Speaker: Christina Farr, Health-Tech Investor at OMERS Ventures, Author of the “Second Opinion” newsletter, Former Technology and Health Reporter at CNBC
Date: October 1, 2021 at 4:00PM - 5:30PM
Talk details will be forthcoming.
Chip 25th Anniversary Symposium
Date: October 20, 2019
The Boston Children’s Hospital Computational Health Informatics Program celebrated our 25th Anniversary last year with a Symposium “Separating the Signal from the Noise: Establishing the Foundation for Healthcare in 2044” at the Harvard Club of Boston.
Landmark Ideas Series
Speaker: Enrico Coiera, PhD, Director of the Centre for Health Informatics at Australian Institute of Health Innovation
Date: April 29, 2021 at 5:00PM - 6:30PM
In an age where technology appears to rule supreme, it is easy to forget that our relationship with technology is complicated. Just as humans shape technology, it shapes us in return. It is also easy to only see things through the lens of the technologies we have to hand, and build solutions that ill fit reality. Electronic health records for example demand that clinical work bends to the needs of documentation, with the end result being burnt out clinicians who do anything but what they were taught at medical school. Algorithms built with our cleverest machine learning methods just end up making concrete the biases implicit in their data sets. Seeing human systems like healthcare as sociotechnical systems helps us understand these unintended consequences, and gives us a different lens to understand technology design and use.
Speaker: Lawrence Lessig, JD, Founder of Creative Commons, Roy L. Furman Professor of Law and Leadership at Harvard Law School
Date: March 1, 2021 at 4:00PM - 5:30PM
Privacy has become a central focus of policy debates in every context. In this talk, Lessig argues that we’re conceiving of the problem in a fundamentally flawed way. Offered is a different framework, radically different but critically better. Or so it is hoped.
Speaker: Atul Butte, MD, PhD, Priscilla Chan and Mark Zuckerberg Distinguished Professor at UCSF and Chief Data Science Officer at University of California Health System
Date: February 22, 2021 at 4:00PM - 5:30PM
There is an urgent need to take what we have learned in our new data-driven era of medicine, and use it to create a new system of precision medicine, delivering the best, safest, cost-effective preventative or therapeutic intervention at the right time, for the right patients. Dr. Butte's lab at the University of California, San Francisco builds and applies tools that convert trillions of points of molecular, clinical, and epidemiological data -- measured by researchers and clinicians over the past decade and now commonly termed “big data” -- into diagnostics, therapeutics, and new insights into disease. Dr. Butte, a computer scientist and pediatrician, will highlight his center’s recent work on integrating electronic health records data across the entire University of California, and how analytics on this “real world data” can lead to new evidence for drug efficacy, new savings from better medication choices, and new methods to teach intelligence – real and artificial – to more precisely practice medicine.
Speaker: Timothy Yu, MD, PhD, Neurogeneticist and Researcher at Boston Children's Hospital
Date: January 11, 2021 at 4:00PM - 5:30PM
Genome sequencing is revolutionizing the diagnosis of rare diseases, but 95% of these conditions still lack effective therapy. With up to 7,000 distinct genetic diseases to tackle, new and creative frameworks will be necessary to meet this need. Recent advances offer the prospect of platform-based therapeutic approaches to certain genetically targetable disorders — in the right circumstances, facilitating the design and deployment of hyper-personalized drugs for conditions affecting as few as even a single patient. The scientific, clinical, ethical, and regulatory implications of these capabilities will be discussed.
Speaker: Shep Doeleman, PhD, 2020 Breakthrough Prize Winner; Astrophysicist at Center for Astrophysics
Date: 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.
Speaker: Ricky Bloomfield, MD, Clinical and Health Informatics Lead at Apple
Date: 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.
Speaker: David Clark, PhD, MS, An Inventor of the Internet; Technical Director at MIT Internet Policy Research Initiative
Date: 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.
Speaker: Nicholas A. Christakis, MD, PhD, MPH, Scientist and Physician at Yale University
Date: 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?
Speaker: Maxine Mackintosh, PhD, Winston Churchill Fellow at the Alan Turing Institute and University College London
Date: October 21, 2019 at 4:00PM - 5:30PM
A day does not go by without a new framework for ethics in AI, particularly in health and social care. But when your health system is based on need versus ability to pay, yet the skills, computational power and often data lies in tech companies, from SMEs to multinationals, it can be difficult to see how a health system can digitize in an equitable and ethical manner. Maxine’s talk will share some examples of the learnings, attitudes and practical ways the UK has approached data stewardship, partnerships, “intangible assets" and transparency of health data organizations looking to work with the NHS. These examples will include learnings from DeepMind Health’s Independent Review Board, the use of consumer data in the UK for health research, and how the UK is approaching some of these discussions at a national, policy level.
BCH AI and Machine Learning Working Group
Speaker: Lucy Gao, PhD, Assistant Professor of Statistics at the University of Waterloo
Date: May 11, 2021 at 2:00PM - 3:00PM
Dr. Gao will discuss the following article: Gao, Bien, and Witten (2020). Selective inference for hierarchical clustering. arXic:2012.02936. This journal club is only available to the BCH community. If you would like to be sent a calendar invite please email email@example.com.
Speaker: Andrew Beam, PhD, Assistant Professor, Department of Epidemiology at the Harvard T.H. Chan School of Public Health
Date: April 13, 2021 at 2:00PM - 3:00PM
Dr. Beam led a discussion on the following article: Tom B Brown, Benjamin Mann, Nick Ryder, et al. Language models are few-shot learners. arXiv preprint arXiv:2005.14165 [cs], 2020. Dr. Beam also discussed results from his group that evaluates this model on medical applications.
Speaker: Ben Reis, PhD, Director, Predictive Medicine Group, Computational Health Informatics Program (CHIP), Faculty at at Harvard Medical School
Date: March 16, 2021 at 2:00PM - 3:00PM
Dr. Ben Reis will lead a discussion on the recent New England Journal of Medicine paper he co-authored, providing the first real-world study of effectiveness of the Pfizer-BioNTech COVID-19 vaccine. It was the largest study yet to quantify the impact of the vaccine outside the confines of a clinical trial. The study used innovative epidemiological methods to analyze vaccine effectiveness for preventing symptomatic diseases, severe illness and death. Dr. Reis will discuss his study and the lessons learned from the nation-wide mass vaccination experience in Israel. The study has been featured in The New York Times, Bloomberg, and Fortune.
Speaker: James Diao, MD, Harvard Medical School MD Student at Boston Children's Hospital
Date: February 23, 2021 at 2:00PM - 3:00PM
James will lead a discussion on approaches to addressing racial equity concerns with clinical algorithms, including for arthritis severity (Pierson et al. 2021) and kidney function estimates (Diao et al. 2021):
Speaker: Rudolph Pienaar, PhD, Staff Scientist at Boston Children's Hospital
Date: January 29, 2021 at 09:30AM - 10:30AM
We are often wowed by the *potential* of AI (and frankly other sophisticated computational approaches) to transform research and clinical workflows. New approaches seem to magically hold unbounded promise. Yet, there is often a large gulf between theory and practice, between a shiny new technique and having anyone just use it. The questions of "How do I get this ? How do I get my data from PACS to connect to this? How do I go from DICOM to something that the neural network wants? How do I get results that are useful?" In this talk I will provide some insights into practically developing, using, and disseminating "AI" (and other) workflows in the BCH clinical and research environment.
Speaker: Guergana Savova, PhD, Associate Professor of Pediatrics, Computational Health Informatics Program at Boston Children's Hospital
Date: December 8, 2020 at 4:45PM - 5:30PM
Dr. Savova led a discussion of tasks and applications of clinical Natural Language Processing (NLP) in medicine, such as: The landscape of neural approaches and clinical NLP (Wu et al, 2019; https://pubmed.ncbi.nlm.nih.gov/31794016/) Data challenges in clinical NLP (de-identified data, usability and challenges) Some tasks and applications Information extraction for cancer surveillance (DeepPhe-CR) (Savova et al, 2017; https://pubmed.ncbi.nlm.nih.gov/29092954/) Treatment information extraction (Bitterman et al, 2020 https://www.aclweb.org/anthology/2020.clinicalnlp-1.21.pdf; Lin et al, 2020 https://www.aclweb.org/anthology/2020.louhi-1.12.pdf) What is trending.
Speaker: Danielle Rasooly, PhD, Postdoctoral Fellow, Computational Health Informatics Program at Boston Children's Hospital
Date: November 10, 2020 at 4:45PM - 5:30PM
Dr. Rasooly led a discussion of the following paper about Google/DeepMind's AI system for breast cancer screening: McKinney et al. International evaluation of an AI system for breast cancer screening. Nature2020. as well as the following paper AI transparency/reproducibility: Haibe-Kains et al. Transparency and reproducibility in artificial intelligence. Nature 2020. The two papers are accessible as pdfs here.
Speaker: 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
Date: 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.
Date: 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.
Speaker: Jonathan Bickel, MD, MS; Ronald Wilkinson, MA, MS, CBIP; Ashley Doherty, MS, at
Date: 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.
Speaker: 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
Date: 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.
Speaker: Tim Miller, PhD, Assistant Professor of Pediatrics, Computational Health Informatics Program at Boston Children's Hospital
Date: 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.
Speaker: 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
Date: 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.