Klarity Health Risk
Predictions – K Scores

Accurate and Actionable AI Powered Risk Predictions

How it works

The pipeline of AI and Expert consensus algorithms ingests wearable and self reported data and produces personalized risk predictions.

Sync

Wearable

Data

Self-Reported

Data

Machine

Learning

Model

Expert

Consensus

Algorithm

Chronic Disease and premature mortality risk prediction

Type 2

Diabetes

 

Cancer

 

Heart

Attack

Stroke

Chronic

Bronchitis

Dementia

 

Angina

High

Blood

Pressure

Mortality

We provide a range of risk prediction models – overall health score, premature mortality, chronic disease risk, including specific models for six main types of cancer, diabetes and more being developed.

If you want to learn more about our risk prediction models, watch the video below:

Health Library

Preventive Health

Based on our data insights we empower individuals to change their health lifestyle through specific and personalised recommendations

The Pros and Cons of the Gluten-Free Diet

Could the Mediterranean Diet Improve Your Respiratory Health?

Cardiovascular Response to Exercise: What are the Physiological Changes that Occur?

View more

AI Data Science Team

Oliver Gale-Grant

Senior Scientific Advisor

9 years experience as a NHS medical doctor and researcher. Expertise in healthcare, data science and computational modeling

Dr. Christy Lane, PhD

Senior Scientific advisor

Dr Lane is the Co-Founder Stanford Wearable Health Lab and Professor at Mount Royal University. Founder and CEO of Vivametrica, a wellness & insuretech company that uses data from wearable devices to predict mortality and disease risks. Vivametrica was acquired by Sprout in 2020

Max Bell, MD, PhD

Senior Clinical advisor

Associate Professor, Senior Lecturer, Head Consultant, ICU, Perioperative Medicine and Intensive Care Medicine, Karolinska University Hospital (Stockholm)

Dr Bell has been and is currently working on several large epidemiological project using Big Data analysis. He is involved in both clinical and academic investigations focusing on personalised medicine; this entails using multiple prediction

Filippo Menolascina, PhD

Senior Scientific advisor

Dr Menolascinais Assistant Professor in Computational Biology and Bioinformatics at the University of Edinburgh, former COO at the Edinburgh Genome Foundry and CSO of Oliba Sr (precision medicine consulting for pharma SME’s).

Andre Ng

Junior Data Scientist

A first-class master’s graduate in Health Data Science, with a strong background in data analytics, statistics, and healthcare research. Proficient with R, Python and SQL.

Alex Moore

Senior Data Scientist

Oxford Neuroscience graduate and computation neuroscience (machine learning MSc). Operating as the lead Data scientist, focused on preventative medicine and personalised medical risk prediction.

Sulochana Subramaniam, PhD

Senior Data Scientist

10 years of experience in the disciplines of machine learning, computer vision, signal processing, image processing and Geospatial Engineering for processing several sorts of data, including images from remote sensing and medical data

Ken Towning

Senior Technology adviser

Ken was the CTO of the NHS Spine Project

Spine was a £12bn NHS technology project aimed at connecting clinicians, patients and local service providers throughout England to a number of essential national services including the Electronic Prescription Service, Summary Care Record, e-Referral Service and Demographics.