MIT Watson AI Lab, Cambridge, MA

On Friday April 27th, MIT and IBM Research will co-host the 2nd North East Computational Health Summit. The theme of this year’s summit is AI in Healthcare, with the objective of bringing together machine learning, medical informatics and computational health professionals to share latest developments and explore potential collaborations to unlock the potential of Artificial Intelligence for Healthcare.

We will also feature an interactive panel, as well as submitted papers and posters on various aspects of computational health including deep learning, observational studies, predictive modelling, translational informatics, patient engagement, explanatory models, health behavior and digital health. There will also be ample opportunities for less formal interactions at lunch-time, and well as during the poster session and breaks.

Call For Participation

We invite submissions of abstracts describing all aspects of research relating to design and implementation of AI solutions in healthcare. This encompasses relevant tasks in data mining, data analytics, data science, natural language processing and machine learning as well as explanatory models. The primary emphasis is on advances in the science and application of AI in healthcare. Submitted abstracts will go through a peer review process and will be selected for either podium or poster presentations by the review committee. Some poster presentations will also be selected for a short oral spotlight presentation prior to the poster session.


Submissions are limited to a total of 1 page, including all content and references. Submission can be made via an EasyChair submission.

Key Dates

  • Abstracts due: Abstract submission is now closed.
  • Notification to Submitters: 03/26/2018.
  • Meeting Date: 04/27/2018


For each accepted paper or poster, at least one author must attend the conference and present the paper/poster. There will be no official publication of papers, however, podium presentations will be video-taped and presenters will be asked to sign a release form to allow the videos to be made available on the conference web site.

While we are seeking to showcase new work, our summit does allow submission of papers that are under review or have been recently published in a conference or a journal.


8:30-9:00 AM Welcome and Opening Remarks
Conference Chairs: Jianying Hu, Amar Das, David Sontag, Regina Barzilay
9:00-10:00 AM Opening Keynote by Kenneth Mandl
An AI and Clinical Decision Making Apps Ecosystem for the Point of Care and Patients at Home
10:00-10:20 AM Coffee Break
10:20-12:00 PM Panel – Explainable AI Models for Health Informatics
Moderator: Finale Doshi-Velez
Panelists: Anil Jain, Byron Wallace, David Sontag, Ziad Obermeyer
12:00-1:30 PM Lunch
1:30-2:30 PM Afternoon Keynote by Peter Szolovits
Knowledge and Learning: How we moved toward data-driven decision support
2:30-2:40 PM Poster Spotlights
2:40-3:45 PM Poster Session (and Coffee Break)
3:45-5:15 PM Contributed Podium Presentations:
5:15 PM Closing Remarks: Conference Chairs

Featured Keynotes

  • Kenneth Mandl, MD, MPH

    Donald A.B. Lindberg Professor of Pediatrics & Biomedical Informatics,
    Harvard Medical School

  • Peter Szolovits, PhD

    Professor of Computer Science and Engineering, EECS,

Featured Panelists

  • Finale Doshi-Velez, PhD

    Assistant Professor, Computer Science,
    Harvard University

  • Anil Jain, MD, FACP

    VP & Chief Health Information Officer,
    IBM Watson Health

  • Byron Wallace, PhD

    Assistant Professor, College of Computer and Information Science,
    Northeastern University

  • David Sontag, PhD

    Assistant Professor, EECS,

  • Ziad Obermeyer, MD, MPhil

    Assistant Professor, Health Care Policy,
    Harvard Medical School

Summit Chairs

Regina Barzilay
Delta Electronics Professor, EECS, MIT
David Sontag
Assistant Professor, EECS, MIT
Amar Das
Learning Health Systems, IBM Research
Jianying Hu
Center for Computational Health, IBM Research AI

Organizing Committee

Eileen Koski Morgan Foreman Daby Sow Soumya Ghosh
Li-Wei H. Lehman Szymon Fedor Varesh Prasad Di Jin

Podium Presentations

3:45-4:00 PM Stage-based Behavioral Coaching for Stress Management: A Case Study on Optimal Policy Estimation with Multi-stage Threshold Q-learning 
Pei-Yun (Sabrina) Hsueh, Chinghua Chen, IBM Research
4:00–4:15 PM DeepFaceLIFT: Interpretable Personalized Models for Automatic Estimation of Self-Reported Pain
 Dianbo Liu, Fengjiao Peng, Andrew Shea, Ognjen (Oggi) Rudovic, Rosalind Picard, MIT
4:15–4:30 PM DrugMap: Monitoring opioid impacts through automated news media processing 
Clark Freifeld, Northeastern University & Boston Children's Hospital
4:30-4:45 PM Standardized and Reproducible Analysis Enables Identification of Novel Primary Graft Dysfunction Biomarkers using Exosome Proteomics
 Nicholas Giangreco, Barry Fine, Nicholas Tatonetti, Columbia University
4:45–5:00 PM Applying Machine Learning in Continuous Glucose Monitoring for Diabetes Management 
Deepak Turaga, Yuan-Chi Chang, Raju Pavuluri, Saket Sathe, Long Vu, Rodrigue Ngueyep Tzoumpe, IBM Research; Lingtao Cao, IBM Watson Health
5:00–5:15 PM Machine Learning for Mobile Health: Progress and Future Directions
 Benjamin Marlin, Roy Adams, Annamalai Natarajan, and Deepak Ganesan, University of Massachusetts, Amherst

Poster Spotlights

Simultaneous Modeling of Multiple Complications for Risk Profiling in Diabetes Care 
Bin Liu, Ying Li, Soumya Ghosh, Zhaonan Sun, Kenney Ng, and Jianying Hu
Identification of Serious Illness Conversations during Intensive Care Unit Admissions using Deep Neural Networks Isabel Chien, Alex Chan, Edward Moseley, Saad Salman, Sarah Bourland, Daniela Lamas, Anne Walling, James Tulsky, and Charlotta Lindvall
Modeling Irregularly Sampled Time Series using RNNs Satya Narayan Shukla, and Benjamin Marlin
Picturing Real-life Stress Through the Lens of Consumer-grade Sensors
 Tian Hao
Updating COPD Clinical Guidelines with Discriminative Rectangle Mixture Model Junxiang Chen, Yale Chang, Brian Hobbs, Peter Castaldi, Michael Cho, Edwin Silverman, and Jennifer Dy
Data-driven Risk Characterization and Prediction of Renal Failure among Diabetic Type 2 Patients using Electronic Medical Records Prithwish Chakraborty, Vishrawas Gopalakrishnan, Sharon Hensley Alford, and Faisal Farooq
Reusable clinical profiles with temporal patterns for machine learning applications Kenneth Roe, Vibhu Jawa, Richard Zhu, Brant Chee, Jordan Matelsky, Matt Kinsey, Jeremy Epstein, Xiaohan Zhang, Christopher Chute, and Casey Taylor
An Automated Pipeline for Monitoring Patient-Ventilator Interaction (PVI) Yeganeh Marghi, Matin Kheirkhahan, and Reza Sharifi