Narrative and Decision Models

Since I managed to break my writers’ block on decision making models last week I want to follow-up with a brief discussion on the use of Narrative in presenting decision models to an audience.

In my first article on decision making models I emphasized that a model must serve a purpose. In explaining our models to others I want to highlight that there are two purposes behind explaining a model; the first is to convince the audience; the second is to convey insights into the model. This is the opposite ordering of how scientifically-trained modellers typically think about communicating results, but it is by far-and-away the prioritisation of most top scientific communicators around the world.

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Decision Making Using Models 3.0

This is my third attempt, over the course of 9 months, to write this article. The first attempt foundered on my desire to go into detail on whether explanation or explanability is a good characteristic of a model or not. I confess, this was overly motivated by my personal frustration at having worked with somebody who, “never let the facts get in the way of a good story.” The second attempt got lost in a forest of anecdotes from previous projects. I was trying so hard to knit them together that I failed to make a point. Today, I want to focus on the single most important thing that I have learned about developing decision making models.

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New hosting

I have had to move my website hosting this week. I was hosted by the computer society at National University of Ireland, Galway for many years. Their hardware is now on its last legs and the building it is hosted in has been turned into a field hospital for Covid patients. It was time to move on.

Many thanks to Compsoc at NUI Galway for the years of hosting.

There is no such thing as a manager

I had the opportunity to interview for a senior position at a very big company recently. The entire process was fascinating for what it says about human nature and about large companies. The outcome of the process is unclear at the time of writing this but I am expecting the intrinsic misalignments in the process will lead it into the reeds from which it is unlikely to emerge. I am as close to the perfect candidate for the role that they will ever interview, but the internal parties are not all aligned around the very existence of the position.

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Talks cancelled – Talks online

I had quite a nice spring season of talks planned for 2020. I was invited to deliver a keynote on AI in Healthcare at Biovaria. And, I was one of the invited speakers for the Dynamics of Immune Repertoires conference where I would also have given a workshop, in Dresden. Covid-19 struck and the rest is history.

Emergencies lead to quick changes of plans. Anthony Kelly from AI in Action reached out to me asking me to take part in a special on AI in Healthcare.

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Writing quickly

Every blog I read eventually contains a post about i) navigating the blog, and ii) the author’s policy on writing. Consider this my attempt at the latter.

I have mentioned before that I find that writing benefits my long-term thought processes. It is meditative. I am forced to formalise my thoughts and chase-up loose ends. I have never considered myself to be good at writing – I failed English in school – but I find my confidence growing as I get older.

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The era of the solo contributor is dead

I have been reconnecting with some of my academic friends. We all belong more or less to the same age cohort. In recent weeks, I have been watching them interacting with one another on Twitter and through various other media. They each have achieved considerable degrees of success in their chosen fields – all have tenure at global top-50 ranked institutions. Through my observations, I have come to the realisation that the era of the solo contributor is dead.

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Data Science in Biomedical Industry

I am asked quite often how I see Data Science in the biomedical industry. I have, of course, many answers each of which is context dependent. However one theme which I find frequently recurring is a sort of straw-man debate which seems to inherently attract technical practitioners.

The debate is usually structured as follows:
How do you see the validation of medical AI products working in practice?
Answer: clinical trials, test-validation sets, blah, blah
But doesn’t this lead to enormous overheads?
Answer: yes, but there are shortcuts
But if you take these shortcuts then don’t you run the risk of running into costly failures when you finally run the clinical trials?
It goes on….

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Preprint Announcement – AI in Medicine Product Development Framework

Working in industrial research is usually very motivating but occasionally it is also frustrating. You’ve just done something really cool but you’re not allowed to tell anybody outside the company about it. Indeed, in a small company there might not be anybody inside of the company who can even appreciate it!

I have worked on roughly 4 really cool projects since leaving academia at the end of 2017. And apart from some basic mentions in my blog (e.g. here and here) most of what I have done has been known only to a few key stakeholders.

Since leaving Fosanis last September I have had a visiting researcher affiliation at the Digital Health Accelerator of the Berlin Institute of Health. I have used my time to mentor a cohort of teams attempting to spin out their ideas; to work on a causal inference project; and, to write a paper about the structural aspects of medical AI products. This week, along with my co-author Vince Madai, we submitted that paper.

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