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Maarten van Smeden

@maartenvsmeden.bsky.social
10.6K followers 484 following 320 posts

statistician • associate prof • team lead health data science and head methods research program at julius center • director ai methods lab, umc utrecht, netherlands • views and opinions my own

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Maarten van Smeden @maartenvsmeden.bsky.social · 05/10/2026
Thanks Frank! Agree with these points, most is in the paper. The issue we also address in the scoping review is that most papers do not provide sufficient detail to even understand the output (e.g. no information of reference pop or algo used) and tendency to over-interpret results (causal language)
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Maarten van Smeden @maartenvsmeden.bsky.social · 05/10/2026
NEW PAPER Shapley value explanations have been a very popular Explainable AI technique commonly used with the aim to improve transparency of black box machine learning models. Excellently led by @alcarriero.bsky.social www.nature.com/articles/s41...
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Maarten van Smeden @maartenvsmeden.bsky.social · 28/08/2026
Now I want a t-test with a confidence interval
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Maarten van Smeden @maartenvsmeden.bsky.social · 28/07/2026
To all academics
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Maarten van Smeden @maartenvsmeden.bsky.social · 20/07/2026
Don’t tell this to ChatGPT
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Maarten van Smeden @maartenvsmeden.bsky.social · 17/07/2026
I agree with you that Breiman was too optimistic about random forest and ignored what we can do with flexible regression models. But I'll give the founding father of "random forest" a little slack for being overly optimistic about his then still recent developments
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Maarten van Smeden @maartenvsmeden.bsky.social · 17/07/2026
Not a big fan of the van der Ploeg paper just mentioned. The reasons random forest was data hungry and prone to overfitting in that study were (what we now consider) bad tuning practices. Bad tuning practices (e.g. 1 SE rule) destroy penalized regression too pubmed.ncbi.nlm.nih.gov/38189632/
pubmed.ncbi.nlm.nih.gov
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Maarten van Smeden @maartenvsmeden.bsky.social · 17/07/2026
What I see is an increasing interest within a large portion of the stats culture in types of modeling that used to be dominated by CS/ML/AI people (e.g. tree-based learning, NN), for prediction tasks but also for inferential/explanation tasks, so distinction between cultures might further decrease
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Maarten van Smeden @maartenvsmeden.bsky.social · 17/07/2026
TBH, I don't feel that strongly about Breiman's paper. Yes, there things in the paper that after 25 years (!!!) we can conclude did not stand the test of time. What I do like is that the central thesis that there are indeed roughly two broad cultures to modeling, each with their own language
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Maarten van Smeden @maartenvsmeden.bsky.social · 11/07/2026
Oeps. Bewijs dat ik niet kan multitasken
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Maarten van Smeden @maartenvsmeden.bsky.social · 11/07/2026
Je kunt van alles vinden van deze data, maar laten we dat parkeren. De primaire studies laten heel heterogeen beeld zien van effecten. Maar in het rapport worden alleen de significante resultaten besproken en uitvergroot. Dus ook deze “correlaties” zijn niet bepaald overtuigend 2/2
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Maarten van Smeden @maartenvsmeden.bsky.social · 11/07/2026
Het rapport bestaat uit verschillende subonderzoeken. Het prospectieve onderzoek en onderzoek naar bacteriële oorzaken levert eigenlijk niets op. Dus de aandacht gaat uiteindelijk uit naar een literatuuronderzoek naar studies die hebben gekeken naar door huisarts geregisseerde longontstekingen 1/2
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Aliëtte Jonkers @aliettejonkers.nl · 09/07/2026
Lang gewerkt aan de reconstructie van dit verhaal. Zaterdag in NRC, vandaag al online: Heeft de geit het weer gedaan? Het bewijs is broodmager dat omwonenden longontstekingen krijgen door de geitenhouderij Cadeaulink 🎁 www.nrc.nl/nieuws/2026/...
nrc.nl
Heeft de geit het weer gedaan? Het bewijs is broodmager dat omwonenden longontstekingen krijgen door de geitenhouderij
Volksgezondheid: Zijn geiten opnieuw een gezondheidsrisico, na de eerdere uitbraak van Q-koorts? Zorgen ze voor longontstekingen? Onderzoek dat dat zou aantonen, wordt „volledig mislukt” genoemd.
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Maarten van Smeden @maartenvsmeden.bsky.social · 07/07/2026
You can use this pmc.ncbi.nlm.nih.gov/articles/PMC...
pmc.ncbi.nlm.nih.gov
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Maarten van Smeden @maartenvsmeden.bsky.social · 06/07/2026
😂
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Maarten van Smeden @maartenvsmeden.bsky.social · 02/07/2026
Normality : a very rare shape for data www.linkedin.com/posts/maarte...
linkedin.com
Statistical terms: what they really mean Multicolinearity— these variables all look the same Heteroscedasticity— the variation varies Attenuation— being too modest Overfitting— too good to be… | Maar...
Statistical terms: what they really mean Multicolinearity— these variables all look the same Heteroscedasticity— the variation varies Attenuation— being too modest Overfitting— too good to be true Co...
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Maarten van Smeden @maartenvsmeden.bsky.social · 09/06/2026
But I like the word methodology
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Maarten van Smeden @maartenvsmeden.bsky.social · 15/05/2026
Came across this gem again today. Still makes me giggle @richarddriley.bsky.social @gscollins.bsky.social Yes, it is published: pubmed.ncbi.nlm.nih.gov/32817260/
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Reposted by Maarten van Smeden
Darren Dahly @statsepi.bsky.social · 28/04/2026
🎯So many important points packed into this editorial. ht @maartenvsmeden.bsky.social www.nature.com/articles/s41...
nature.com
Show us the evidence for the value of medical AI - Nature Medicine
Claims that medical AI is improving care must be backed by appropriate evidence.
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Reposted by Maarten van Smeden
Eslam Maher @eslammaher.bsky.social · 25/03/2026
Appraising ML prediction models @maartenvsmeden.bsky.social 1) Share model and code 2) Predictors and language 3) Class imbalance & calibration 4) Model validation sample size 5) Is the model clinically useful in practice? www.jclinepi.com/article/S089...
jclinepi.com
Preliminary appraisal of machine learning based prediction models
A significant portion of healthcare research is devoted to the development of prediction models, yet the integration of these models into routine clinical care remains limited. Persistent barriers, su...
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Reposted by Maarten van Smeden
Frank Harrell @f2harrell.bsky.social · 17/03/2026
Fantastic new paper casting doubt on explainability of explainable AI. To explain complex machine learning algorithms you need reproducibility of the explanation at a minimum academic.oup.com/ehjdh/articl... #machinelearning #Statistics #StatsSky @maartenvsmeden.bsky.social
academic.oup.com
Signal or noise? Evaluating commonly used attribution methods for explaining deep neural networks in electrocardiogram classification
AbstractAims. Attribution-based explainability methods are widely used in electrocardiogram (ECG) analysis to interpret predictions from ‘black-box’ deep n
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Reposted by Maarten van Smeden
Ben Van Calster @benvancalster.bsky.social · 10/03/2026
Happy to see this in print! doi 10.1146/annurev-statistics-042324-123749 @maartenvsmeden.bsky.social @laurewynants.bsky.social @vanamsterdam.bsky.social and Ewout Steyerberg
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Maarten van Smeden @maartenvsmeden.bsky.social · 06/03/2026
Cluster analysis will always give you clusters
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Maarten van Smeden @maartenvsmeden.bsky.social · 06/03/2026
I have got the data and I figured out exactly how to do my cluster analysis all I need is a relevant question that my cluster analysis is going to answer
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Reposted by Maarten van Smeden
Donald Szlosek @dszlosek.bsky.social · 03/03/2026
What if you combine open datasets with AI? Apparently, a 3 fold increase in low quality research papers, mass-produced by paper mills. Interesting study in @jclinepi.bsky.social #academicsky #episky #medsky #Skystats Thanks to @maartenvsmeden.bsky.social for initially posting this on Linkedin!
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Ben Van Calster @benvancalster.bsky.social · 13/12/2025
Our guidance regarding performance measures for medical AI models is finally out! - Stop bashing AUROC, although it does not settle things - Calibration and clinical utility are key - Show risk distributions - Classification statistics (e.g. F1) are improper www.thelancet.com/journals/lan...
thelancet.com
Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance
Numerous measures have been proposed to illustrate the performance of predictive artificial intelligence (AI) models. Selecting appropriate performance measures is essential for predictive AI models i...
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Maarten van Smeden @maartenvsmeden.bsky.social · 05/12/2025
NEW PAPER The use of explainable AI in healthcare evaluated using the well known Explain, Predict and Describe taxonomy by Galit Shmueli link.springer.com/article/10.1...
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Maarten van Smeden @maartenvsmeden.bsky.social · 27/11/2025
🧐
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Reposted by Maarten van Smeden
Cameron Patrick @cameronpat.bsky.social · 25/11/2025
this is one of my favourite observations about sample size calculations. (afaik first articulated by Miettinen in 1985)
"On the first, and pivotal, level the decision is a binary one—the
choice between zero and nonzero as the size. Validity considerations alone are often sufficient to imply that zero is the optimal size"

- O. S. Miettinen "Theoretical Epidemiology" (1985)
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Maarten van Smeden @maartenvsmeden.bsky.social · 25/11/2025
Ha! I did not know I quoted Miettinen :). Thanks for the reference
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Maarten van Smeden @maartenvsmeden.bsky.social · 25/11/2025
For some research studies the optimal sample size should be estimated at 0
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Maarten van Smeden @maartenvsmeden.bsky.social · 18/11/2025
“Data available upon reasonable request” is academic language for you can get my data OVER MY DEAD BODY
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Maarten van Smeden @maartenvsmeden.bsky.social · 14/11/2025
I take version control very seriously
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Maarten van Smeden @maartenvsmeden.bsky.social · 14/11/2025
Manuscript_Final_Version_actualFINALcopy_version9b_USETHISONE.docx
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Maarten van Smeden @maartenvsmeden.bsky.social · 01/11/2025
Prediction models that are used to guide medical decisions are usually regulated under medical device regulation. This means, putting a calculator out there to promote the use your new prediction model is likely to break some rules.
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Maarten van Smeden @maartenvsmeden.bsky.social · 12/10/2025
The lasso works really well in particular settings and for particular purposes. If you are after high prediction performance alone and you have a rather large sample size, it can be an excellent choice indeed. But most analytical goals are not only about prediction
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Maarten van Smeden @maartenvsmeden.bsky.social · 08/10/2025
Kind reminder: data driven variable selection (e.g. forward/stepwise/univariable screening) makes things *worse* for most analytical goals
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Maarten van Smeden @maartenvsmeden.bsky.social · 25/09/2025
NEW FULLY FUNDED PHD POSITION Looking for a motivated PhD candidate to join our team. Together with Danya Muilwijk, Jeffrey Beekman and I, you will explore opportunities and limitations of AI in the context of organoids For more info and for applying 👉 www.careersatumcutrecht.com/vacancies/sc...
careersatumcutrecht.com
Vacancy — PhD position on AI methodology for prediction of patient outcomes using organoid models
Are you passionate about bringing personalized medicine to the next level and make real impact in healthcare? Join our team and develop novel AI methodology to improve predictions of relevant patient ...
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Reposted by Maarten van Smeden
Darren Dahly @statsepi.bsky.social · 22/09/2024
Interpretable "AI" is just a distraction from safe and useful "AI"
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Maarten van Smeden @maartenvsmeden.bsky.social · 19/08/2025
This is right tho. Let’s therefore call them sensitivity positive predictive value curves bsky.app/profile/laur...
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Maarten van Smeden @maartenvsmeden.bsky.social · 19/08/2025
For details: arxiv.org/abs/2412.10288
arxiv.org
Performance evaluation of predictive AI models to support medical decisions: Overview and guidance
A myriad of measures to illustrate performance of predictive artificial intelligence (AI) models have been proposed in the literature. Selecting appropriate performance measures is essential for predi...
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Maarten van Smeden @maartenvsmeden.bsky.social · 19/08/2025
No.
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Maarten van Smeden @maartenvsmeden.bsky.social · 13/08/2025
I wonder who those people are who come here dying to know what GenAI has done with some prompt you put in
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Maarten van Smeden @maartenvsmeden.bsky.social · 12/08/2025
If you think AI is cool, wait until you learn about regression analysis
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Maarten van Smeden @maartenvsmeden.bsky.social · 11/08/2025
TL;DR: Explainable AI models often don't do a good job explaining. They can be very useful for description. We should be really careful when using Explainable AI in clinical decision making, and even when judging face validity of AI models Excellently led by @alcarriero.bsky.social
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Maarten van Smeden @maartenvsmeden.bsky.social · 11/08/2025
NEW PREPRINT Explainable AI refers to an extremely popular group of approaches that aim to open "black box" AI models. But what can we see when we open the black AI box? We use Galit Shmueli's framework (to describe, predict or explain) to evaluate arxiv.org/abs/2508.05753
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Maarten van Smeden @maartenvsmeden.bsky.social · 31/07/2025
This is, however, not clever or safe writing, it is a bad collective habit that needs to stop. Not by avoiding references to causality but by clear referencing to it pubmed.ncbi.nlm.nih.gov/37286459/
pubmed.ncbi.nlm.nih.gov
Guidelines for Reporting Observational Research in Urology: The Importance of Clear Reference to Causality - PubMed
Observational studies often dance around the issue of causality. We propose guidelines to ensure that papers refer to whether or not the study aim is to investigate causality, and suggest language to ...
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Maarten van Smeden @maartenvsmeden.bsky.social · 31/07/2025
The healthcare literature is filled with "risk factors". This word combination makes research findings sound important by implying causality, while avoiding direct claims of having identified causal associations that are easily critiqued.
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Maarten van Smeden @maartenvsmeden.bsky.social · 24/07/2025
And taking this analogy one step further: it gives genuine phone repair shops a bad name
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Maarten van Smeden @maartenvsmeden.bsky.social · 23/07/2025
When forced to make a choice, my choice will be logistic regression model over linear probability model 103% of the time
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