Machine learning in medicine: calm down

Machine learning in medicine:
calm down
Ben Van Calster
Department of Development and Regeneration, KU Leuven (B)
Department of Biomedical Data Sciences, LUMC (NL)
Research Ethics Committee, University Hospitals Leuven (B)
Epi-Centre, KU Leuven (B)
Innsbruck, November 8th, 2019

No holy grail
3Indiana Jones and the Last Crusade, 1989.

Contents
• What are we talking about? AI, ML, and its applications
• ML instead of traditional statistics: success guaranteed?
• Typical applications of ML in medicine
• Ten concerns for model development, validation and implementation
• Beyond the hype
4

Artificial intelligence (AI)
6
Attributed to John McCarthy (Stanford) in 1955:
“Every aspect of learning or intelligence can in principle
be so precisely described that a machine can be made
to simulate it.”
Russell and Norvig (2010):
- Think like a human
- Act like a human
- Think rationally
- Act rationally
1. What are we talking about 2. Success guaranteed? 3. Applications 4. Concerns 5. Beyond the hype
McCarthy et al. A proposal for the Dartmouth summer research project on artificial intelligence, 1955. URL http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html.
https://www.independent.co.uk/news/obituaries/john-mccarthy-computer-scientist-known-as-the-father-of-ai-6255307.html
Russell & Norvig. Artificial Intelligence: A Modern Approach (3rd ed). New Jersey: Prentice Hall, 2010.

Machine learning
7
Branch of AI, term attributed to Arthur Lee Samuel (IBM) in 1959.
Wikipedia (sorry):
Algorithms are developed to perform a specific task without using explicit instructions, relying
on patterns and inference instead.
Shillan et al (2019): form of AI in which a model learns from examples rather then pre-
programmed rules.
History-computer.com
Shillan et al. Crit Care 2019;23:284.

Gaming
8https://www.trendhunter.com/trends/hello-neighbor

Gaming
9https://towardsdatascience.com/using-deep-learning-to-improve-fifa-18-graphics-529ec44ea37e

Speech recognition (e.g. Siri, Bixby)
10https://focalcode.com/blog/10-real-world-examples-of-machine-learning-and-ai-2018-2/

Credit card fraud
11https://www.slideshare.net/dalpozz/adaptive-machine-learning-for-credit-card-fraud-detection

Shoplifting
12https://newatlas.com/vaak-vaakeye-ai-theft-detection/59263/

Non-exhaustive list
13
Gaming
Natural Language Processing (Siri etc)
Fraud detection
Shoplifting
Object recognition (e.g. for driverless cars)
Facial recognition
Traffic analysis and predictions (e.g. Waze app)
Electrical load forecasting
(Social) media and advertising (people you may know, movie suggestions, …)
Spam filtering
Search engines (e.g. Google PageRank)
Handwriting recognition
Also less positive applications, such as ‘deepfake’ adult movies
And… medicine and healthcare

Machine learning in medicine: overview
14Intellspot.com
Input and
output data
Input
data
PREDICTION PATTERNS

Popularity skyrocketing
15Search on https://www.ncbi.nlm.nih.gov/pubmed/ on (performed Oct 18, 2019)

Funding
16

Start-ups
17https://medicalstartups.org/top/ai/

Reason for popularity
18
“Typical machine learning algorithms are highly flexible
So will uncover associations we could not find before
Hence better predictions and management decisions”
→ One of the master keys, with guaranteed success!

Machine learning
(vs traditional statistics):
success guaranteed?
19

AI in medicine vs elsewhere
• AI is everywhere
o You get ads for products you’ve just been searching (e.g. a cruise)
o You get recommendations for movies based on movies that you watched
• If 1/3 of the ads/movies are of interest to me, performance is poor but it ‘worked’
• Consequences in medicine are different: 1/3 decent predictions is unacceptable
20

Traditional Statistics vs Machine Learning
21Breiman. Stat Sci 2001;16:199-231.

A wild oversimplification
22Used visualizations from: Topol, Nat Med 2019;25:44-56; Van Calster et al, Ultrasound Obstet Gynecol 2007;29:496-504; kdnuggets.com.
X1
X2
X3
X4
X5
X6
X7
X8
Y
Classical statistical modeling
(regression)
Flexible algorithms
(machine learning)
Artificial neural networks (incl deep learning),
Support vector machines, Decision trees,
Random forests, boosted tree models, …

23
Shmueli. Keynote talk at 2019 ISBIS conference, Kuala Lumpur; taken from slideshare.net
Bzdok. Nature Methods 2018;15:233-4.
??

24
Traditional regression modeling can be used for prediction, is used for
prediction, and is able to do prediction very well!
Steyerberg. Clinical prediction models (2nd ed). New York: Springer, 2019.
Riley et al. Prognosis Research in healthcare. Oxford: OUP, 2019.

Machine Learning: success guaranteed?
25Christodoulou et al. J Clin Epidemiol 2019;110:12-22.

26Christodoulou et al. J Clin Epidemiol 2019;110:12-22.

Typical applications of
ML in medicine
27

Deep learning for medical images
28Topol. Nat Med 2019;25:44-56.

29
Topol. Nat Med 2019;25:44-56.
Titano et al. Nat Med 2018;24:1337-41; Nam et al. Radiology 2019;290:218-28; Ehteshami Bejnordi et al. JAMA 2017;318:2199-210;
Esteva et al. Nature 2017;542:115-8; De Fauw et al. Nat Med 2018;24:1342-50; Raman et al. Eye 2019;33:97-109.

30Liu et al. Lancet Dig Health 2019;1:e271-97.

Machine Learning for EHR data
31
Rajkomar et al. Npj Digit Med 2018;1:18.
Rose. JAMA Netw Open 2018;1:e181404.

32Rajkomar et al. Npj Digit Med 2018;1:18.

33Goldstein et al. J Am Med Inform Assoc 2017;24:198-208.

Clustering
34Ahlqvist et al. Lancet Diabetes Endocrinol 2018;6:361-9.

Concerns for development,
validation, and
implementation
35

Ten concerns for predictive analytics
36
 1. Poor study design makes choice of algorithm irrelevant
 2. Flexible algorithms are data hungry, but rich datasets have poor quality
 3. There is large heterogeneity between settings and studies
 4. The modeling strategy and reporting is often problematic
 5. The signal-to-noise ratio in medicine is often low
 6. Using a model makes it invalid (a ‘prediction paradox’)
 7. Flexible algorithms are hard to interpret
 8. Algorithms should be available and free from conflict of interest
 9. Regulatory approval does not guarantee that it works well
10. Algorithms can be unfair for subgroups
(we do not even touch upon actual clinical utility)

1. Study design trumps algorithm choice
37
Do you have a clear research question?
Do you have data that help you answer the question?
What is the quality of the data?
Dilbert.com
Riley. Nature 2019;275:27-9.
One simple example: EHR data were not collected for research purposes

Example
38
Uses ANN, Random Forests, Naïve Bayes, and Logistic Regression.
(Logistic regression performed best, AUC 0.92)
Hernandez-Suarez et al. JACC Cardiovasc Interv 2019;12:1328-38.

Example (contd)
39
The model also uses postoperative information.
David J Cohen, MD: “The model can’t be run properly until you know about both the presence and the absence of
those complications, but you don’t know about the absence of a complication until the patient has left the hospital.”
https://www.tctmd.com/news/machine-learning-helps-predict-hospital-mortality-post-tavr-skepticism-abounds

Example 2
40
Predictions based on whether ruler was on image. This
implied that the clinician was concerned about the lesion.
Paper mentioned that people could do it themselves
using smartphone: that is completely different
Esteva et al. Nature 2017;542:115-8.
https://towardsdatascience.com/is-the-medias-reluctance-to-admit-ai-s-weaknesses-putting-us-at-risk-c355728e9028

Example 3
41Winkler et al. JAMA Dermatol 2019; in press.

Example 4
42Khazendar et al. Facts Views Vis ObGyn 2015;7:7-15.

2. Flexible algorithms are data hungry
43http://www.portlandsports.com/hot-dog-eating-champ-kobayashi-hits-psu/

Flexible algorithms are data hungry
44Van der Ploeg et al. BMC Med Res Methodol 2014;14:137.

Flexible algorithms are data hungry
45
https://simplystatistics.org/2017/05/31/deeplearning-vs-leekasso/
Marcus. arXiv 2018; arXiv:1801.00631

3. Large heterogeneity
46

Large heterogeneity in healthcare
47
Riley et al. BMJ 2016;353:i3140.
Davis et al. JAMIA 2017;24:1052-61.
Steyerberg et al. Stat Med 2019;38:4290-309.

Heterogeneity of logistics and processes
48Agniel. BMJ 2018;360:k1479.

Heterogeneity between studies
49
Badgeley et al. arXiv 2018; arXiv:1811.03695v1.
Finlayson et al. Science 2019;363:1287-9.

Many possible hidden variables
50Riley. Nature 2019;572:27-9.

Example
51
Validation in the same hospital Validation in another hospital
Norgeot et al. JAMA Netw Open 2019;2:e190606.

Reliable risk estimates: the Achilles heel?
52
Shah et al. JAMA 2018;320:27-8.
Van Calster et al. BMC Med, in press.
Rose. JAMA Netw Open 2018;1:e181404.

4. Poor modeling and unclear reporting
53
Christodoulou et al. J Clin Epidemiol 2019;110:12-22.
Collins and Moons. Lancet 2019;393:1577-9.

Poor modeling and unclear reporting
54
What was done about missing data? 45% fully unclear, 100% poor or unclear
How were continuous predictors modeled? 20% unclear, 25% categorized
How were hyperparameters tuned? 66% unclear, 19% tuned with information
How was performance validated? 68% unclear or biased approach
Was calibration of risk estimates studied? 79% not at all, HL test common
Prognosis: time horizon often ignored completely
Christodoulou et al. J Clin Epidemiol 2019;110:12-22.

Example
55
“model based on all of the 473 available variables”
Alaa et al. PLoS One 2019;14:e0213653.

No thank you
56Hutson. Science 2019;365:416-7.

5. Signal-to-noise ratio (SNR) is often low
57
High SNR: predictors-outcome relationship is clear and sharp, little affected by unwanted
noise
There is support that flexible algorithms work best with high SNR, not with low SNR.
How can methods that look everywhere be better when you do not know where to look or what you look for?
Hand. Stat Sci 2006;21:1-14.

Algorithm flexibility and SNR
58Makridakis et al. PLoS One 2018;13:e0194889.
Ennis et al. Stat Med 1998;17:2501-8.
Goldstein et al. Stat Med 2017;36:2750-63.

Yet…
59Rajkomar et al. NEJM 2019;380:1347-58.

6. Using a model makes it invalid
60
Peek et al. https://www.bmj.com/content/357/bmj.j2099/rr-0
Challen et al. BMJ Qual Saf 2019;28:231-7.
Coston et al. arXiv 2019; arXiv:1909.00066.
Peek et al (2017): “predictions influencing behaviours that in turn invalidates predictions”

7. Low interpretability
61
??
Deep learning image adapted from: Topol. Nat Med 2019;25:44-56.

8. Model (un)availability and CoI
62
Panch et al. npj Digit Med 2019;2:77.
Van Calster et al. JAMIA 2019; in press.
Boulesteix et al. Biom J 2019;61:1314-28.

Proprietary algorithms and CoI
63
Van Calster et al. JAMA Intern Med 2019;179:731. Janssens. Genes 2019;10:448.
Van Calster et al. JAMIA 2019; in press.
https://www.theguardian.com/politics/2019/oct/11/dominic-cummings-accused-of-conflict-of-interest-over-nhs-fund?CMP=Share_iOSApp_Other.
“Ethically unacceptable to have a business model
that focuses on selling an algorithm”

9. Regulatory approval of AI (FDA, CE,…)
64
® Evolving domain, trying to keep up with reality.
® Approval not easy from academia (non-profit): should be looked at.
® Approval does not mean that it works well or is safe. This is changing (e.g. FDA).
® What with continuously updated algorithms (across time and place)?
® Balance between transparency, commercial interests, IPR, and societal benefit.
Parikh et al. Science 2019;363:810-2.
Schulz et al. Clin Chem 2019;65:10.
https://www.theguardian.com/politics/2019/oct/11/dominic-cummings-accused-of-conflict-of-interest-over-nhs-fund?CMP=Share_iOSApp_Other.

Regulation of AI (FDA, CE, NMPA)
65https://www.fda.gov/media/122535/download

Regulation of AI (FDA, CE, NMPA)
66
Topol. Nat Med 2019;25:44-56.
https://amp.theguardian.com/society/2019/oct/15/councils-using-algorithms-make-welfare-decisions-benefits?__twitter_impression=true
https://www.digitalhealth.net/2019/10/healthcare-apps-safety-standards/

Example: Babylon’s “GP at hand” app
67
https://www.thetimes.co.uk/article/gp-at-hand-app-babylon-bleeds-cash-dgtnldmqk;
https://www.thetimes.co.uk/article/its-hysteria-not-a-heart-attack-gp-app-tells-women-gm2vxbrqk; http://unitelive.org/matt-hancock-gp-at-hand/;
https://www.newstatesman.com/politics/health/2019/07/computer-will-see-you-now?amp&__twitter_impression=true;
Burki. Lancet 2019;394:457-60.

10. Algorithms are easily unfair
68
Rajkomar et al. Ann Intern Med 2018;169:866-72.
Obermeyer et al. Science 2019;366:447-53.

The Gartner hype cycle
70https://www.gartner.com/en/research/methodologies/gartner-hype-cycle

Let’s go to the ‘plateau of productivity’
71

Algorithms are only algorithms, and dumb
72
Chen and Asch. NEJM 2017;376:2507-9.
Beam and Kohane. JAMA 2018;319:1317-8.
Maddox et al. JAMA 2019;321:31-2.
https://www.fatml.org/; http://www.equator-network.org/reporting-guidelines/tripod-statement/

Methodology is key (as always)
73Wiens et al. Nat Med 2019;25:1337-40.

Beyond the hype towards added value
74
METHODOLOGY REPORTING
GIGO DATA QUALITY DATA SIZE
PRIOR KNOWLEDGE
HETEROGENEITY INTERPRETABILITY
TRANSPARENCY AVAILABILITY
FAIRNESS
If methodology is poor, ML will not save you.
If methodology is good, ML may or may not improve predictions
My experience:
- You get funding for cool ideas, not for good methodology
- Universities do not really check whether their researchers do high quality research

Machine learning in medicine: calm down

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