Presentation for EAPS 2022 congres in Barcelona

1. Possibilities and pitfalls of
AI in PICU
Joppe Nijman
Pediatric Intensivist / Clinician Researcher
University Medical Center Utrecht
EAPS congress 2022
Slideshare.net/joppenijman

2. Faculty Disclosure
Company Name
Honoraria/
Expenses
Consulting/
Advisory Board
Funded
Research
Royalties/
Patent
Stock
Options
Ownership/
Equity
Position
Employee
Other
(please specify)
X No, nothing to disclose
Yes, please specify:

3. “ Big data is data that contains
greater variety, arriving in
increasing volumes and with
more velocity, making it difficult
or impossible to process using
traditional methods.
3

4. 4
Photo archive Wilhelmina Kinderziekenhuis, Utrecht, The Netherlands

5. 14 patients
Admitted daily
266,112,000 meas
Every single day
~220 parameters
Measured every second
5

6. 6
Beam et al. JAMA 2018.

7. 7
https://bit.ly/2v2aokk 2017.

8. “ Artificial intelligence will not
replace doctors.
But doctors who use a rtificial
intelligence will replace those
who don’t.
8
Berci Meskó - https://medicalfuturist.com/

9. How (not) to make your AI model
9
1 3 5
6
4
2
Healthcare data
Chosing the right
statistical method
Model evaluation &
prediction
Data preparation Modelling Implementation,
legal & ethical
issues

10. Type of data
⬡ (Semi)-structured vs unstructured
⬡ Text, numbers, images, etc.
Data availability?
 FAIR data
(Findability, Accessibility, Interoperability, Reusability)
Healthcare data
10
Johnson et al. JACC 2018

11. Research for new breast
cancer biomarkers
⬡ Structured data
⬡ Mainly numbers (imaging)
⬡ Machine learning?
∙ Amount of data
∙ Complex interactions
Example
11
Rodrigues-Ferreira et al. Cancer Lett 2022.

12. Garbage in = garbage out
⬡ Measurement error
⬡ Missing data
⬡ Normalization
⬡ Unclassified data
⬡ Confounding (e.g. treatment effects)?
Data preparation
12
Johnson et al. JACC 2018
https://xkcd.com/1838/

13. Measurement error, missing data and treatment effects
13
Zoodsma et al. Unpublished data 2022.

14. 14
Sepanski et al. Front Pediatr 2018.
Heterogeneity & normalization

15. Automated sepsis prediction
algorithm in EHR EPIC
Unclassified data
15
https://www.statnews.com/2021/09/27/epic-sepsis-algorithm-
antibiotics-model/
Huat Goh et al. Nature Comm 2021

16. Technique selection
⬡ Conventional
∙ E.g. Regression
⬡ Advanced data science / machine
learning / artificial intelligence
16
Johnson et al. JACC 2018

17. 17
Lorberfeld. Towards Data Science (towardsdatascience.com) 2019.

18. Modelling & performance
18
Johnson et al. JACC 2018

19. Performance
19
Swalin. KDNuggets 2018
Auyeung. CK-12 foundation.

20. Your model is finished!
20
Johnson et al. JACC 2018

21. Your model is finished!
21
Wynants et al. BMJ 2020
Sperrin et al. BMJ 2020

22. 22
Plana et al. JAMA Net Open 2022.

23. 23
Van Royen et al. Eur Resp J 2022.

24. Small steps in PICU…
24
Aczon et al. Pediatr Crit Care Med 2021.
Ehrman et al. Front Pediatr 2022.
Matam et al. J Clin Mon Comp 2019.
Shah al. J Clin Med 2020.

25. Small steps in PICU…
25
& the next talks
in this session

26. Interested?
Join the Data Science Working Group of ESPNIC
⬡ https://www.equator-network.org/ (TRIPOD-AI)
⬡ https://www.clinical-trials.ai/ (CONSORT-AI)
⬡ https://www.go-fair.org/ (FAIR data use)
⬡ https://datacamp.com
26

27. 27
Thanks!
Slideshare.net/joppenijman
Any questions?
j.nijman@umcutrecht.nl
https://picudatalab.com
@joppe_nijman
Design: slidecarnival.com; icons: thenounproject.com

28. Feature selection
28
Mourad et al. Scientific Reports 2020