Machine Learning for Health @ NIPS 2016
This workshop will bring together machine learning researchers, clinicians and healthcare data experts. The program consists of invited talks, contributions and clinical challenges. Please see our detailed workshop description, program committee and organizers.

Keynote Videos
Part 1
Part 2
Part 3
Part 4
Part 5

Please direct questions to:

Workshop Location: Room 116

8:15 ­- 8:25: Introduction

8:25 ­- 9:10: Opening Keynote by Leo Anthony Celi (45 min)

9:10 ­- 9:40: Eric Xing (30 min)

9:40 - 10:30: Contributed spotlights (50 min for a total of 10)

    Differential Data Augmentation Techniques for Medical Imaging Classification Tasks
    Zeshan Hussain, Francisco Gimenez, Darvin Yi and Daniel Rubin.
    Multi-task Learning for Predicting Health, Stress, and Happiness
    Natasha Jaques, Sara Taylor, Ehimwenma Nosakhare, Akane Sano and Rosalind Picard.

    Learning to count and classify mosquitoes for the Sterile Insect Technique
    Yaniv Ovadia, Yoni Halpern, Dilip Krishnan, Josh Livni, Daniel Newburger, Ryan Poplin, Tiantian Zha and D. Sculley.

    Stratified Locality-Sensitive Hashing for Sublinear Time Critical Event Prediction
    Y. Bryce Kim, Erik Hemberg and Una-May O'Reilly.
    Towards multiple kernel principal component analysis for integrative analysis of tumor samples
    Nora K. Speicher and Nico Pfeifer.

    Variational Adversarial Deep Domain Adaptation for Health Care Time Series Analysis
    Sanjay Purushotham, Wilka Carvalho and Yan Liu.
    Fast and Efficient Feature Engineering for Multi-Cohort Analysis of EHR Data
    Michal Ozery-Flato, Chen Yanover, Assaf Gottlieb, Omer Weissbrod, Naama Parush Shear-Yashuv and Yaara Goldschmidt.

    Probabilistic intensity normalization of PET/SPECT images using Variational mixture of Gamma distributions.
    Alberto Llera, Ismael Huertas, Pablo Mir and Christian F. Beckmann.

    Semi-Supervised Sequence Learning for Continuous Digital Biomarkers
    Brandon Ballinger, Johnson Hsieh, Nimit Sohoni, Greg Marcus, Jose Sanchez, Geoff Tison and Jeff Olgin.

    Combining Kernel and Model Based Learning for HIV Therapy Selection
    Sonali Parbhoo, Volker Roth and Finale Doshi-Velez.

10:30 ­-11:15: Coffee break and Poster Session I (30 min)

11:15 -­ 11:30: Award session I: a word from the sponsors (30 min)

11:30 ­- 12:00: Clinician pitches & discussion I (for a total of 3)

    Data driven techniques to optimise the management of septic shock
    Matthieu Komorowski.

    Machine Learning in Textual Data of Cardiovascular Disease via Phrase Mining and Network Embedding
    David Liem, Doris Xin, Vincent Kyi, Quan Cao, Leah Briscoe, Fangbo Tao, Karol Watson, Jiawei Han and Peipei Ping.

    Challenges and preliminary outcomes of real world data analytics based on the national e-prescription of Greece: The use case of Diabetes Mellitus
    George E. Dafoulas, Stavros Liatis, Petros P. Sfikakis and Konstantinos Makrilakis. 


1:45  2:30: Keynote: Neil Lawrence (45 min)

2:30  - 2:45: Award session II: a word from the sponsors (30 min)

2:45 - 3:30: Coffee break and Poster Session II (30 min)

3:30 ­ 4:00: Niels Peek (30 min)

4:00 ­ 4:30: Sendhil Mullainathan (30 min)

4:30 ­ 5:00: Poster session III (30 min)

5:00  5:30: Jenna Wiens (30 min)

Important Dates

September 22, 2016 Travel Award Submission Deadline  
October 1, 2016 Round 1 Early Submission Deadline  
October 3, 2016 Round 1 Acceptance Notification  
October 6, 2016 NIPS Early Registration Deadline  
October 31, 2016 Round 2 Submission Deadline EXTENSION  
November 10, 2016 Clinician Pitch Submission Deadline  
November 15, 2016 Acceptance Notification for Papers and Clinician Pitches  
December 1, 2016 Final camera ready papers due  
December 9, 2016 Workshop  
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  • Call for Papers NIPS 2016 Workshop on Machine Learning for Health (NIPS ML4HC) workshop at the Twenty-Ninth Annual Conference on Neural Information Processing Systems (NIPS 2016) in ...
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