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![• Deep-learning AI:
• Image recognition (e.g., Facebook’s facial
recognition system [Menlo Park, California],
• self-driving cars,
• speech recognition (e.g., Apple’s Siri
[Cupertino, California],
• Google Brain [Mountain View, California],
• Amazon’s Alexa Voice [Seattle,
Washington]),
• Google DeepMind AlphaGo, mobile apps
(e.g., Cardiogram app [San Francisco,
California]),
• machine vision software in cameras,
• IBM Watson (North Castle, New York), and
• robots.](https://image.slidesharecdn.com/bigdata-181018130839/75/Big-Data-and-Artificial-Intelligence-15-2048.jpg)
Uploaded byKamarul Imran
Big Data and Artificial Intelligence
The document discusses the potential collaboration on big data and artificial intelligence, emphasizing the impact of big data on public health and the challenges associated with data processing and analysis. It outlines key concepts related to artificial intelligence, machine learning, and deep learning, as well as various applications in healthcare such as cancer detection and patient monitoring. Additionally, it highlights the importance of interdisciplinary collaboration among experts in fields like epidemiology, computer science, and data engineering to leverage the opportunities presented by these technologies.
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Big Data and Artificial Intelligence
- 1. Potential Collaboration onBig Data and Artificial Intelligence Kamarul Imran Musa MD M Community Med PhD Associate Professor (Epidemiology and Statistics) Department of Community Medicine [email protected]
- 2. • Big data •IR 4.0 • Artificial intelligence • Machine learning • Examples of projects This Photo by Unknown Author is licensed under CC BY
- 3. What is BigData • Big data is a term for data sets that are • large or • complex • Main feature: • Traditional data processing application software is inadequate to deal • Data size – wont fit a local machine memory https://en.wikipedia.org/wiki/Big_data https://upload.wikimedia.org/wikipedia/commons/d/dd/Big_%26_Small_Pu mkins.JPG 3
- 4. The rise ofbig data • The huge data -> Big Data • Big Data -> direct and indirect effects on the public health (Khoury and Ioannidis 2014). 4 This Photo by Unknown Author is licensed under CC BY
- 5. Challenges in BigData • Data capture • Sources, format, connection • Data storage • Transport storage, size, scalability • Data analysis, search, sharing, transfer • Massive size, time, memory, processing power • Visualization, querying, updating • Massive observations, languages, real-time data • Information privacy. • Personal identifier, standards, cloud storage https://3.bp.blogspot.com/-u_Yi8z1_yJ8/WBtQazL681I/AAAAAAAAEHU/-K2q1HexnO0My0UcqOIEQjynBpexkB- DgCLcB/s400/8233546_orig.gif http://exhibitsalive.com/modular/IpoANI.gif 5
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- 7. Industrial revolution 4.0 •The Fourth Industrial Revolution (IR 4.0) that has been occurring since the middle of the last century • is characterized by a fusion of technologies: • physical, • digital, and • biological spheres • (National Academies of Sciences, Medicine et al. 2017). 7This Photo by Unknown Author is licensed under CC BY-SA
- 8. The 4th IndustrialRevolution • Cyber-physical systems • Internet of Things • Internet of Systems 8
- 9. Artificial intelligence • Whatis 'Artificial Intelligence - AI' • Artificial intelligence (AI) = simulated intelligence in machines. • These machines are programmed to "think" like a human • mimic the way a person acts. • The ideal characteristic of artificial intelligence • ability to rationalize and take actions that have the best chance of achieving a specific goal • ** although the term can be applied to any machine that exhibits traits associated with a human mind, such as learning and solving problems. https://www.investopedia.com/terms/a/artificial-intelligence-ai.asp
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- 13. Deep learning • Deeplearning = deep structured learning = hierarchical learning • part of a broader family of machine learning methods https://en.wikipedia.org/wiki/Deep_learning https://medium.com/swlh/ill-tell-you-why-deep-learning-is-so-popular-and-in-demand- 5aca72628780
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- 15. • Deep-learning AI: •Image recognition (e.g., Facebook’s facial recognition system [Menlo Park, California], • self-driving cars, • speech recognition (e.g., Apple’s Siri [Cupertino, California], • Google Brain [Mountain View, California], • Amazon’s Alexa Voice [Seattle, Washington]), • Google DeepMind AlphaGo, mobile apps (e.g., Cardiogram app [San Francisco, California]), • machine vision software in cameras, • IBM Watson (North Castle, New York), and • robots.
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- 21. Examples of projects •Big Data • Big data projects: • Fields: • “omics” data, human • gut microbiome sequencing, • social media, and • cardiac imaging • Too large and heterogeneous, and change too quickly, to be stored, analyzed, and used.
- 22. https://www.genome.gov/images/content/dna_sequencing.jpg (a) Heat mapwith dendogram plot, and (b) MDS plot, showing the transcriptional correlation. (c-f) scatter and bar plots showing the genes up-regulated (blue color and labeled "1"), down-regulated (red color and labeled "-1") or non- differentially expressed
- 23. • At theUniversity of Pittsburgh • link the molecular signatures of people with breast cancer to a host of clinical data, including demographic information associated with risk such as age, ethnicity and body weight. • 5 petabytes, or 5 million gigabytes, which is enough data to overload around 40,000 new iPhone 6 devices. • Public databases • TCGA and METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) • contain data on the entire set of genes, RNA transcripts and proteins of thousands of breast-cancer tumours
- 25. Big data, showingcorrelation between a CDC study on cardiovascular disease and a study conducted based on hostility in Twitter tweets. This demonstrates how big data from social media might be used to in new ways to evaluate population health. https://www.dicardiology.com/article/understanding-how-big-data-will-change-healthcare
- 26. • Satellite imageryhas the power to capture high- resolution, real-time data • providing more frequent and higher resolution information about girls’ and women’s lives • reveal pockets of gender inequalities that are typically masked by averages on the country or district level.
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- 29. • AI techniquesin cardiovascular medicine: • to explore novel genotypes and phenotypes in existing diseases • improve the quality of patient care • enable cost-effectiveness, • reduce readmission and mortality rates.
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- 31. • “ …CAD system based on one of the most successful object detection frameworks, Faster R-CNN. The system detects and classifies malignant or benign lesions on a mammogram without any human intervention. The proposed method sets the state of the art classification performance on the public INbreast database, AUC = 0.95.”
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- 33. • Deep learningarchitecture • To a clinically heterogeneous set of three-dimensional optical coherence tomography scans • Demonstrate performance in making a referral recommendation that reaches or exceeds that of experts on a range of sight-threatening retinal diseases after training on only 14,884 scans.
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- 35. • Detecting lungnodules using deep learning. Tim Salimans, Open AI. • “Lung cancer is the leading cause of cancer-related death worldwide. • However, large-scale implementation of such screening programs requires radiologists to evaluate a huge number of scans, which is costly and error-prone. • Aidence is an Amsterdam start-up developing an AI assistant for helping radiologists with detecting, reporting and tracking of lung nodules. This talk covers the deep learning techniques that we use to obtain state of the art”
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- 37. • “Streams, whichwas developed in partnership with technology company DeepMind, uses a range of test result data to identify which patients could be in danger of developing AKI and means doctors and nurses can respond in minutes rather than hours or days - potentially saving lives. More than 26 doctors and nurses at the Royal Free Hospital are now using Streams and each day it is alerting them to an average of 11 patients at risk of acute kidney injury.” https://www.royalfree.nhs.uk/news-media/news/new-app-helping-to-improve-patient-care/
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- 40. • Algorithms havebeen developed to: • Take raw digital data output by the gamma camera • identify where the heart is, reconstruct it into tomographic images and re- orient those images • Take tomographic images of the heart • evaluate the signals from several hundred portions of the myocardium, comparing the strength of the signals with those expected in a normal heart and generate an exact quantitative measurement of the location, extent and severity of perfusion abnormalities of the heart. • Analyze the dynamic functioning of the heart (i.e., the way it contracts and thickens during its cycle). • A dynamic measurement of the heart cavity volume is performed from electrocardiographically gated three-dimensional nuclear cardiology images by automatically identifying the endocardial and epicardial surfaces and following their motion throughout the cardiac cycle.
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- 42. • Despite theirdifferences, EBM and ML can assist one another. • Algorithms can facilitate more precise estimates of individual risk, with implications for choice between diagnostic tests or therapies that can then be compared in prospective, adaptive, randomized controlled trials. • Mendelian randomization and statistical analyses based on directed acyclic graphs and different matching techniques. • validate causal inferences based on ML associations. • Clinical trials can compare ML-based interventions with usual care • to assess their feasibility and validity in routine care. • ML needs to develop common nomenclatures, evaluation and reporting standards, comparative analyses of different algorithms, and training programs for clinicians http://annals.org/aim/article-abstract/2680060/machine-learning-evidence-based-medicine
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- 46. Summary • Big Data= Lots and variety of data • Artificial intelligence, machine learning , deep learning • Who ? • Expert in the area of interest – YOU • Expert in the computer science – Data engineering , Data scientists • Expert in disease modelling – statisticians, epidemiologists • Data source, algorithm, prediction, deployment • Resources – HPC, GPU-based analysis • Opportunity , new field