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Machine Learning for Clinical Data Analysis and Healthcare
Jenna Wiens · Finale P Doshi-Velez · Can Ye · Madalina Fiterau · Shipeng Yu · Le Lu · Balaji R Krishnapuram

Tue Dec 10 07:30 AM -- 06:30 PM (PST) @ Harvey's Tallac
Event URL: http://goo.gl/lpgjyN »

Advances in medical information technology have resulted in enormous warehouses of data that are at once 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 labeling and clustering, clinical outcome prediction, efficient and scalable processing of medical records, feature selection or dimensionality reduction in clinical data, tools for personalized medicine, and time-series analysis with medical applications.

Author Information

Jenna Wiens (Massachusetts Institute of Technology)
Finale P Doshi-Velez (Harvard)
Can Ye (CMU)
Madalina Fiterau (UMass Amherst)

Madalina Fiterau is an Assistant Professor at the College of College of Information and Computer Sciences at UMass Amherst, with a focus on AI/ML. Previously, she was a Postdoctoral Fellow in the Computer Science Department at Stanford University, working with Professors Chris Ré and Scott Delp in the Mobilize Center. Madalina has obtained a PhD in Machine Learning from Carnegie Mellon University in September 2015, advised by Professor Artur Dubrawski. The focus of her PhD thesis, entitled “Discovering Compact and Informative Structures through Data Partitioning”, is learning interpretable ensembles, with applicability ranging from image classification to a clinical alert prediction system. Madalina is currently expanding her research on interpretable models, in part by applying deep learning to obtain salient representations from biomedical “deep” data, including time series, text and images. Madalina is the recipient of the GE Foundation Scholar Leader Award for Central and Eastern Europe. She is the recipient of the Marr Prize for Best Paper at ICCV 2015 and of Star Research Award at the Annual Congress of the Society of Critical Care Medicine 2016. She has organized two editions of the Machine Learning for Clinical Data Analysis Workshop at NIPS, in 2013 and 2014.

Shipeng Yu (Siemens)
Le Lu (National Institutes of Health)
Balaji R Krishnapuram (Siemens Medical Solutions USA, Inc.)

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