AI in Healthcare
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The document discusses how AI and machine learning can help address challenges in healthcare by analyzing complex medical data. It provides examples of how AI can help with tasks like analyzing medical images to assist radiologists, predicting drug response from scans, and using electronic health records to better understand diseases and patient heterogeneity. The document also acknowledges challenges like the need for large labeled datasets and ensuring interpretability and avoidance of bias.
AI in Healthcare
- 1. AI and Healthcare Paul Agapow Oncology R&D November 2020
- 2. Disclosure • Does not reflect official AZ thought or projects • No conflicts of interest 2
- 3. About me • Have been a: • At • Oncology R&D RWE / ML&AI @AZ • Data Science Institute @ICL • Centre for Infection @HPA(UK) • Universities, industry, government … 3 health informatician, data scientist, bioinformatician, database administrator, epi-informaticist, software dev, data manager, consultant, molecular geneticist, evolutionary scientist, biochemist, immunologist, programmer …
- 4. The problem
- 5. Healthcare, health, disease, human biology are vast and complicated 5 The human body 1 million different types of molecules About 50 trillion cells Of about 200 different types Each cell has 23 pairs of chromosomes These make up 6.4 billion basepairs (positions) Organised into about 18,000 genes (Or maybe more like 40,000 genes) Genetic material elsewhere in the cell
- 6. Disease is an interaction of multiple biological compartments, age, lifestyle, history, exposure, environment previous treatment and chance 17 November 2020Name6
- 7. The data is complicated & diverse 7 Labs, genomics, clinical exams, images, physical measurements, chemical, health records, other ‘omics, observations, medications … 17 November 2020Name
- 8. What are our healthcare problems? 17 November 2020Name8 Gathering information More and better data, monitoring patients, new molecular technologies, imaging, devices, integration of different modalities, EHR records Understanding disease What is a disease, pathophysiological mechanisms, biomarkers, patient subtypes Developing interventions Finding possible targets, candidate molecules, running trials, analysing trials Delivering healthcare Diagnosing patients, predicting outcomes, targeted therapy, resource allocation & optimization
- 9. Is AI the solution?
- 10. Messy data But what is AI / Machine Learning / Data Science? 10 Clear assumptions Explicit models … No model Statistical modelling Machine Learning / AI … a continuum of approaches Few assumptions Other than things we talk about a lot … Clean & controlled data Trained from data
- 11. 17 November 2020Name11 • Complex multi-modal data • Often poor idea of underlying mechanism or model • Messy problems with messy data • Lots of available data (caveat) • Many healthcare questions are classical data questions (classify, optimize, predict) • Healthcare should be data-driven • Great success in other complex domains ML/AI is well suited for healthcare & therapy development
- 12. But what are the pitfalls? 12 Need more (labelled) data And healthcare data needs to be handled carefully May require specialised computation & skills Some problems difficult to adapt to ML Bias & interpretability – data never lies, but what is it telling us?
- 13. How AI could be used
- 14. Radiology & imaging widely used in healthcare 14 • X-rays, CT, MRI, PET, sonograms … • But interpretation is laborious • Scope for human error – 71% of detected lung cancers were retrospectively found on previous scans – 5-9% disagreement between experts – 23% when no clinical information supplied • Not enough radiologists • Not enough time https://www.rsna.org/en/news/2019/ May/uk-radiology-shortage
- 15. Ai is good at recognising things in images 15 • Lots of prior art • Lots of data to train models from • “AI radiologist” – would be more consistent – faster – could double-check or triage • But there’s more …
- 16. Baseline scan Sequential scans • Can we define novel efficacy endpoints? i.e. identify quantitative changes in the image that predict overall survival more robustly than conventional endpoints (e.g. RECIST) Radiomic analysis of medical images Specific scientific questions to address: • Can we predict response to specific drugs from the baseline scan? i.e. duration of PFS or OS • Can we get insight into toxicity? i.e. improved prediction, diagnosis or understanding of AEs such as ILD • Can the scans provide other insights? e.g. tumour genetics, e.g. therapy resistance, e.g. POM biomarkers? • Can we effectively combine radiomic insights with other clinical data in order to accelerate and improve patient stratification algorithms? Radiomics is the science of extracting quantitative features from medical images to measure shape, intensity, density, texture, etc. The analysis of these ‘radiomic features’ can reveal disease characteristics that are not readily appreciated by the naked eye.
- 17. AI for PD-L1 scoring in Urothelial Carcinoma Deep learning can automatically score PD-L1 expression in Tumour cells and Immune cells Slide stained for PD-L1 expression Cells that were automatically detected using AI
- 18. • It costs ~ $1-2B and 10 years to develop & launch a drug • Each patient in a clinical trial costs $1-10K • The “valley of death”: most candidate drugs will fail • Post-approval adds to the costs • Eroom’s Law 18 The tough maths of drug development ePharmacology.hubpages.com
- 19. AstraZeneca generates and has access to more data than ever before. Target ID Target Validation Discovery Pre-Clinical Clinical Commercial Post Marketing Surveillance Genetic & Genomic Data Patient-Centric Data Sensors & Smart Devices Interactive Media Healthcare Information network Market Data
- 20. “AI will not replace drug hunters, but drug hunters who don’t use AI will be replaced by those who do.” -Andrew Hopkins, CEO Exscientia 17Name20
- 21. AI for drug candidate selection & prioritization 21 https://www.biopharma-excellence.com/news/2019/6/30/artificial-intelligence-a-revolution-in- biopharmaceutical-development
- 22. • Similar patient presentation can mask vastly different molecular machinery • Even within a “homogenous” condition, patients will have different outcomes • What are the treatment effects for individual patients? Understanding these leads to: • More effective trials • More effective treatment • Insights on pathophysiology 22 Patients are heterogenous Heterogeneity in lesion change in colorectal cancer Nikodemiou et al. (2020)
- 23. AI enabled mining of electronic health records to better understand diseases COPD T2D ▪ Transform patients into sequences of diagnosis codes ▪ Look for over-represented temporal pairs of codes ▪ Collapse pairs into trajectories of diagnoses ▪ Combine similar trajectories with graph similarity Brunak et al. Nature Coms. 2016 Topology based Patient-Patient network, identify distinct subtypes of T2D Dudley et al. Sci. transl. Med, 2015
- 24. Data driven KOL identification and site selection 24 Network Analysis Federated EHRs Real Time I/E analysis of Trial protocol Patient referral network of oncologists & surgeons treating NSCLC based on claims data. Color represents physician grouping. Size of bubble represents physician PageRank. • Claims data is used to map physician networks based on patient referrals • Network analytics such as PageRank algorithm are used to determine which physicians are most important in the network • Network connections are used to map existing relationships between oncologists & surgeons
- 25. Building a external control arm from Real World Data 25 Patients with unmet medical need Single-arm trial Inclusion / exclusion criteria Matched patients on standard of care can be compared to new treatment Access to New Medicine Patients from historical trials / RWE data Inclusion / exclusion criteria Apply Propensity Score Matching Matching requires Deep data not just Big Data
- 26. A lot of knowledge is associative or relational – FOAF Knowledge graphs can help us capture and explore these 17 November 2020Name26
- 27. A lot of healthcare surrounds logistics, supply & demand AI can solve this 17 November 2020Name27 https://www.digitalcommerce360.com/2019/09/06/use-artificial- intelligence-to-transform-the-hospital-supply-chain/
- 29. Therapy development costs continue to increase • Eroom’s Law: cost of developing new drug roughly doubles every nine years • Acceleration of biomedical research not reflected in drug development • Recent uptick in approvals does not reflect decreasing costs 29 Pharmacelera (2014)
- 30. How do we know what a system is doing? 30 • Interpretability is non-negotiable – Biased data can give rise to biased models – And a model may not be doing what we think it is – AI models can only be built for data that you have – Validation is critical • And they need a lot of data
- 31. • Labelled data is the new oil • Unfortunately • Data coverage is sparse • Data is weird • And also WEIRD • Diverse data (more and unexploited information) • Governance & privacy issues • More data from: • Real-time and intimate integration with EHRs • Devices • Federated networks • Collaborate with national centres, long-term funding & broad collaborations 31 Where does the data come from? Reddy (2020)
- 32. • Data Science & AI have the potential to transform the way we identify and develop medicines • Life Science companies have made large investments in building DS&AI capabilities • If you are driven by science and passioned about improving lives then, I’d strongly recommend you seek an opportunity in R&D (AstraZeneca maybe …) Example jobs at AstraZeneca – 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 32 Final thoughts
- 33. Confidentiality Notice This file is private and may contain confidential and proprietary information. If you have received this file in error, please notify us and remove it from your system and note that you must not copy, distribute or take any action in reliance on it. Any unauthorized use or disclosure of the contents of this file is not permitted and may be unlawful. AstraZeneca PLC, 1 Francis Crick Avenue, Cambridge Biomedical Campus, Cambridge, CB2 0AA, UK, T: +44(0)203 749 5000, www.astrazeneca.com 33