Connectionists: NIPS 2014 Workshop on Machine Learning for Clinical Data, Healthcare and Genomics

[Julia Vogt]

Please join us for the NIPS 2014 Workshop on Machine Learning for Clinical Data, Healthcare and Genomics



When: Dec. 12th 2014, 8am-6:30pm

Where:  Palais des congrès de Montréal, Level 5; room 511 f., Montreal, Quebec, Canada, 

Workshop Website: http://www.ml4chg.org/



Schedule:


Morning theme:

Specialized models for structure recovery from clinical datasets

08:00-08:25 AM

Poster setup

08:25-08:35 AM

Introduction

08:35-09:30 AM

Opening talk: John Mattison, Kaiser Permanente

09:30-10:00 AM

Invited talk: David Sontag, New York University

10:00-10:30 AM

Coffee Break (and posters)

10:30-11:00 AM

Round table discussions

11:00-11:30 AM

Invited talk: Gilles Clermont, University of Pittsburgh

11:30-12:00 PM

Invited talk: Chris Williams, University of Edinburgh

12:00-03:00 PM

Lunch Break

Afternoon theme:

Clinical Genomics and Precision Medicine

3:00-3:30 PM

Invited talk: Michal Rosen-Zvi, IBM Research

3:30-4:00 PM

Invited talk: Michael Brudno, University of Toronto

4:00-4:30 PM

Poster Session

4:30-5:00 PM

Coffee Break (and posters)

5:00-5:30 PM

Invited talk: Suchi Saria, Johns Hopkins University

5:30-6:30 PM

Precision Medicine: How to make it work? (Discussion Panel)

Abstract:

Advances in medical information technology have resulted in enormous warehouses of data that are both overwhelming and sparse. A single patient visit may result in tens to thousands of measurements and structured information, including clinical factors, diagnostic imaging, lab tests, genomic and proteomic tests. Hospitals may see thousands of patients each year. However, each patient may have relatively few visits to any particular medical provider. The resulting data are a heterogeneous amalgam of patient demographics, vital signs, diagnoses, records of treatment and medication receipt and annotations made by nurses or doctors, each with its own idiosyncrasies.

The objective of this workshop is to discuss how advanced machine learning techniques can derive clinical and scientific impact from these messy, incomplete, and partial data. We will bring together machine learning researchers and experts in medical informatics who are involved in the development of algorithms or intelligent systems designed to improve quality of healthcare. Relevant areas include health monitoring systems, clinical data labelling and clustering, clinical outcome prediction, efficient and scalable processing of medical records, feature selection or dimensionality reduction in clinical data, tools for personalized medicine,  time-series analysis with medical applications and clinical genomics.
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