Principal Investigator Fredo Durand
I will review our work in extracting clinically relevant characterizations of anatomy and pathology from medical images in two domains. First, joint modeling of image, genetic and clinical data is used to gain insight into the patterns of disease in large heterogeneous clinical populations. Examples include studies of white matter disease in stroke patients from brain MRI, of genetically defined patterns of emphysema in COPD patients as observed in chest CT, and others. The second family of applications aims to provide accurate delineations of pathology and make predictions in medical scans of individual patients. Examples include functional imaging of the placenta and cardiac image analysis for surgical planning.
2016 MIT Digital Health Conference
Principal Investigator Admir Masic
Principal Investigator Neha Narula
Principal Investigator Rick Danheiser
The MIT community relies on our enterprise systems for a range of activities — everything from hiring and evaluating employees to managing research grants and facilities projects to maintaining student information. Our vision in updating our systems is 1) to create easy-to-use and well-integrated systems, streamlined processes, and comprehensible and accessible data for reporting and analysis; 2) to simplify our business processes to improve efficiency and effectiveness; 3) to modernize our enterprise systems and data architecture to take advantage of more innovative technology and functionality; and 4) to make our data accessible and actionable by implementing more robust data governance through clear ownership and accountability.
This talk shares both our plan and some best practices from recent efforts at transforming a complex collection of digital and non-digital assets into a more cohesive landscape, including a) addressing systems, processes, and data wholistically; b) developing a thoughtful and actionable multi-year roadmap of digital transformation projects; and c) engaging and assisting our entire community every step of the way.
Receptor Tyrosine Kinases (RTKs) are critical for normal human physiology, but can be oncogenic when highly expressed or mutated in a wide array of human cancers. To define the critical components in these networks, we have developed mass spectrometry based methods enabling the absolute quantification of tyrosine phosphorylation sites in RTK signaling networks at high temporal resolution following stimulation with different ligands or inhibitors, in vitro and in vivo. Quantitative phosphorylation data generated in this analysis provides insight into the occupancy of multiple tyrosine phosphorylation sites on the receptor, highlights mechanisms of differential regulation in response to different ligands, and highlights resistance mechanisms to selected inhibitors.
Cardiovascular disease remains the leading cause of death in the industrialized world. Although research into the etiology and treatment of cardiac disease remains a focus of numerous research groups, the accurate identification of patients who are at risk of adverse events following a heart attack remains a major challenge in clinical cardiology. In this talk I will describe how sophisticated computational biomarkers, which integrate a diverse array of clinical information, can be used to identify patients who are at elevated risk of death after a cardiac event. This work demonstrates that computational biomarkers can provide useful and powerful insights that can help guide clinical decision making.
Principal Investigator Stefan Helmreich