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Conference Details - Agenda

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2018 MIT AI in Life Sciences and Healthcare Conference

December 4-5, 2018
Day 01 Day 02 | All

Day 1: Tuesday, December 4th, 2018

8:00 - 9:00

Registration with Light Breakfast

9:00 - 9:10

Welcome Remarks

Session 1: Tools and Impact

9:10 - 9:40

Machine Learning for Pharmaceutical Discovery and Synthesis
This talk will describe ongoing efforts within DARPA and MIT-industry sponsored consortium projects focused on applications of machine learning techniques to pharmaceutical discovery and synthesis. We will summarize the integration of these techniques into a computer aided synthesis planning workflow that, for a given molecular target, predicts a rank ordered list of reaction paths that connect the target to purchasable starting materials via a series of feasible reaction steps. Secondly, we will describe on-going efforts to develop machine learning approaches for quantitative predictions of chemical structure and property relationships. Finally, we will briefly discuss progress on using natural language processing to extract chemical data from prior historical data.
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9:40 - 10:10

Future of Digital Medicine and Clinical Trials with Novel Machine Learning and AI Architectures
Phase 3 clinical outcome trials to evaluate new drugs, therapies, and vaccines are the most complex experiments performed in medicine. Around 50% of phase 3 trials fail, costing healthcare industries, governments, and academic research hospitals billions of dollars and delay life-saving treatments to patients. This talk will share novel phase 2 and 3 clinical trials designed by unorthodox “self-learning” AI algorithms to help accelerate medical product development and bring new innovations and advances to patients safely. Our clinical trial design achieves significant reduction in tumor sizes in patients suffering from glioblastomas (brain tumors) and offers personalized and precision dosing recommendations to individuals compared to human-expert policies. We outline a strategic plan to conduct clinical trials with devices, algorithms, and real-world evidence in accordance with the 21st Century Cures Act.
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10:10 - 10:40

Robust Data Analytics in Biopharmaceutical Manufacturing
Although process data analytics is a valuable tool for improving the manufacturing of biologic drugs, selection of the best method requires a substantial level of expertise. This talk describes a robust and automated approach for process data analytics tool selection that allows the user to focus on goals rather than methods. The approach first applies tools to automatically interrogate the data to ascertain its characteristics, e.g., nonlinearity, correlation, dynamics. This information is then used to select a best-in-class process data analytics tool. The approach is demonstrated for industrial data for the manufacturing of a monoclonal antibody.
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10:40 - 11:00

Networking Break

Session 2: Quest for Intelligence

11:00 - 11:30

The MIT Quest for Intelligence
The MIT Quest for Intelligence (The Quest) aims to build on MIT’s rich history of innovation and impact in the study of intelligence, our next step towards the future. Comprised of two linked entities, The Core and The Bridge, The Quest aims to advance two fundamental intelligence challenges: Can we reverse engineer intelligence? And, how can we deploy our current and expanding understanding of intelligence to the benefit of society?

11:30 - 12:00

Making Images Part of Medical Record
Currently, medical images require a physician to extract clinically relevant information. This talk will explore current work towards making images part of the quantitative medical history and to enable large-scale image-based studies of disease. Although large databases of clinical images contain a wealth of information, medical acquisition constraints result in sparse scans that miss much of the anatomy. These characteristics often render computational analysis impractical as standard processing algorithms tend to fail when applied to such images. Our goal is to enable application of existing algorithms that were originally developed for high resolution research scans to severely undersampled images. Application of the method is illustrated in the context of neurodegeneration and white matter disease studies in stroke patients.
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12:00 - 12:30

Next-Generation Machine Learning for Biotechnology
Machine learning, a collection of data-analytical techniques aimed at building predictive models from multi-dimensional data sets, is becoming integral to modern biological research. By enabling one to generate models that learn from large data sets and make predictions on likely outcomes, machine learning can be used to study complex cellular systems, such as biological networks. In this talk, we discuss opportunities and challenges at the intersection of machine learning and biotechnology, ones that could impact disease biology, drug discovery, and synthetic biology.
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12:30 - 1:30


Session 3: Patient /Subject Insight

1:30 - 2:00

Learning About Breast Cancer from Images and Text
Breast cancer is global problem with over 500,000 women dying from the disease every year, yet all of our decisions and insights are based on only a fraction of the information that exists at both the patient and population level. In this talk, we explore a machine learning approach to cancer that integrates rich patient information at population scale, and discuss the type of tools this enables. We have developed A.I systems for automatically reading mammograms, performing personalized risk assessment and mining medical records and implemented them clinically at Massachusetts General Hospital.
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2:00 - 2:30

AI for Passive In-Home Patient Monitoring: From Wearables to Invisibles
This talk introduces Emerald, a novel MIT technology for in-home non-intrusive patient monitoring. The Emerald device is a WiFi-like box that runs customized machine learning algorithms to learn digital biomarkers from the wireless signals in the patient's home. It can remotely monitor the patient’s gait speed, falls, respiratory signal, heart rate, and sleep quality and stages. The sensing is completely passive – i.e., the patient can go about her normal life without having to wear any sensors on her body, write a diary, or actively measure herself. This talk will discuss the technology and the results from pilot studies in various therapeutic areas.
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2:30 - 3:00

Emotion AI and Future Health
AI and machine learning are becoming embedded in our wearables and smartphones, enabling new insights and interventions for improving lives for patients with conditions including autism, epilepsy, and depression. The latter is growing and forecast to become the #1 disease burden by 2030. How close are we to forecasting changes in mood, stress, and physical health before they happen? Could AI help us prevent future diseases such as depression, and help people stay healthy instead of becoming sick tomorrow? This talk will show the latest findings that suggest that a better mental health future could be possible with AI.
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3:00 - 3:30

Networking Break

3:30 - 4:30

Session 4: Panel Discussion: Perspectives on AI in Life Science
Moderator: John Roberts
- Ellie Chabi, Santen Pharmaceutical
- Ryan Davis, Secure AI Labs
- Prof. Martha Gray, MIT
- Cory Kidd, Catalia Health
- Andrew A. Radin, twoXAR

4:30 - 5:30

Networking Reception