AI BASED DIAGNOSIS (RETROSPECTIVE DISEASE MODELS)
It’s estimated that 80% of health data is invisible to current systems because it’s unstructured. We can use cognitive technology to help
healthcare organisations unlock vast amounts of health data and power diagnosis. We can identify, review, retrieve and store far more
medical information – every medical publication, journal, symptom, and case study of treatment and response around the world –
exponentially faster than any human. And it doesn’t just store data, it’s capable of finding meaningful insight in it. Unlike humans, its decisions
are all evidence based and free of cognitive biases or overconfidence, enabling rapid analysis and vastly reducing – even eliminating –
misdiagnosis.
Our models are adaptive dynamic - analyzing patient's test data together with their medical data, superimposing relevant medical diagnostic
information obtained from KM library and fine tuning the disease diagnosis model.
We have developed disease identification models based on retrospective analytics and machine learning based pattern matching that assists
the healthcare practitioner to diagnose the disease with greater accuracy. These are industry standard robust disease diagnosis models built
on the principles of computational neuroscience.
Our disease diagnosis portfolio:
Diagnostic Imaging
Age related macular degeneration
Diabetic Retinopathy
Chronic Kidney Disease (CKD + CKDU)
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AI - BASED PROGNOSIS (PREDICTIVE DISEASE MODELS)
Prognostic or Predictive healthcare is essential to find out how a current condition could be expected to affect a person’s health in the future
within a definitive probability range, which use predictive models, statistical learning models and forecasts techniques to understand the
future and answer: “What could happen?”. Predictive analysis attempt to make an ‘educated guess’ as to the probabilities of certain
developments, based on past data and current test data of the patient. It also shows what is likely to occur if the patient continues down the
course it is already on, i.e., the cost of action/inaction.
Using the AI/ML/DL , Fuzzy systems, Bayesian and system dynamics driven pattern recognition to identify patient's risk, future progression of
the disease and probability of developing a certain condition is another area where AI is beginning to take hold in healthcare. We use multi-
feature multi-vitiate scenario Bayesian-MLMC analysis in our prognostic models to predict the progression with superior accuracy.
Our disease prognosis portfolio:
Prognostic Imaging
Age related macular degeneration
Diabetic Retinopathy
Chronic Kidney Disease (CKD + CKDU)
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PUBLIC HEALTH : AI - BASED DISEASE OUTBREAK PREDICTION
Our disease outbreak prediction portfolio:
Dengue outbreak prediction
Vector borne diseases outbreak prediction
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