BCH AI & Machine Learning Working Group

The Boston Children’s Hospital Artificial Intelligence and Machine Learning Working Group gives our clinicians and investigators a forum for sharing knowledge and collaborating across the many facets of artificial intelligence and machine learning.

Core objectives:

create a forum for Boston Children’s Hospital investigators to find like-minded collaborators
foster an environment of knowledge exchange
collaborate on funding options to improve infrastructure
create a unified body for industry discussions

Focus areas:

clinical decision making
image processing and interpretation
hospital administrative functions and capacity planning
basic methods
life sciences and drug development
omics research and omics-informed medicine

Participating programs and sponsors include:

Computational Health Informatics Program (Professor Ken Mandl)
Research Computing (Associate Professor Piotr Sliz)
Department of Radiology
- Imaging Informatics (Assistant Professor Sanjay Prabhu)
- Computational Radiology Laboratory (Professor Simon Warfield, Associate Professor Ali Gholipour, Assistant Professor Benoit Scherrer and Sila Kurugol)
- Fetal-Neonatal Neuroimaging and Developmental Science Center (Professor Ellen Grant, Assistant Professors Rudolph Pienaar and Yangming Ou)
Urodynamics Program (Hsin-Hsiao Scott Wang)
Innovation Program (Professor John Brownstein and Jessica Zhang)
Clinical Informatics Fellowship (Jonathan Hron and Marvin Harper)

We host:

quarterly workgroup meetings
seminars
journal clubs

Please send an email to register your interest in joining.

Previous Events

BCH AI and Machine Learning Journal Club: Arjun Manrai, PhD

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.

Previous Events

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