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.
Patient centricity and digital solutionsAhmed Graouch
Beyond product offerings, it also positions Medtech companies to help hospitals and health systems transition to the future of health through services.
The term “digital twin” refers to the digital version of a physical device or process. By bridging the physical and the virtual worlds, data is transmitted seamlessly allowing the virtual entity to exist simultaneously with the physical device or process. Digital twins are emerging as virtual test beds for
possible solutions before they implement physical devices. These computer-based models are fed individual and population data and mimic the electrical and physical properties of an object.
Medical device companies are using this technology to simulate how their devices are being used in the
clinical setting.
In our view of the future of health, radically interoperable data is likely to play a huge role in transforming health care. Data from medical technologies such as wearables, remote monitors, and
sensors will be standardized, stored, updated, and aggregated with other sources of information such as social media platforms, retailers, and electronic health records.
The combined data will create a complete personal profile that physicians and health systems can use to help ensure that
I deliver health services in an appropriate fashion.
The health emergency underway worldwide has highlighted the need to strengthen the surveillance and care of the sick at home, to avoid hospital overcrowding that we have seen in recent months inevitably compromise the management of the emergency itself. X-RAIS is an AI tool, which as a third eye supports radiologists during the reporting phase of radiological images. Within this context, we extended X-RAIS capabilities with ALFABETO (ALl FAster BEtter TOgether).
apidays LIVE India - The digitisation of healthcare by Dr S.S. Lal, Global Fo...apidays
apidays LIVE India 2021 - Connecting 1.3 billion digital innovators
May 20, 2021
The digitisation of healthcare
Dr S.S. Lal, President of Global Foundation for Health and Hygiene
Our Journey to Release a Patient-Centric AI App to Reduce Public Health CostsDatabricks
Health costs are exploding year by year. Thanks to Artificial Intelligence it is possible to address patient needs in a cost-efficient manner.
In the case we will present, we will demonstrate how as part of a telemedicine service we implemented a solution allowing to reduce triage cost of patients by leveraging AI. The app we developed not only allowed to reduce cost but is significantly improving the patient experience.
The Hive Think Tank: Unpacking AI for Healthcare The Hive
In this The Hive Think Tank talk, Ash Damle, CEO of Lumiata takes a deep dive into Lumiata’s core technological engine - the Lumiata Medical Graph, which applies graph-based machine learning to compute the complex relationships between health data in the same way that a physician would, and how this medical AI engine powers personalization and automation within risk and care management.
Healthcare is changing rapidly. It is clear that humans need mechanisms to automate some parts of data processing and help humans in decision making. This talk will concentrate on how to improve the machine understanding of unstructured data.
Patient centricity and digital solutionsAhmed Graouch
Beyond product offerings, it also positions Medtech companies to help hospitals and health systems transition to the future of health through services.
The term “digital twin” refers to the digital version of a physical device or process. By bridging the physical and the virtual worlds, data is transmitted seamlessly allowing the virtual entity to exist simultaneously with the physical device or process. Digital twins are emerging as virtual test beds for
possible solutions before they implement physical devices. These computer-based models are fed individual and population data and mimic the electrical and physical properties of an object.
Medical device companies are using this technology to simulate how their devices are being used in the
clinical setting.
In our view of the future of health, radically interoperable data is likely to play a huge role in transforming health care. Data from medical technologies such as wearables, remote monitors, and
sensors will be standardized, stored, updated, and aggregated with other sources of information such as social media platforms, retailers, and electronic health records.
The combined data will create a complete personal profile that physicians and health systems can use to help ensure that
I deliver health services in an appropriate fashion.
The health emergency underway worldwide has highlighted the need to strengthen the surveillance and care of the sick at home, to avoid hospital overcrowding that we have seen in recent months inevitably compromise the management of the emergency itself. X-RAIS is an AI tool, which as a third eye supports radiologists during the reporting phase of radiological images. Within this context, we extended X-RAIS capabilities with ALFABETO (ALl FAster BEtter TOgether).
apidays LIVE India - The digitisation of healthcare by Dr S.S. Lal, Global Fo...apidays
apidays LIVE India 2021 - Connecting 1.3 billion digital innovators
May 20, 2021
The digitisation of healthcare
Dr S.S. Lal, President of Global Foundation for Health and Hygiene
Our Journey to Release a Patient-Centric AI App to Reduce Public Health CostsDatabricks
Health costs are exploding year by year. Thanks to Artificial Intelligence it is possible to address patient needs in a cost-efficient manner.
In the case we will present, we will demonstrate how as part of a telemedicine service we implemented a solution allowing to reduce triage cost of patients by leveraging AI. The app we developed not only allowed to reduce cost but is significantly improving the patient experience.
The Hive Think Tank: Unpacking AI for Healthcare The Hive
In this The Hive Think Tank talk, Ash Damle, CEO of Lumiata takes a deep dive into Lumiata’s core technological engine - the Lumiata Medical Graph, which applies graph-based machine learning to compute the complex relationships between health data in the same way that a physician would, and how this medical AI engine powers personalization and automation within risk and care management.
Healthcare is changing rapidly. It is clear that humans need mechanisms to automate some parts of data processing and help humans in decision making. This talk will concentrate on how to improve the machine understanding of unstructured data.
Overview of the Challenges & Opportunities within Healthcare Information Technology amid the 2009 Healthcare Reforms. Cost savings, business models and medical technology and software solutions are described.
When it comes to AI use for prediction, diagnosis and treatment of medical conditions, reality is often replaced with a hype. Limitations should be known. A review of AI failures and challenges in healthcare showing why it is not likely for algorithms to replace physicians in the nearest future.
This presentation discuss major applications of AI in Healthcare including medical diagnostics, personalized treatments and optimizing US healthcare system. This presentation also discuss some of the challenges of implementing AI in healthcare.
Presentation covers basics of Big Data & its potential uses in healthcare. Data is growing & moving faster day by day. Getting access to this valuable data & factoring it into clinical & advanced analytics is critical to improve care. So there must be analysis of big data to make effective decisions.
AI in Healthcare: From Hype to Impact (updated)Mei Chen, PhD
The primary goal of this workshop is to help health professionals gain a critical understanding of the various types of AI technologies available so they can make wise decisions and invest AI for healthcare improvement.
The use of artificial intelligence in healthcare has the potential to assist healthcare providers in many aspects of patient care and administrative processes as well as improve patient outcomes.
AI analyzes data throughout a healthcare system to mine, automate and predict processes. Some of the use cases are :
1. Early Diagnosis of diseases
2. Improved clinical trial processes
3. Mental health apps etc.
AI and VR in Health: What's Now, What's NextEnspektos, LLC
Data Source: www.digihealthinformer.com
Fard Johnmar's presentation from the future::present digital health breakfast. Research focuses on the evolution of the health artificial intelligence and virtual reality markets. Key areas of focus include what diseases these technologies are being used to manage, which organizations are driving their uptake, key investment activity and more.
Artificial Intelligence in Medical Imaging: An Analysis of Funding for Start-upsSimon Harris
There are over 50 start-up companies developing artificial intelligence solutions for medical imaging. Combined, these companies have raised over $100 million in funding. This short report from Signify Research shows the trends in capital funding for these companies and highlights how funding breaks down by company, by region and by clinical application.
Healthcare AI will undoubtedly become one of the fastest growing industries in the industry. Although the medical and health artificial intelligence industry was valued at US$ 600 million in 2014 , it is expected to reach a staggering US$ 150 billion by 2026. There are countless AI applications in the healthcare industry, let’s look at some outstanding ones.
The Life-Changing Impact of AI in HealthcareKalin Hitrov
For IT Leaders in the healthcare and pharmaceutical industries looking to understand the impact of AI on their industries and how to overcome the ethical and efficiency challenges that come with its use.
AI in Healthcare | Future of Smart Hospitals Renee Yao
In this talk, I specifically talk about how NVIDIA healthcare AI software and hardware were used to support healthcare AI startups' innovation. Three startups featured: Caption Health, Artisight, and Hyperfine. Audience: healthcare systems CXOs.
Artificial intelligence (AI) is already transforming healthcare. It's an invaluable tool, capable of storing and processing vast amounts of data almost simultaneously. AI allows for rapid and accurate diagnosis, early detection, advanced research and much more.
Overview of the Challenges & Opportunities within Healthcare Information Technology amid the 2009 Healthcare Reforms. Cost savings, business models and medical technology and software solutions are described.
When it comes to AI use for prediction, diagnosis and treatment of medical conditions, reality is often replaced with a hype. Limitations should be known. A review of AI failures and challenges in healthcare showing why it is not likely for algorithms to replace physicians in the nearest future.
This presentation discuss major applications of AI in Healthcare including medical diagnostics, personalized treatments and optimizing US healthcare system. This presentation also discuss some of the challenges of implementing AI in healthcare.
Presentation covers basics of Big Data & its potential uses in healthcare. Data is growing & moving faster day by day. Getting access to this valuable data & factoring it into clinical & advanced analytics is critical to improve care. So there must be analysis of big data to make effective decisions.
AI in Healthcare: From Hype to Impact (updated)Mei Chen, PhD
The primary goal of this workshop is to help health professionals gain a critical understanding of the various types of AI technologies available so they can make wise decisions and invest AI for healthcare improvement.
The use of artificial intelligence in healthcare has the potential to assist healthcare providers in many aspects of patient care and administrative processes as well as improve patient outcomes.
AI analyzes data throughout a healthcare system to mine, automate and predict processes. Some of the use cases are :
1. Early Diagnosis of diseases
2. Improved clinical trial processes
3. Mental health apps etc.
AI and VR in Health: What's Now, What's NextEnspektos, LLC
Data Source: www.digihealthinformer.com
Fard Johnmar's presentation from the future::present digital health breakfast. Research focuses on the evolution of the health artificial intelligence and virtual reality markets. Key areas of focus include what diseases these technologies are being used to manage, which organizations are driving their uptake, key investment activity and more.
Artificial Intelligence in Medical Imaging: An Analysis of Funding for Start-upsSimon Harris
There are over 50 start-up companies developing artificial intelligence solutions for medical imaging. Combined, these companies have raised over $100 million in funding. This short report from Signify Research shows the trends in capital funding for these companies and highlights how funding breaks down by company, by region and by clinical application.
Healthcare AI will undoubtedly become one of the fastest growing industries in the industry. Although the medical and health artificial intelligence industry was valued at US$ 600 million in 2014 , it is expected to reach a staggering US$ 150 billion by 2026. There are countless AI applications in the healthcare industry, let’s look at some outstanding ones.
The Life-Changing Impact of AI in HealthcareKalin Hitrov
For IT Leaders in the healthcare and pharmaceutical industries looking to understand the impact of AI on their industries and how to overcome the ethical and efficiency challenges that come with its use.
AI in Healthcare | Future of Smart Hospitals Renee Yao
In this talk, I specifically talk about how NVIDIA healthcare AI software and hardware were used to support healthcare AI startups' innovation. Three startups featured: Caption Health, Artisight, and Hyperfine. Audience: healthcare systems CXOs.
Artificial intelligence (AI) is already transforming healthcare. It's an invaluable tool, capable of storing and processing vast amounts of data almost simultaneously. AI allows for rapid and accurate diagnosis, early detection, advanced research and much more.
CORD Rare Drug Conference, June 8 - 9, 2022
Opportunities and Challenges for Data Management Real-World Data and Real-World Evidence
• Patient support programs: Sandra Anderson, Innomar Strategies
• AI for Data Management and Enhancement: Aaron Leibtag, Pentavere
• Patient Support and RWE: Laurie Lambert, CADTH
Machine learning, health data & the limits of knowledgePaul Agapow
Lecture for Imperial College London's MSc in Health Data Analytics, critiquing a recent paper on COVID diagnosis and moving out to talk about good practices (& limits) in ML and model building
Big Data Provides Opportunities, Challenges and a Better Future in Health and...Cirdan
This presentation was delivered by Ashraf Mina, NSW Pathology at the Pathology Horizons 2017 Conference in Cairns, Australia.
Pathology Horizons 2017 is an annual CPD conference organised by Cirdan on the future of pathology. You can access more information about the event at www.pathologyhorizons.com
The company was founded in 2010 and is headquartered in Lisburn, Northern Ireland and has additional offices in Canada and Australia.
Cirdan is also responsible for organising Pathology Horizons, an annual and open CPD conference on the future of pathology. For more information visit - www.pathologyhorizons.com
Big Data and its Impact on Industry (Example of the Pharmaceutical Industry)Hellmuth Broda
While we bemoan the ever increasing data tsunami new technologies allow to harvest the gold nuggets in the hay stack.
Using the example of the Pharmaceutical Industry some of the possible business uses for Big Data Analitics are outlined.
Krishnaprasad Thirunarayan and Amit Sheth: Semantics-empowered Approaches to Big Data Processing for Physical-Cyber-Social Applications, In: Proceedings of AAAI 2013 Fall Symposium on Semantics for Big Data, Arlington, Virginia, November 15-17, 2013.
With the rapid proliferation of mobile phones, social media, and sensors, it is critical to collect and convert big data so generated into actionable information that is relevant for decision making. In this session, we explore challenges and approaches for synthesizing relevant background knowledge and inferences that can enable smart healthcare and ultimately benefit community at large.
Paper: http://www.knoesis.org/library/resource.php?id=1903
Talk entitled "from the Virtual Human to a Digital Me" presented at the Virtual Physiological Human 2012 Conference held at IET Savoy, Savoy Place, London, 18-20 September 2012.
The Learning Health System: Thinking and Acting Across ScalesPhilip Payne
A Learning Health System (LHS) can be defined as an environment in which knowledge generation processes are embedded into daily clinical practice in order to continually improve the quality, safety, and outcomes of healthcare delivery. While still largely an aspirational goal, the promise of the LHS is a future in which every patient encounter is an opportunity to learn and improve that patient’s care, as well as the care their family and broader community receives. The foundation for building such an LHS can and should be the Electronic Health Record (EHR), which provides the basis for the comprehensive instrumentation and measurement of clinical phenotypes, as well as a means of delivering new evidence at the patient- and population levels. In this presentation, we will explore the ways in which such EHR-derived phenotypes can be combined with complementary data across a spectrum from biomolecules to population level trends, to both generate insights and deliver such knowledge in the right time, place, and format, ultimately improving clinical outcomes and value.
Data Science Deep Roots in Healthcare IndustryDinesh V
Data Science transforms the healthcare industry with impeccable solutions that can improve patient care through EHRs, medical imaging, drug discovery, predictive medicines and genetics and genomics.
Presentation that gives an overview of the impact of IT on radiology, including the growing role of biomarkers and artificial intelligence and deep learning on the (future) radiology profession. The shift to precision medicine and personalized care are explained, the reasons for a re-definition of radiology are addressed.
Disrupting the Oncology Care Continuum through AI and Advanced AnalyticsMichael Peters
Having Presented at #SROA18 on the need to move from basic Data and Reporting to Advanced Analytics and Artificial Intelligence, I thought I would share my deck for all.
Can drug repurposing be saved with AI 202405.pdfPaul Agapow
Presented at DigiTechPharma, London May 2024.
What is drug repurposing. Why is it needed? What systematic approaches are there? Is AI a solution? Why not?
IA, la clave de la genomica (May 2024).pdfPaul Agapow
A.k.a. AI, the key to genomics. Presented at 1er Congreso Español de Medicina Genómica. Spanish language.
On the failure of applied genomics. On the complexity of genomics, biology, medicine. The need for AI. Barriers.
Digital Biomarkers, a (too) brief introduction.pdfPaul Agapow
Presentation at the Artid workshop, U. Bristol, March 2024, on digital biomarkers for improved clinical trials and monitoring of complex diseases, including neurological & movement disorders.
Journal club and talk given to Health Data Analytics MSc, February 2023. Reflecting on how to do good machine learning over biomedical data, the pitfalls and good practices
Where AI will (and won't) revolutionize biomedicinePaul Agapow
Presented AI & Big Data Expo, London, December 2022.
Given the hype and success of machine learning and AI in other fields, its application in healthcare is only natural.
- However, the actual successes in medicine have been limited, with a number of high-profile failures.
- Here, I propose that biology is uniquely complex, with our lack of domain knowledge limiting the application of AI.
- However, there is reason for cautious optimism, with AI-lead approaches shifting the odds in our favour.
Analysing biomedical data (ers october 2017)Paul Agapow
Presented at European Respiratory Society, Berlin, October 2017. High level talk to mix of clinicians and scientists on the difficulties of biomedical analysis, including practical, statistical and data issues.
Interpreting transcriptomics (ers berlin 2017)Paul Agapow
Presented at European Respiratory Society, Berlin, October 2017. High level talk to mix of clinicians and scientists on analyzing transcriptomic / gene expression data
These simplified slides by Dr. Sidra Arshad present an overview of the non-respiratory functions of the respiratory tract.
Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
Study Resources:
1. Chapter 39, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 17, Human Physiology by Lauralee Sherwood, 9th edition
4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
Knee anatomy and clinical tests 2024.pdfvimalpl1234
This includes all relevant anatomy and clinical tests compiled from standard textbooks, Campbell,netter etc..It is comprehensive and best suited for orthopaedicians and orthopaedic residents.
New Drug Discovery and Development .....NEHA GUPTA
The "New Drug Discovery and Development" process involves the identification, design, testing, and manufacturing of novel pharmaceutical compounds with the aim of introducing new and improved treatments for various medical conditions. This comprehensive endeavor encompasses various stages, including target identification, preclinical studies, clinical trials, regulatory approval, and post-market surveillance. It involves multidisciplinary collaboration among scientists, researchers, clinicians, regulatory experts, and pharmaceutical companies to bring innovative therapies to market and address unmet medical needs.
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
ASA GUIDELINE
NYSORA Guideline
2 Case Reports of Gastric Ultrasound
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
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Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
These lecture slides, by Dr Sidra Arshad, offer a quick overview of the physiological basis of a normal electrocardiogram.
Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar lead (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
6. Describe the flow of current around the heart during the cardiac cycle
7. Discuss the placement and polarity of the leads of electrocardiograph
8. Describe the normal electrocardiograms recorded from the limb leads and explain the physiological basis of the different records that are obtained
9. Define mean electrical vector (axis) of the heart and give the normal range
10. Define the mean QRS vector
11. Describe the axes of leads (hexagonal reference system)
12. Comprehend the vectorial analysis of the normal ECG
13. Determine the mean electrical axis of the ventricular QRS and appreciate the mean axis deviation
14. Explain the concepts of current of injury, J point, and their significance
Study Resources:
1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. Chapter 3, Cardiology Explained, https://www.ncbi.nlm.nih.gov/books/NBK2214/
7. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
The Gram stain is a fundamental technique in microbiology used to classify bacteria based on their cell wall structure. It provides a quick and simple method to distinguish between Gram-positive and Gram-negative bacteria, which have different susceptibilities to antibiotics
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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 …
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
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?
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
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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
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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
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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
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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
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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
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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
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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
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Final thoughts
33. Confidentiality Notice
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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
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