For more information about the event, please visit 12th WBME - Workshop on Biomedical Engineering
The ubiquity of algorithms and data is one of the hallmarks of the Information Age. In a world powered by technology, where smartphones have more computing power than all of NASA’s computers during the Apollo missions, algorithms run virtually everything.
However, in the advent of the 4th Industrial Revolution, data is growing faster than it can be analyzed and classic algorithms have been unable to cope with this Big Data explosion. This is where Artificial Intelligence (AI), and Machine Learning (ML) in particular, really shine.
ML systems learn directly from data without being explicitly told to do so, and they have found enormous success in such tasks as email filtering, computer-aided diagnostics (CADx) and autonomous driving. Companies like Facebook, Amazon, Netflix and Google are investing heavily on AI, and ML engineer and data scientist positions are among the highest paid and “sexiest” jobs of the early 21st century. Nonetheless, getting past the hype and putting buzzwords aside is hard when one lacks a basic understanding of how these systems actually work. This is especially relevant in the health sector where transparency and accountability are of paramount importance.
In this workshop, we will give an overview of what ML actually is, highlighting some of its applications to the health sector - from the rise of expert systems in the 80s to the diagnosis, prognosis and treatment of COVID-19 - how it is shaping the present and how it may one day decide our future.
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Overview of Artificial Intelligence and Machine Learning (40 mins)
- Historical Perspective
- What is AI?
- What is ML?
- Traditional Programming vs. ML
- Learning Tasks
- Learning Scenarios
- ML Applications
- ML Pipeline
- The “Five Tribes” of ML
- ML Algorithms
- Debugging Problems
- Main challenges
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Machine Learning in Medicine and Healthcare (30 mins)
- Patient data
- Consumer health wearables
- Electronic health records (EHR)
- Clinical datasets
- Case Studies
- (Wu et al., 2010) Endocrinology: Diabetes Detection
- (Norvig, 1992) Hematology: MYCIN
- (Szolovitz, 1982) Internal Medicine: CADUCEUS
- (Esteva et al., 2017) Dermatology: Skin Lesion Classification
- (Majkowska et al., 2019) Pneumology/Radiology: Chest X-Ray Interpretation
- (Arcadu et al., 2019; Raman et al., 2019) Ophthalmology: Diabetic Retinopathy Detection and Progression
- (Steiner et al., 2018; Wu et al., 2019) Oncology: Breast Cancer Screening
- (Harutyunyan et al., 2019) General Practice: Clinical Event Prediction
- (Libbrecht & Noble, 2019; Zou et al., 2019) AI/ML in Genetics and Genomics
- (Zhou et al., 2020) Surgery: Pre-Operative Planning, Intra-Operative Guidance and Surgical Robots
- (Gottesman et al., 2019; Komorowski et al., 2018) AI/ML in the ICU: Sepsis Treatment
- (AWS) NLP in Healthcare: Amazon Comprehend Medical
- (Harmon et al., 2020) COVID-19 Diagnosis
- (Yan et al., 2020) COVID-19 Prognosis
- (Arshadi et al., 2020) COVID-19 Treatment
- Patient data
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Opportunities, Challenges and Future Prospects (20 mins)
- AI/ML in Healthcare: Benefits and Drawbacks
- Opportunities
- ML and Ethics
- ML and Cloud Computing
- Federated Learning
- Meta-Learning: learning to learn
- Future Directions
- Green Models
- Pattern Recognition + Symbolic AI
- MLOps
- Quantum Machine Learning
Altae-Tran, H. et al. (2017). Low data drug discovery with one-shot learning. ACS Cent. Sci., 3: 283-293
Arcadu, F. et al. (2019). Deep learning algorithm predicts diabetic retinopathy progression in individual patients. Npj Digit. Med., 2: 92-101
Arshadi, A. et al. (2020). Artificial intelligence for COVID-19 drug discovery and vaccine development. Front. Artif. Intell., 3: 65
Biamonte, J. et al. (2018). Quantum Machine Learning. Nature, 549: 195-202
Chen, I. et al. (2020). Ethical machine learning in healthcare. Retrieved from arXiv:2009.10576
Ching, T. et al. (2018). Opportunities and obstacles for deep learning in biology and medicine. J. R. Soc. Interface, 15: 20170387
Domingos, P. (2012). A few useful things to know about machine learning. Comm. ACM, 55(10): 78-87
Esteva, A. et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542: 115-118.
Esteva, A. et al. (2019). A guide to deep learning in healthcare. Nat. Med., 25: 24-29
Garnelo, M., Arulkumaran, K. & Shanahan, M. (2016). Towards Deep Symbolic Reinforcement Learning. Retrieved from arXiv:1609.05518
Ghassemi, M. et al. (2018). A review of challenges and opportunities in machine learning for health. Retrieved from arXiv:1806.00388
Gottesman, O. et al. (2019). Guidelines for reinforcement learning in healthcare. Nat. Med., 25: 14-18
Harmon, S. et al. (2020). Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets. Nat. Commun., 11: 4080
Harutyunyan, H. et al. (2019). Multitask learning and benchmarking with clinical time series data. Sci. Data, 6: 96
Hassabis, D. et al. (2017). Neuroscience-inspired artificial intelligence. Neuron, 95(2): 245-258
Jiang, F. et al. (2017). Artificial intelligence in healthcare: past, present and future. Stroke Vasc. Neurol., 2: e000101
Kalis, B., Collier, M. & Fu, R. (2018). 10 promising AI applications in health care. Retrieved from Harvard Business Review
Komorowski, M. et al. (2018). The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care. Nat. Med., 24: 1716-1720
Libbrecht, M. & Noble, S. (2015). Machine learning applications in genetics and genomics. Nat. Rev. Genet., 16: 321-332
Lillicrap, T. et al. (2020). Backpropagation and the brain. Nat. Rev. Neurosci., 21(6): 335-346
Litjens, G. et al. (2017). A survey on deep learning in medical image analysis. Retrieved from arXiv:1702.05747
Lotte, F. et al. (2018). A review of classification algorithms for EEG-based brain-computer interfaces: a 10 year update. J. Neural Eng., 15: 031005
Majkowska, A. et al. (2020). Chest radiograph interpretation with deep learning models: assessment with radiologist-adjudicated reference standards and population-adjusted evaluation. Radiology, 294(2): 421-431
Mnih, V. et al. (2015). Human-level control through deep reinforcement learning. Nature, 518: 529-533
Nam, C. et al. (2018). Brain-Computer Interfaces Handbook: The Technological and Theoretical Advances. Boca Raton, FL: CRC Press
Panch, T., Szolovits, P. & Atun, R. (2019). Artificial intelligence, machine learning and health systems. J. Glob. Health, 8: 020303
Piwek, L. et al. (2016). The rise of consumer health wearables: promises and barriers. PLOS Medicine, 13(2): e1001953
Rajkomar, A., Dean, J. & Kohane, I. (2019). Machine learning in medicine. N. Engl. J. Med., 380: 1347-1358
Raman, R. et al. (2019). Fundus photograph-based deep learning algorithms in detecting diabetic retinopathy. Eye, 33: 97-109
Ravì, D. et al. (2017). Deep learning for health informatics. IEEE Journ. Biom. Health Inform., 21(1): 4-21
Ribeiro, M., Singh, S. & Guestrin, C. (2016). “Why should I trust you?” Explaining the predictions of any classifier. Retrieved from arXiv:1602.04938
Rieke, N. et al. (2020). The future of digital health with federated learning. Retrieved from arXiv:2003:08119
Samuel, A. (1959). Some studies in machine learning using the game of checkers. IBM Journal, 3(3): 535-554
Sculley, D. et al. (2015). Hidden technical debt in machine learning systems. Adv. Neur. Inform. Proc. Syst., 28: 2503-2511.
Schmidt, R., Schneider, F. & Hennig, P. (2020). Descending through a crowded valley: benchmarking deep learning optimizers. Retrieved from arXiv:2005.01547
Sheller, M. et al. (2020). Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. Scientific Reports, 10(1): 1-12
Simeone, O. (2017). A brief introduction to machine learning for engineers. Retrieved from arXiv:1709.02840
Steiner, D. et al. (2018). Impact of deep learning assistance on the histopathologic review of lymph nodes for metastatic breast cancer. Am. J. Surg. Pathol., 42(12): 1636-1646
Strickland, E. (2019). IBM Watson, heal thyself: how IBM Watson overpromised and underdelivered on AI healthcare. Retrieved from IEEE spectrum
Strubell, E., Ganesh, A. & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. Retrieved from arXiv:1906.02243
Szolovits, P. (1982). Artificial Intelligence in Medicine. Retrieved from Peter Szolovits’ personal website (CSAIL)
Topol, E. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25: 44-56.
Tse, L. et al. (2018). Graph cut segmentation methods revisited with a quantum algorithm. Retrieved from arXiv:1812.03050
Wiens, J. & Shenoy, E. (2017). Machine learning for healthcare: on the verge of a major shift in healthcare epidemiology. Clin. Infect. Dis., 66: 149-153
Wolpert, D. (1996). The lack of a priori distinctions between learning algorithms. Neur. Comp., 8: 1341-1390
Wu, N. et al. (2019). Deep neural networks improve radiologists’ performance in breast cancer screening. Retrieved from arXiv:1903.08297
Yan, L. et al. (2020). An interpretable mortality prediction model for COVID-19 patients. Nat. Mach. Intell., 2: 283-288
Yu, K. et al. (2018). Artificial intelligence in healthcare. Nat. Biomed. Eng., 2: 719-731
Yu, W. et al. (2010). Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes. BMC Med. Inform. Decis., 10: 16-23
Zhang et al. (2019). MetaPred: meta-learning for clinical risk predictions with limited patient electronic health records. Retrieved from arXiv:1905.03218
Zhao, A. et al. (2019). Data augmentation using learned transforms for one-shot medical image segmentation. Retrieved from arXiv:1902.09383
Zhou, X. et al. (2020). Artificial intelligence in surgery. Retrieved from arXiv:2001.00627
Zou, J. et al. (2019). A primer on deep learning in genomics. Nat. Gen., 51: 12-18
- (Berkeley) CS 189/289A: Introduction to Machine Learning
- (Berkeley) CS 285: Deep Reinforcement Learning
- (deeplearning.ai) Andrew Ng's AI for Medicine Course
- (Harvard) CS 181: Machine Learning
- (MIT) Brains, Minds & Machines Summer Course
- (MIT) Deep Learning and Artificial Intelligence lectures
- (MIT) 6.S897: Machine Learning for Healthcare
- (MIT) 6.874: Deep Learning in the Life Sciences
- (Princeton) COS597C: Machine Learning for Health Care
- (Stanford) BIODS220: Artificial Intelligence in Healthcare
- (UCL) David Silver’s Course on Reinforcement Learning
- (UToronto) APS360 Artificial Intelligence Fundamentals
- (GitHub) Awesome MLOps
- (GitHub) Awesome Machine Learning
- (GitHub) EthicalML/awesome-production-machine-learning
- Siemens AI Lab
- Siemens Machine Intelligence Research Group
- Volkswagen Group ML Research
- MIT Clinical Machine Learning Group
- MIT CSAIL Clinical Decision Making Group
- Center for Artificial Intelligence in Medicine & Imaging
- (OpenAI) AI and Compute
- (Siemens) ML/DL 2019 Whitepaper
- State of AI Report 2020
- Learn X in Y minutes