Causal Diagnostics
Despite the many advances in medical technology, practical clinical diagnosis is still largely based on statistical symptom pattern-matching. The idea that a cluster of symptoms can lead to the correct diagnosis is often an oversimplification. Not only symptoms are not binary (either one has it or not) but complex diseases are multifactorial and can display a wide number of dissimilar effects and confounding signals. To better deal with probable root causes OIA leverages our years of research in model-driven causal discovery at some of the best universities (Oxford, Cambridge, Karolinska, Imperial) developing the area of causal diagnostics, a cutting-edge and responsible model-based approach to medicine different from simplistic AI approaches based on statistical classification regularly unable to provide generative models or explanations for their arbitrary choices.
At OIA we have created and continue creating a family of novel and interdependent remote and mobile solutions for rapid and affordable generation of personal longitudinal data using sophisticated neuro-symbolic machine learning repurposing off-the-shelf electronics augmented with this responsible AI. Our mission is to build the first portable and personal general-purpose medical device to help with patient monitoring and super-early diagnosis.
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Longitudinal, non-invasive, (quasi) real-time and cost-effective health monitoring is hugely important if we are to improve public health and deliver the promise of personalised medicine for super early accurate diagnosis.
Causal diagnostics can revolutionise medicine by refocusing on first principles, and causal generating mechanisms to redesign treatment and drugs beyond often symptom relief. The training and inference engines behind causal diagnosis are based on what is identified as neuro-symbolic computation, a combination of the best AI tools with cutting-edge expert systems that rely on both mind and machine to fight disease. 
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We call our neuro-symbolic computation approach a type of responsible Artificial Intelligence (AI) because it goes beyond current black-box approaches in AI in order to reach a deeper understanding to critical questions intended to unveil root causes in areas of medicine.
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Responsible AI
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While techniques such as machine & deep learning (e.g. deep neural networks) are very powerful in tasks related with classification, they are only so under very controlled conditions and are ill-equipped to deal with basic cognitive functions that we as humans take for granted, including basic logical inference and abstraction for model generation and hypothesis testing.
The combination of symbolic computation and statistical machine learning is thus a a powerful combination to tackle complex problems and a way forward to achieve better and more responsible A.I. This is because it offers interpretable models that correspond to observations and can be tested rather than encoded in obscure black-box explanations. This is what OIA has been successfully pioneering, developing and implementing for several years, translating cutting-edge science to real-world human challenges. |
To illustrate the limitations of statistical AI, no amount of big data or statistical massaging (no matter how 'sophisticated') can make a traditional deep neural network to perform a simple arithmetic operation if it has not seen it before because it has no concept of number or symbol and will treat the whole term as an object.
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Personalised Healthcare
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OIA has developed an innovative risk assessment score used in our remote blood testing and analytics solution Algocyte. The measures were tested against 100 thousand cases demonstrating that they pick up signals to separate healthy from unhealthy groups even from noisy sources (the NHANES database from the CDC) . On the center (Score) is the numeric score that goes from 0 (healthy) to 10 (unhealthy) quantifying how removed are a person individual values against reference population values. On the right (NCCIS) is the non-linear colour-coded score that separates further the healthy from unhealthy groups. On the left is how the scores are displayed and utilised by Algocyte.
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The score adapts and learns over time with incremental precision each patient own baseline allowing the application to flag for deviations smaller than the traditional reference population values thereby allowing a level of personalisation and sophistication not available before in the analysis of blood test results. On the left can be seen how the adaptive score follows changes captured by the non-adaptive (normal) score but, more important, it adapts (right figure) over time resetting new normal v abnormal reference intervals adapted to each patient.
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These scores are at the centre of Algocyte's machine learning system to automate tasks such as patient triaging by required attention, and patient profile alerting.
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Selection of publications
We have shown how our algorithms can deal with complex biological systems even in the face of very little data by using model-based approaches combined with sophisticated data meaning extraction.
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World media coverage of our research methods behind causal diagnostics
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