Machine Learning for Healthcare (MLHC)

August 18th - 19th, 2017 - Northeastern University
Interdisciplinary Science and Engineering Complex (ISEC)
805 Columbus Avenue Boston, Massachusetts 02120

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Northeastern University

Interdisciplinary Science and Engineering Complex (ISEC)
805 Columbus Avenue Boston, Massachusetts 02120

Friday August 18, 2017 - ISEC Auditorium and Atrium


Welcoming Remarks

Session 1

The BIDMC Explore IT Program

John Halamka, BIDMC

BIDMC has implemented Machine Learning functionality from Amazon and Google to support several use cases. In this presentation, the speaker will review the implementation experience and the outcomes achieved.

Deep Learning as an FDA-Cleared Product

Daniel Golden, Arterys

Radiological diagnosis and interpretation is ready for an overhaul. Radiologists spend countless hours on tasks that are onerous and error-prone, resulting in high costs and frequent misdiagnoses. Arterys is working to address these deficiencies, using deep learning to vastly improve the speed and consistency with which radiologists read cardiac MRI studies. Our first product, Arterys Cardio DL, is the first technology ever to be cleared by the FDA that leverages cloud computing and deep learning in a clinical setting. We discuss the technology behind the software and how we proved its safety and efficacy to secure FDA clearance in the United States and the CE Mark in Europe.

Coffee Break and Discussion

Session 2

How Can NLP Help Cure Cancer?

Regina Barzilay, MIT

Majority of cancer research today takes place in biology and medicine. Computer science plays a minor supporting role in this process if at all. In this talk, I hope to convince you that NLP as a field has a chance to play a significant role in this battle. Indeed, free-form text remains the primary means by which physicians record their observations and clinical findings. Unfortunately, this rich source of textual information is severely underutilized by predictive models in oncology. In the first part of my talk, I will describe a number of tasks where NLP-based models can make a difference in clinical practice. For example, these include improving models of disease progression, preventing over-treatment, and narrowing down to the cure. This part of the talk draws on active collaborations with oncologists from MGH. In the second part of the talk, I will push beyond standard tools, introducing new functionalities and avoiding annotation-hungry training paradigms ill-suited for clinical practice. In particular, I will focus on interpretable neural models that provide rationales underlying their predictions, and semi-supervised methods for information extraction.

Tools for Interpretable Machine Learning with Healthcare Applications

Cynthia Rudin, Duke

How do patients and doctors know that they can trust predictions from a model that they cannot understand? Transparency in machine learning models is critical in high stakes decisions, like those made every day in healthcare. My lab creates machine learning algorithms for predictive models that are interpretable to human experts. As it turns out, by using modern optimization tools, one often does not need to sacrifice accuracy to gain interpretability. We will focus mainly on the problem of building medical scoring systems using data. We provide applications to ADHD diagnosis, sleep apnea screening, EEG monitoring for seizure prediction in ICU patients, and early detection of cognitive impairments. Then, we switch to creating logical models, and in particular, rule lists, which are a form of decision tree. Finally we will discuss how to model recovery curves that have realistic shapes and realistic uncertainty bands, and show an application to modeling recovery curves for prostatectomy patients.
I will focus on work of students Berk Ustun, Hima Lakkaraju, William Souillard-Mandar, and Fulton Wang. Other collaborators include Brandon Westover, Matt Bianchi, Randall Davis, Dana L. Penney, Tyler McCormick, and Ronald C. Kessler.


Coffee Break and Discussion

Dinner and Discussion

Saturday August 19, 2017 - ISEC Auditorium and Atrium


Session 3

The algorithm for precision medicine

Matt Might, Harvard/Utah

Precision medicine requires an algorithmic approach to the delivery of care, and it encounters a wide range of computational challenges. This talk will center on an in-depth case study in precision medicine, highlighting computational challenges at the forefront of the field.

Opportunities to Apply Machine Learning in Neurocritical Care

Soojin Park, Columbia

The Neurocritical care patient is monitored for reversible secondary brain injury. Timely personalized assessments of subclinical or early state changes in the neuroICU currently rely on vigilance and constant availability of expert interpretation. Those at most risk are obtunded or comatose patients, but state changes in even conscious patients may be clinically asymptomatic or subtly evade detection. With the proliferation of multimodality neuro monitoring and advances in data acquisition and analytics, the field of neuro critical care has generated studies in signal processing and machine learning, advancing the science of detection, prediction, and goal setting. There is growing demand for the implementation of these findings.


Session 4

Challenges in Developing Learning Algorithms to Personalize Treatment in Real Time

Susan Murphy, University of Michigan

A formidable challenge in designing sequential treatments is to determine when and in which context it is best to deliver treatments. Consider treatment for individuals struggling with chronic health conditions. Operationally designing the sequential treatments involves the construction of decision rules that input current context of an individual and output a recommended treatment. That is, the treatment is adapted to the individual's context; the context may include current health status, current level of social support and current level of adherence for example. Data sets on individuals with records of time-varying context and treatment delivery can be used to inform the construction of the decision rules. There is much interest in personalizing the decision rules, particularly in real time as the individual experiences sequences of treatment. Here we discuss our work in designing online "bandit" learning algorithms for use in personalizing mobile health interventions.

Optimized risk stratification and treatment decisions with machine learning

Collin Stultz, MIT

The accurate assessment of a patient’s risk of adverse events remains a mainstay of clinical care for patients with cardiovascular disease. Sophisticated methods, such as those based on machine learning, form an attractive platform to build improved risk metrics because they can easily incorporate disparate pieces of data, yielding classifiers with improved performance. Using data from more than 5200 patients admitted with a non-ST segment elevation acute coronary syndrome we constructed an artificial neural network that identifies patients at high risk of cardiovascular death 1-year after the index event. We further demonstrate how q-learning can be used to find optimal treatment strategies for patients at high risk of death after an acute coronary syndrome.

Panel Discussion & Closing Remarks

Program Chairs

Assistant Professor in Computer Science, Harvard School of Engineering and Applied Sciences
Associate Professor Departments of Anesthesiology/Critical Care Medicine and Pediatrics Johns Hopkins University School of Medicine
PhD Student, Computer Science, Viterbi Dean's Doctoral Fellow, and Alfred E. Mann Innovation in Engineering Fellow at the University of Southern California
Assistant professor in Computer and Information Science at Northeastern University, Boston, MA
Assistant Professor of Computer Science and Engineering (CSE) at the University of Michigan

Senior Advisory Committee:

Dean of the College of Computer and Information Science, Northeastern University
Associate Professor and Canada Research Chair in Computational Biology, University of Toronto
Associate Professor, Biomedical Informatics Emory University
Associate Professor of Biomedical Informatics, Affiliated with Computer Science, Columbia University
Professor of Computer Science at Cornell Tech in New York City and a Professor of Public Health at Weill Cornell Medical College
Schlumberger Centennial Chair Professor of Electrical and Computer Engineering at The University of Texas at Austin
Professor of Computer Science at the University of Alberta
Dugald C. Jackson Professor MIT Department of Electrical Engineering and Computer Science
Professor of Computer Science, University of Pittsburgh
Technical Fellow and Managing Director, Microsoft Research
Lawrence J. Henderson Professor of Pediatrics, Boston Childrens Hospital
HST Faculty, Distinguished Professor in Health Sciences and Technology and Electrical Engineering and Computer Science, Massachusetts Institute of Technology
Professor of Medicine, Biomedical Engineering and Molecular Physiology and Biological Physics
Professor of Computer Science at the University of British Columbia
Senior Lecturer in Computer Science at Makerere University
Associate Professor at UC Riverside's Computer Science Department
Professor of Computer Science and Engineering in the MIT Department of Electrical Engineering and Computer Science
Associate Professor, Medicine - Biomedical Informatics Research, Stanford University
Founder’s Board Chair of Neurocritical Care, Professor in Pediatrics-Neurology, Neurology - Ken and Ruth Davee Department and Pharmacology, Northwestern
Chairman, Department of Anesthesiology Critical Care Medicine - Children's Hospital Los Angeles
Professor of Machine Learning, School of Informatics, University of Edinburgh

Accepted Papers

Jeremy Weiss*, Carnegie Mellon University
Amirreza Farnoosh, Northeastern University; Mehrdad Nourani, University of Texas at Dallas; Sarah Ostadabbas*, Northeastern University
Savannah Bergquist*, Harvard University; Gabriel Brooks, Dartmouth-Hitchcock Medical Center; Nancy Keating, Harvard Medical School, Brigham and Women's Hospital; Mary Beth Landrum, Harvard Medical School; Sherri Rose, Harvard Medical School
JosŽ Forte*, University of Groningen; Marco Wiering, University of Groningen; Hjalmar Bouma, University Medical Center Groningen; Fred de Geus, University Medical Center Groningen; Anne Epema, University Medical Center Groningen
Madalina Fiterau*, Stanford University; Suvrat Bhooshan, Stanford University; Jason Fries, Stanford University; Charles Bournhonesque, Stanford University; Jennifer Hicks, Stanford University; Eni Halilaj, Stanford University; Christopher Re, Stanford University; Scott Delp, Stanford University
Albert Haque*, Stanford University; Michelle Guo, Stanford University; Alexandre Alahi, Stanford University; Amit Singh, Lucile Packard Children's Hospital; Serena Yeung, Stanford University; N. Lance Downing, Stanford; Terry Platchek, Lucile Packard Children's Hospital; Li Fei-Fei, Stanford University
Hei Law*, University of Michigan; Jia Deng, University of Michigan, Ann Arbor; Khurshid Ghani, University of Michigan
Zachary Lipton*, UCSD; Nathan Ng, UCSD; Rodney Gabriel , UCSD; Charles Elkan, UCSD; Julian McAuley, UC San Diego
ESamuele Fiorini, University of Genoa; Andrea Tacchino, Italian Multiple Sclerosis Foundation - Scientific Research Area; Giampaolo Brichetto, Italian Multiple Sclerosis Foundation - Scientific Research Area; Alessandro Verri, University of Genova, Italy; Annalisa Barla*, Universitˆ degli Studi di Genova
Matteo Ruffini*, UPC; Ricard Gavaldˆ, UPC; Esther Lim—n, Institut Catalˆ de la Salut
Aniruddh Raghu*, MIT; Marzyeh Ghassemi, MIT; Matthieu Komorowski, Imperial College London; Leo Celi, MIT; Pete Szolovits, MIT
Yinchong Yang*, Siemens AG, LMU MŸnchen; Volker Tresp, Siemens AG and Ludwig Maximilian University of Munich ; Peter Fasching, Department of Gynecology and Obstetrics, University Hospital Erlangen
Yujia Bao*, University of Wisconsin-Madison; Zhaobin Kuang, University of Wisconsin, Madison; Peggy Peissig, Marshfield Clinic Research Foundation; David Page, University of Wisconsin, Madison; Rebecca Willett, University of Wisconsin, Madison
Bryan Conroy*, Philips Research North America; Minnan Xu-Wilson, Philips Research North America; Asif Rahman, Philips Reserach
Ronnachai Jaroensri*, MIT CSAIL; Amy Zhao, MIT; Fredo Durand, MIT; John Guttag, MIT; Jeremy Schmahmann, Massachusetts General Hospital; Guha Balakrishnan, MIT; Derek Lo, Yale University
Maria Jahja*, North Carolina State University; Daniel Lizotte, UWO
Elizabeth C. Lorenzi, Stephanie L. Brown, Zhifei Sun, and Katherine Heller
Joseph Futoma, Sanjay Hariharan, Katherine Heller, Mark Sendak, Nathan Brajer, Meredith Clement, Armando Bedoya, and Cara O'Brien
Kazi Islam*, UC Riverside; Christian Shelton, UC Riverside
Yuan Ling, Philips Research North America; Sadid A. Hasan*, Philips Research North America; Vivek Datla, Philips Research North America; Ashequl Qadir, Philips Research North America; Kathy Lee, Philips Research North America; Joey Liu, Philips Research North America; Oladimeji Farri, Philips Research North America
Edward Choi*, Georgia Institute of Technology; Siddharth Biswal, Georgia Institute of Technology; Bradley Malin, Vanderbilt University; Jon Duke, Georgia Institute of Technology; Walter Stewart, Sutter Health; Jimeng Sun, CS
Silvio Moreira*, INESC-ID; Glen Copperfield,; Paula Carvalho, INESC-ID; M‡rio Silva, INESC-ID; Byron Wallace, Northeastern
Nathan Hunt*, MIT; Marzyeh Ghassemi, MIT; Harini Suresh, MIT; Pete Szolovits, MIT; Leo Celi, MIT; Alistair Johnson, MIT
Arya Pourzanjani*, UCSB; Tie Bo Wu, UCSB; Richard M. Jiang, UCSB; Mitchell J. Cohen, Denver Health Medical Center; Linda R. Petzold, UCSB
Zihan Wang*, University of Toronto; Michael Brudno, U Turonto; Orion Buske, Centre for Computational Medicine, SickKids Hospital
Alistair Johnson*, MIT; Tom Pollard, MIT; Roger Mark, MIT

Accepted Clinical Abstracts

Vasua Chandrasekaran, Jinghua He, Monica Reed Chase, Aman Bhandari, Christopher Frederick, and Paul Dexter
Anasuya Das, Leifur Thorbergsson, Aleksandr Grigorenko, David Sontag, Iker Huerga
Adam Perer*, IBM Research; Bum Chul Kwon, IBM Research; Janu Verma, IBM Research; Kenney Ng, IBM Research; Ben Eysenbach, MIT; Christopher deFilippi, INOVA; Walter Stewart, Sutter Health
Manuel Martinello, Harshavardhan Binnamangalam, Philip Hofstetter, John Kokesh, Samantha Kleindienst, Tiffany Romain, Noah Bedard, and Ivana Tosic



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