The document discusses the challenges of using real-world evidence (RWE) and machine learning (ML) in healthcare analysis, emphasizing the complexities and biases inherent in health data. It argues that while ML may be a beneficial tool for analyzing RWE, it introduces issues related to data bias, validation, and interpretability. The author suggests that the focus in healthcare analytics should center more on biological insights rather than purely analytical methods.

1. Machine learning,
health data & the limits
of knowledge
Paul Agapow
ONC R&D ML&AI AstraZeneca
<paul.agapow@astrazeneca.com>
20201/3/10

2. 2
Disclosure
• Does not reflect official AZ thought or projects
• No conflicts of interest

3. 3
About me
• Have been a:
• At
• Oncology R&D ML&AI / RWE @AZ
• Data Science Institute @ICL
• Centre for Infection @HPA (UK)
• Universities, industry, government …
health informatician, data scientist, bioinformatician, database
administrator, epi-informaticist, software dev, data manager,
consultant, molecular geneticist, data scientist, evolutionary
scientist, biochemist, phylogeneticist, immunologist, programmer …

4. Using this paper as a jumping-off point
• The Hierarchical Classifier for COVID-19
Resistance Evaluation (2021) Shakhovska,
Izonin & Melnykova, Data v6:6
• https://doi.org/10.3390/data6010006
• https://www.mdpi.com/2306-
5729/6/1/6/htm
• How to analyse for patterns in COVID data
when the observational data is diverse &
complex
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5. Data is a
saviour & a
curse
• Data & analytics has saved us several times in the current
crisis
• But too much data can create problems
• And data is not information
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6. RWE: real world evidence
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• Electronic Health Records
• Registries
• Claims databases
• Repurposed trial data
• Defined:
• Anything that isn’t an RCT
(randomised controlled trial)
• Observational data
• Anything we have to consider the
context & sourcing of?
• Why?
• Cheap
• Ethical
• Accesses scales & types of data &
situations that are otherwise
unavailable

7. • Where was it collected?
• Who did they look for?
• What are those peoples
habits and histories?
But all (RWE) data is biased
What population does it
come from?
• “severe asthma” or
“PDL1 expression”
• What are the diagnostic
devices?
• What’s common medical
practice there?
What are the definitions
used?
• E.g. surveys, visits
• Are inclusion / exclusion
at random?
• What incidental
correlations?
• Choice of features
What causes data to be
included / excluded?
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8. The COVID publication: is it good data?
• Do we know where it came from?
• Do we know who is in it?
• Is there missing data?
• “maybe” COVID?
• Are the populations comparable?
• Are antibody levels comparable?
• Different test kits?
• Imbalanced classes?
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The data

9. How do we analyse RWE correctly?
• Patients are complex:
• Co-morbidities
• Lifestyle, prior history, exposure
• Demographics, genetics, epigenome, microbiome …
• Disease is complex:
• Affects different body subsystems
• Health data is complex:
• Sparse, irregular
• A product of a healthcare system …
• Underlying models unclear
• Many opportunities for confounders & noise
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10. 10
Is ML the best approach for RWE analysis?
Messy data
Clear
assumptions
Explicit
models … No model
Statistical modelling Machine Learning / AI
…
a continuum of approaches
Few
assumptions
Clean &
controlled data
Trained from
data
Larger data

11. But what are the pitfalls of using ML on health data?
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• Need more (labelled) data
• Bias – how was the data
sourced?
• Needs to be handled carefully
• May require specialised
computation & skills
• Some problems difficult to
adapt to ML
• Interpretability – data never
lies, but what is it telling us?

12. Clustering: how simple algorithms can
actually be very complex
• Idea of clustering is simple: but what does it actually do?
• Every dataset has clusters, even random noise
• Do clusters reflect the underlying reality?
• Are the clusters revealed valid and/or robust?
• Are the clusters of groups you are interested in?
• A cluster is the truth, it’s a hypothesis
(The paper is modestly convincing about these points)
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13. The COVID publication: is it good methodology?
• Many different methods but:
• What’s the concordance?
• What use is 6-7 methods?
• Ensemble them?
• Where’s the validation?
• What’s the question?
• How many people are actually
infected with COVID? or
• Can we build a model to calculate
this?
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The data

14. What makes a good machine-learning approach?
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• Be clear what it is predicting
• It should be reproducible
• It should be validated:
• Internally: performance, convergence, loss,
sensitivity, robust, …
• Externally: against another dataset
• Almost any ML method can
• Do (slightly) better than humans
• Get better than 50%
• If it is “better”, compared to what?
How do the
systems in the
paper measure
up?

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How do we know what a system is doing?
• Interpretability is non-negotiable
• AI models can only be built for data that
you have
• Biased data gives rise to biased models
• A model may not be doing what we
think it is
• Toolkits like Shap & Lime make
interpretability easy and comparable
(Paper used very interpretable systems)

16. How could this have been done better?
• What question are we trying to solve?
• “What’s the actual level of infected people in the population”?
• In what time period or setting?
• What’s actionable?
• What data can we get?
• What data can we get for validation?
• We don’t need 6-7 different methods, just 1 good one
• Be clear about “how good” the results are
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17. Summary
• RWE may be a broad and over-reaching category
• But it underlines the complexity & biases of health data
• ML may be the best approach for analysing RWE
• However its power and flexibility introduces other problems
• Data “bias”
• Validation
• Interpretability
• ML “findings” are almost always just hypotheses
• Healthcare analytics should not be about analytics but about biology
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18. Final thought
• If you are driven by science and passionate about improving lives, why not work at
AstraZeneca?
• Example jobs – please visit our careers website
• Principal Data Scientist - https://careers.astrazeneca.com/job/gaithersburg/principal-
data-scientist/7684/14833674
• Associate Director Imaging & AI - Imaging & Data Analytics -
https://careers.astrazeneca.com/job/gothenburg/associate-director-imaging-and-ai-
imaging-and-data-analytics/7684/14469379
• Data Sciences & AI Graduate Programme – UK -
https://careers.astrazeneca.com/data-sciences-and-ai-graduate-programme
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