Tag: Machine Learning in healthcare

Machine Learning in healthcare is an AI capability. In Savana we use machine learning to generate predictive models in clinical research.

How to never become a world expert in AI.

The reason why Francisco Franco could not do AI.

The specific way to use data layers to predict.

What I learnt about forescasting from the US Government.

If I ask you what predictions you’ve gotten right in your life, almost certainly one or two will come to mind.
What you saw coming before it happened.
It’s also likely that the guess evokes in you a kind of regret that you didn’t do anything about it.
“I got it right, but it didn’t do me any good because I didn’t do anything with it.”

There are some very clever people out there who actually do something with forescasting. Putting money in or whatever.

They don’t just sit at the bar, like you and me.
If I had done something about it I would have been able to monetize things I’ve gotten right in my life, like:
– The rise of China
– Craft beer
– Bitcoin.
A few years ago I studied in California with some gentlemen who worked for the American government and who had some methodologies to get things right.
They structured and systematized uncertainty. Obviously they didn’t get everything right, but they got something more than average.
It is curious that there are a couple of techniques for this.

And it’s not the tarot or a crystal ball.

One is to look for strange or surprising things. If something doesn’t add up on the street or in the newspaper, watch out. There’s a tendency there.

The other fascinates me…
And that is that some things are so big that we don’t see them.

Like the particular day when more people moved to live in cities than in the countryside. It was around 2010 or so.

The day Humankind reached its peak oil usage. Also around that time.
These are things that are so big that you don’t see them,
And there are trends there.

This happens with data in health.

It’s so big that most people don’t see it.
You don’t see what you can get out of it.

And we’re not talking about complicated things like asking disruptive predictive questions.

We’re talking about everyday things, like understanding the effectiveness of an intervention.
Or pulling out epidemiology where you only had indirect and biased studies.
Or knowing which biologics are being most cost-effective by patient phenotype.

Look, I only got going with one of my accurate predictions.

And that was with AI in Medicine.
It’s still a little while for most to realize its power.
If you want to get ahead of the curve, it’s here.

The obvious reason why you shouldn't say "big data" anymore.

Hey, maybe we could stop using the expression “big data”.
In the worlds that are more advanced than ours in technology, almost nobody says it anymore.
And I guess the reason is that it is redundant. In other words, it is already understood that in 2022 almost all “data” is “big”.
In any case, we could talk about “small data” when the opposite happens.
But, well, this is just me being a little bit of a fad.
What interests you is to realize that having brought massive data analysis techniques to healthcare has allowed us to see where we could not see before.
So please pay attention to this. It´s important,

It is possible that you have ever had your hands on a database.

And you thought you had a lot of patients.
But gradually there were fewer and fewer of them as you were left with:
– women
– of age between X and Y
– with breast cancer
– but not just any breast cancer but this breast cancer
– but not just this breast cancer and that’s it
– but it also has to have this marker
– and this staging
– and this mutation

– and this treatment

And yeah…

It turns out that in the end there are very few.
And what was big, a little while after the excitement, is already small.

If you were trying to describe the epidemiology, you still don’t care….

But if you were trying to look at the effect of a drug…

…welcome to the “effect size dilution.”

Our inseparable companion.

Against which, however, we have recently found a solution.

It has to do with not taking a few data here and there, but taking them all.
But all means all.

Of course, this cannot be done by human beings, because it would be unmanageable and unaffordable.

A machine can do it.
In other words, if I want all the music, I don’t go to a store and get all the records, I subscribe to Spotify. You know what I mean.

The machine has to be able to read any format, any file, any system from any hospital. In multiple languages.

Otherwise, no party.

We call our machine to do that Savana Data Gatherer.

But well, the name is the least important thing.

What matters is that it works.

And yes, it is extremely respectful of privacy and data ownership.

You say how big you want it, from 1 hospital to 3 continents.


So if you want to avoid effect size dilution, it’s here.

Complete the info, and a KAM will contact you ASAP:

Want to use it?:

Start with your proposed AI + RWE use case:

This is the first step for AI + RWE: