(Photo : Humphrey Kanyoke) Hypertension is the leading cause of cardiovascular diseases. It is also a highly modifiable risk factor if treated on time and monitored well. Machine learning (ML) models have proven to be very effective in helping manage hypertension and have been known to outperform other traditional statistical tools. Humphrey Kanyoke, the CEO of MediTrak Life talks about his journey and the path that led him to develop innovative ML-based solutions to tackle hypertension. Kanyoke’s story also highlights what is possible when high-level public health knowledge and experience are merged with business acumen, drive, and principles.
"I was a public health researcher in Africa for many years, overseeing various aspects of public health research studies involving tens of thousands of study participants," he says. "Some of these included maternal health studies sponsored by the World Health Organization (WHO) and the Bill and Melinda Gates Foundation; studies which exposed me to the devastating effects of high blood pressure in pregnant women (preeclampsia). My interest in the condition originated from there. And then it intensified into a singular goal to help eliminate when my dad passed away from cardiac complications brought on by his hypertension."
It was while pursuing an MBA at Purdue University in West Lafayette, IN that Kanyoke founded MediTrak Life to actualize his vision of developing global scale solutions to combat hypertension. Almost 6 years later, and after more than $500,000 in funding from investors including Elevate Ventures, an exclusive IP license with the Purdue Research Foundation, several awards, and collaborations with leading researchers, MediTrak Life has deployed its premier hypertension management tool, Zeus.
Zeus is an ML-based predictive blood pressure management tool that allows physicians to curate personalized treatment plans for patients. Zeus is currently being used to treat patients at various hospitals and medical centers in Texas and Indiana, including IU Health.
"We basically asked ourselves a series of questions when we looked to conceptualize our approach: is it possible to granularize the broad, often cited causes of hypertension? Is it possible to turn these granularized causes into data points that can be quantified, monitored, […]
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