Home Can startup Suggestic tackle Type 2 diabetes?

Can startup Suggestic tackle Type 2 diabetes?

Suggestic is a digital startup trying to tackle Type 2 diabetes using machine learning to create personalized interventions and guide users through them towards disease reversal.  

The founders have created a “lifestyle GPS” app that works as a personal assistant to help people with Type 2 diabetes navigate towards a healthier lifestyle by providing contextual suggestions related to nutrition, physical activity, exercise, sleep and stress management. Focusing on the concept of precision medicine – targeted treatment based on an individual’s gene, environment and lifestyle – it employs machine learning technology to extract and validate causal patterns between each person’s genes, metabolism, diet and psychology.

I spoke to Shai Rosen, CMO and founder, who explained that the impetus for Suggestic came from personal experience, with himself and another founder having lost family members due to complications caused by Type 2 diabetes and two of their team being diagnosed pre-diabetic (now reserved). Shai detailed the need in healthcare to view the person beyond their medical self

“We need to really  understand who is the person and what is the right intervention for them…. to see the patient as not only a biological being but also psychological self which changes in different contexts. To really do this, we need machine learning,” Rosen said.

By adapting machine-learning models, Suggestic culls and analyzes correlated patterns between the user’s genes, diet and metabolism, then generates personalized recommendations to reverse the diabetes.

“Adequate control can lead in many cases to the reversal of symptoms and biomarkers enabling persons with diabetes to lead an almost normal life,” Shai explains. “That is the reversal we are aiming for by helping them change and maintain the healthiest lifestyle for each individual.”

Suggestic augments existing care

Users will download Suggestic’s app and receive baseline dietary and exercise advice for diabetics. From there “you add layers of personalization,” Shai says, from age and weight to lab results and known blood-sugar triggers, stressing that their role was not to replace that of health professionals, but rather to extend or augment their work, particularly in the area of better adherence to treatment and improved care. 

Suggestic is still in early stages of testing, having only recently moved from a  closed alpha phase to closed beta phase. They currently have a waiting list of over 1,000 people wanting to access their platform, which will be offered free to health consumers.

There’s also plans to incorporate components of their platform into employee wellness plans with the goal to produce reduced absenteeism and enhanced productivity at work and fewer doctor visits. “I always thought mainstream users would be the drivers, but we have also been approached by big health organizations and pharmaceutical companies,” Shai admits. “We’ve been impressed at how open the medical industry has been to use AI and talk about personalization, particularly when it comes to  lifestyle and treatment compliance.”

Suggestic is constantly crunching data and with an emphasis on evidence based medicine and is backed by a board of health professionals, geneticists and machine learning academics. They have a team of  investors that Shai considers “patient and really understanding of the time this kind of work takes. We will not hurry our process beyond what makes sense just to please an investor.”

The crossroads between machine learning and personalized healthcare is definitely something that will become an important part of medicine, and it will be interesting to see the outcomes of later trials by Suggestic as they work to improve the prognosis for those afflicted by diabetes.

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