Events

Shep Doeleman, PhD, 2020 Breakthrough Prize Winner; Astrophysicist at Center for Astrophysics

Landmark Ideas Series

November 9, 2020 at 4:00PM - 5:30PM

What can medicine learn about collaboration and data sharing from one of the most successful team science projects of all time--creating a telescope the diameter of the earth to snap an image of a black hole? Black holes are cosmic objects so massive and dense that their gravity forms an event horizon: a region of spacetime from which nothing, not even light, can escape. Einstein's theories predict that a distant observer should see a ring of light encircling the black hole, which forms when radiation emitted by infalling hot gas is lensed by the extreme gravity. The Event Horizon Telescope (EHT) is a global array of radio dishes that forms an Earth-sized virtual telescope, which can resolve the nearest supermassive black holes where this ring feature may be measured. On April 10th, 2019, the EHT project reported success: we have imaged a black hole and have seen the predicted strong gravitational lensing that confirms the theory of General Relativity at the boundary of a black hole.  This talk will describe the project, and the global collaborative approach that produced these first results, as well as future directions that will enable real-time black hole movies.

Ben Reis, PhD, Faculty, Computational Health Informatics Program (CHIP); Director, Predictive Medicine Group, Computational Health Informatics Program (CHIP) Assistant Professor of Pediatrics, Harvard Medical School at Boston Children's Hospital

BCH AI & Machine Learning Working Group

October 16, 2020 at 09:30AM - 10:30AM

Dr. Ben Reis discussed recent developments in machine learning approaches to some of the grandest challenges of human health, including pandemic prediction, suicide prevention, bioterrorism detection, and drug safety prediction. The focus was on understanding both the methodological challenges involved and the ramifications of generating actionable predictions in these critical areas. The talk concluded by formulating a set of central challenges and opportunities facing the field of Predictive Medicine.

BCH AI and Machine Learning Working Group Lightning Talks

BCH AI & Machine Learning Working Group

September 9, 2020 at 09:30AM - 10:30AM

The BCH AI and Machine Learning Working Group held our first Lightning Talks session, where multiple investigators gave brief overviews of numerous Machine Learning applications at Boston Children’s Hospital to foster clinical and machine learning collaborations across the hospital.

Jonathan Bickel, MD, MS; Ronald Wilkinson, MA, MS, CBIP; Ashley Doherty, MS, at

BCH AI & Machine Learning Working Group

August 14, 2020 at 09:30AM - 10:30AM

Boston Children’s Hospital data warehouse integrates 15 years of extensive clinical and administrative data sources and more years of selected data sources. While the contents are used extensively for daily operational reporting, the potential for extensive retrospective and predictive analytics is largely untapped. Jonathan Bickel, Ashley Doherty, and Ron Wilkinson will show something of the breadth of data available in the EDW, discuss how predictive modeling tools can access the data, discuss ideas for predictive modeling applications that they think would be valuable, and explain the conditions on which access to the data can be granted.

Yangming Ou, PhD, Assistant Professor of Radiology; Affiliate Faculty, Computational Health Informatics Program; Faculty, Fetal-Neonatal Neuroimaging Data Science Center at Boston Children's Hospital

BCH AI & Machine Learning Working Group

July 17, 2020 at 09:30AM - 10:30AM

Dr. Yangming Ou briefly reviewed some major concepts and milestones of AI in medical images. The focus of Dr. Ou’s talk was on 3D medical images, for AI’s application in disease diagnosis, outcome prediction, early screening, neuroscience, and others. Dr. Ou then discussed some major challenges and potential opportunities, including further improving accuracy in detecting small diffuse lesions, and facilitating AI in small sample sizes.

Tim Miller, PhD, Assistant Professor of Pediatrics, Computational Health Informatics Program at Boston Children's Hospital

BCH AI & Machine Learning Working Group

June 30, 2020 at 4:45PM - 5:30PM

Dr. Timothy Miller discussed articles that he recently published on natural language processing of computerized text. 1. Dligach D, Majid A, Miller T. Toward a Clinical Text Encoder: Pretraining for Clinical Natural Language Processing With Applications to Substance Misuse. SSRN. 2020. 2. Miller T, Avillach P, Mandl K. Experiences Implementing Scalable, Containerized, Cloud-based NLP for Extracting Biobank Participant Phenotypes at Scale. SSRN. 2020.

Arjun (Raj) Manrai, PhD, Faculty, Computational Health Informatics Program (CHIP); Director, Laboratory for Probabilistic Medical Reasoning; Assistant Professor, Harvard Medical School at Boston Children's Hospital

BCH AI & Machine Learning Working Group

May 8, 2020 at 09:30AM - 10:30AM

Blood laboratory measures such as glucose and hemoglobin are the basis for much of clinical decision making, yet baseline variation for many laboratory measures remains incompletely characterized across age, gender, and race groups. I will introduce foundational techniques from machine learning and statistical genetics and show how they can be applied to systematically unpack variation in blood laboratory data across population groups. These analyses reveal widespread demographic structure in blood laboratory data.

Ricky Bloomfield, MD, Clinical and Health Informatics Lead at Apple

Landmark Ideas Series

March 2, 2020 at 4:00 PM - 5:30 PM

Healthcare has been slow to adopt scalable, interoperable, user-centric solutions as other industries have done, but technology is finally catching up with the needs of patients. Ricky will share how Apple's support and use of open standards has helped accelerate adoption across the country.

David Clark, PhD, MS, An Inventor of the Internet; Technical Director at MIT Internet Policy Research Initiative

Landmark Ideas Series

February 13, 2020 at 4:00PM - 5:30PM

In the early days of the Internet, technical innovation shaped its future. Today, issues of economics, market dynamics, incentives, and some fundamental aspects of networked systems shape the future. This talk will summarize eleven forces that are shaping the future of the Internet and make an argument that we are at a point of inflection in the character of the Internet, as profound as the change in the 1990’s when the Internet was commercialized.

Nicholas A. Christakis, MD, PhD, MPH, Scientist and Physician at Yale University

Landmark Ideas Series

December 16, 2019 at 4:00PM - 5:30PM

Human beings choose their friends, and often their neighbors and co-workers, and they inherit their relatives; and each of the people to whom we are connected also does the same, such that, in the end, we humans assemble ourselves into face-to-face social networks. Why do we do this? How has natural selection shaped us in this regard? What role do our genes play in the topology of our social ties? And how might a deep understanding of human social network structure and function be used to intervene in the world to make it better?