RCTs vs Real-World Evidence for medical AI

Randomised controlled trials (RCTs) have been the gold standard for statistical evidence, of treatment effect, for over 100 years. Their strength is in their attempt to avoid major sources of bias in a comparison of the evidence. However, they are costly to run, particularly in the domain of personalised medicine, to which medical AI products typically belong.

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Invited Speaker: Dynamics of Immune Responses

I have been invited to speak at the Dynamics of Immune Responses workshop/seminar/conference in May-June 2020. The invitation arose through my previous efforts to found a company in this space.

There is a growing awareness in the field of immunology of the potential for using mathematical techniques. The wedge-issue here is the cascade of data appearing via new cytometry techniques; large-data looks like a math issue to most people. I of course come from the other side of a spectrum – everything looks like a math issue to me – I wanted to stimulate drug development which engages with immune system dynamics by founding my company.

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Causality and the Scientific Method

I have a short thought, stemming from a combination of projects that I’m working on at the moment, and I want to share it.

The current trend towards Causality in AI is very attractive to people like me. It matches our personal biases and views of the world. However, it is lacking a natural heuristic. How do we decide how much resources to devote to alternative models of the world, as we gather evidence as to their accuracy?

Like I say, I have a number of parallel projects, many of which address exactly this question on technical and biological levels.

Effectual entrepreneurship takes place in situations of high uncertainty and low knowledge. As uncertainty decreases, planning and management take over.

There is something from the world of business, studying entrepreneurship, which might be a better heuristic than any normative model I can come up with. Effectual entrepreneurship is a perspective on entrepreneurship, studying highly successful repeat entrepreneurs (eg. Elon Musk), which establishes control, rather than planning, at the core of entrepreneurial activities.

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Build – Test – Move

First a mea culpa, I have a huge backlog of relatively heavy articles that I really want to add to the blog. But I’ve been busy getting married – congratulations to me – and I didn’t have enough time. I strongly believe in following relatively strict guidelines on writing and editing articles, where I set myself deadlines and avoid over-writing on topics – it is just a blog after all – but for deep insights I do also have a minimum standard that I want to be able to produce before I’m willing to hit the Publish button.

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Why do Trees work better than DNNs on genome data?

This topic occurred to me following my recent talk at a dental conference at Charité Berlin. Upon hearing that I have a strong interest in inference, my fellow keynote mentioned that it drives him crazy that random forests, and similar algorithms, work so much better than DNNs on genomic data. He challenged me to come up with a reason for why this is the case.

I think that I know why. The problem I have is that I suspect that I can never prove it. That issue of not being able to prove things in machine learning is probably an equally interesting topic, for a future article, but here I want to address my theory of why random forests work better than DNNs for analysing genome data.

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Keynote @ Charité Berlin

Apparently, it’s that time again. I just gave my second invited keynote at a conference at Charité Berlin. It was really fun.

The audience were dentists – academic dentists. I confess that I struggled to understand why they thought I would be a good fit for their conference. My previous keynote was at the BIH Digital Health Forum – a much more obviously appropriate audience. But, perhaps strangely, the fit was very good.

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Mathematics and Biology III – Bioinformatics

When I sat down in Summer 2018 to begin my blog one of my goals was to write approximately 5 definitive articles about Mathematics and Biology. So far, I have been pretty hard on the efforts in both fields to come together. I began with a review of the very different world-views inherent in the two subjects – combined with a call to arms for likeminded people to come and help out. I followed this with a more practical consideration of the repertoire of techniques necessary and the career constraints, which actively work against combining these two disciplines. Today I want to consider the shining example of bioinformatics – the one area in which mathematics is clearly being used in biology and which demonstrates a clear career path.

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Mathematics and Biology II – Practical considerations

Last month I wrote about the historical world-views of mathematicians and biologists. These articles are part of a planned four part series, in an attempt to first understand and then improve the working relationship between these two key scientific disciplines. This is all a work in progress, so at the end, I will try to take the key learnings from each of the articles and distill them into a single composed article.

This month, I want to discuss the practical considerations why mathematics and biology still don’t work so well together.

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