolic syndrome is characterized by five factors: a large waist size, high blood pressure, high triglycerides, high blood sugar and low high-density lipoprotein (HDL), considered the “good” cholesterol. Those factors can lead to chronic heart disease, stroke and diabetes. Combined, …
Aetna and GNS Healthcare use analytics to predict patients at risk for metabolic syndrome
Analyzing big data can predict patients’ future risk of metabolic syndrome and allow individuals and clinicians to work together on preventative steps that save lives and money.
While organizations have used a lot of big data projects to discern trends, a study conducted by Aetna and GNS Healthcare analyzed data from almost 37,000 members of an Aetna employer customer who opted in for screening of metabolic …
In the life sciences, data can come in many forms, including information about genomic sequences, molecular pathways, and different populations of people. Those data create a potential bonanza, if scientists can overcome one stumbling block: how to handle the complexity of information. Tools and techniques for analyzing big data promise to mold massive mounds of information into a better understanding of the basic biological mechanisms and how the results can be applied in, for example, health care.
“Big data” is …