Predictive analytics in healthcare: Forecasting diseases & outcomes with AI

Sajeev Nair, Pioneering Biohacker, Best-Selling Wellness author, and Founder, Vieroots Wellness Solutions talks about the role of predictive analytics in healthcare

Predictive analytics is taking the world by storm, and is now one of the most widely used applications of artificial intelligence and machine learning in the business world. But nowhere else is its application more useful than in the healthcare domain, and especially in its booming subdomain of prevention. 

This is because the core features of predictive analytics fit preventive needs in healthcare like a T. There are at least three such broad matches. Firstly, at its very core, predictive analytics is the method of using data to forecast future outcomes. And preventive healthcare is all about forecasting future outcomes so that adverse outcomes can be prevented. 

Secondly, for predictive analytics to work with great accuracy, huge amounts of relevant data are needed. Some domains don’t easily generate such data. But concerning healthcare, there is a huge amount of relevant, unique and readily available data on each human being. For instance, a detailed genetic test on a person can throw up a huge volume of genetic data that includes data about hard-coded genetic variants responsible for hundreds of lifestyle diseases. 

These can include common non-communicable diseases like diabetes, hypertension, cardiovascular diseases like heart attacks & strokes, cancers, dementia, COPD, arthritis, lupus, other autoimmune diseases, etc, as well as rare diseases too. 

Predictive analytics thrive on multidimensional data, and in this aspect too, healthcare has an edge. For instance, metabolic and lifestyle data can add highly useful layers to the predictive analytic models that primarily use genetic data. This is because while genetic variants of diseases are like loaded guns, what actually pulls the trigger is often metabolic factors or faulty lifestyles. 

Yet another dimension that makes healthcare such a unique data generator is the emergence of wearables like continuous glucose monitors (CGMs), smart rings, fitness trackers, and smartwatches. They can generate a steady stream of vital health metrics that can add immense value to predictive analytics. 

Thirdly, modern predictive analytics use not just data analysis but tools including AI, machine learning, and statistical modelling to find patterns that predict future outcomes. For this dimension of predictive analytics to work perfectly, there must be data on groups. And thanks to medical research, there is an ever growing sea of data on groups of people based on their age, sex, race, lifestyle etc. 

While a few decades earlier, there were only individual studies to rely on for such data, post the completion of the Human Genome Project (HGP) in 2003, there have been permanent and institutionalised approaches for the continuous generation of genetic, metabolic & lifestyle data. The best examples of such institutions include the UK Biobank founded in 2006 and the USA’s Million Veteran Program launched in 2011. 

With healthcare having such perfect synergy with predictive analytics, it is no wonder that powerful solutions that use predictive analytics have emerged in the healthcare field first before anywhere else. Examples include Eplimo, Lumiata, Entopsis & Atlas. All these have distinctly different approaches to prevention. While Eplimo is a genetic plus metabolic solution for preventive health, Lumiata creates a machine graph to replicate a doctor’s knowledge so that nurses too can use it. Entopsis uses signature analysis and comparison of protein composition of biofluids using machine learning to detect emerging health conditions, and Atlas uses a unique database of movements to perfect exercises. 

The genomic health-tech companies use a detailed genetic test combined with a comprehensive metabolic assessment of each client, and uses an AI based predictive analytics model to detect geno-metabolic risks for developing 200+ lifestyle diseases, years, or decades before they can develop. They also use AI based predictive analytics on a massive database of research on lifestyle interventions to unearth which all lifestyle modifications can work against the detected disease risks from the genetic and metabolic tests. Such research-validated lifestyle interventions span diet, nutrition, supplements, exercise, yoga, meditation, sleep, breathwork, ayurvedic herbals, alternative therapies, etc., that can keep these detected disease risks at bay.


artificial intelligence (AI)health newsmachine learningpredictive analytics
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