Health matters. It matters to each of us as individuals and to society as a whole. It lies at the heart of our economic, political, social and environmental prosperity and is one of the largest industries in the world.

Modern health systems can treat and cure more diseases than ever before. New technology is bringing innovation to old treatments. Yet significant quality, access and cost issues remain and our health systems are becoming increasingly unsustainable.

AI increases the ability for healthcare professionals to diagnose the disease from the symptoms and biomarkers, assist in finding current severity, predict the disease progression trend and end state with definitive probability and  better understand the day-to-day patterns  and needs of the people they care for, and with that understanding they are able to provide better feedback, guidance and support disease cure and for staying healthy.

AI is already being used to detect diseases, such as cancer, more accurately and in their early stages. For example, according to the American Cancer Society, 12.1 million mammograms are performed annually in the US, but a high proportion of these mammograms yield false results, leading to 1 in 2 healthy women being told they have cancer. The use of AI is enabling review and translation of mammograms 30 times faster with 99% accuracy, reducing the need for unnecessary biopsies as well as reducing the uncertainty and stress of a misdiagnosis.
Our Healthcare industry services

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 –

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)

Learn more...

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)

Learn more...

Proscriptive healthcare analytics attempt to quantify the effect of future decisions in order to advise on possible outcomes before the decisions are actually made. At their best, prescriptive analytics predicts not only what will happen, but also why it will happen providing recommendations regarding actions that will take advantage of the predictions.
Improving patient care requires the alignment of big health data with the appropriate and timely decisions. The innovation of prospective analytics will support clinical decision-making and deliver administration priorities and actions.

We use optimization and simulation algorithms to advice on possible outcomes and answer: “What should we do?” it not only aims to predict developments and emerging patterns, but to actually offer possible courses of action and estimate their probable outcomes. We use optimized search algorithms to identify, review, retrieve and store medical information from every medical publication,  journal, symptom, and case study of treatment and response around the world and use advanced NLP techniques to extract relevant information to assist the future course of actions and develop our decision science models.

Our disease prospective healthcare decision portfolio:

Chronic Kidney Disease (CKD + CKDU)

Learn more...

Our disease outbreak prediction portfolio:

Dengue outbreak prediction
Vector borne diseases outbreak prediction

Learn more...
© 2019 Macroprism Technologies LLP (“Macroprism”).  All rights reserved.
Ai > Decision Science