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PMOHR SIGNED

Probabilistic modelling of electronic health records

Total Cost €

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EC-Contrib. €

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Partnership

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Project "PMOHR" data sheet

The following table provides information about the project.

Coordinator
THE CHANCELLOR MASTERS AND SCHOLARSOF THE UNIVERSITY OF CAMBRIDGE 

Organization address
address: TRINITY LANE THE OLD SCHOOLS
city: CAMBRIDGE
postcode: CB2 1TN
website: www.cam.ac.uk

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country United Kingdom [UK]
 Project website http://franrruiz.github.io/pmohr.html
 Total cost 269˙857 €
 EC max contribution 269˙857 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2015
 Funding Scheme MSCA-IF-GF
 Starting year 2016
 Duration (year-month-day) from 2016-10-01   to  2019-09-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    THE CHANCELLOR MASTERS AND SCHOLARSOF THE UNIVERSITY OF CAMBRIDGE UK (CAMBRIDGE) coordinator 269˙857.00
2    TRUSTEES OF COLUMBIA UNIVERSITY IN THE CITY OF NEW YORK US (NEW YORK) partner 0.00

Map

 Project objective

The growing worldwide adoption of Electronic Health Records (EHR) enables new research opportunities to analyse massive amounts of medical information, motivated by the promise of improving health systems while providing significant budget savings. Biomedical research increasingly uses machine learning methods as a data-driven approach to learn complex comorbidity patterns of diseases, study drug interactions, and form predictions. The analysis of EHRs may not only lead to knowledge discovery, but it also facilitates personalised medical treatment and early diagnosis of the diseases through the design of clinical support systems.

However, current approaches for the analysis of EHRs are still in their early stages. The two main technical challenges that need to be addressed are integration of heterogeneous data and scalability to massive datasets. Most of the existing methods are tailored to homogeneous data and, therefore, to a single source of information, and hence they cannot handle EHR datasets. Scalability also represents a difficulty for most of the current machine learning techniques, which are limited to the analysis to moderate-sized datasets.

In this project, we will develop novel tools for the analysis of heterogeneous EHR data. Our approach will be based on probabilistic modelling techniques, since they are an effective approach for understanding real-world data in many areas of science. We will make use of Bayesian nonparametric modelling techniques, coupled with stochastic variational inference to allow for scalable inference. Probabilistic models, including BNPs, are amenable to both descriptive and predictive analysis at the same time. We will collaborate with the Department of Biomedical Informatics, who will provide their knowledge about the problem, allowing for good model formulations and results analysis.

 Publications

year authors and title journal last update
List of publications.
2019 Francisco J. R. Ruiz, Michalis K. Titsias
A Contrastive Divergence for Combining Variational Inference and MCMC
published pages: , ISSN: , DOI:
2020-04-06
2019 Francisco J. R. Ruiz, Susan Athey, David M. Blei
SHOPPER: A Probabilistic Model of Consumer Choice with Substitutes and Complements
published pages: , ISSN: , DOI:
Annals of Applied Statistics 2020-04-06
2017 Maryam Fatemi, Karl Granstrom, Lennart Svensson, Francisco J. R. Ruiz, Lars Hammarstrand
Poisson Multi-Bernoulli Mapping Using Gibbs Sampling
published pages: 2814-2827, ISSN: 1053-587X, DOI: 10.1109/TSP.2017.2675866
IEEE Transactions on Signal Processing 65/11 2020-04-06
2017 Maja Rudolph; Francisco J. R. Ruiz; David M. Blei
Word2Net: Deep Representations of Language
published pages: , ISSN: , DOI:
NIPS Workshop on Bayesian Deep Learning 2020-04-06
2018 Francisco J. R. Ruiz, Isabel Valera, Lennart Svensson, Fernando Perez-Cruz
Infinite Factorial Finite State Machine for Blind Multiuser Channel Estimation
published pages: 177-191, ISSN: 2332-7731, DOI: 10.1109/TCCN.2018.2790976
IEEE Transactions on Cognitive Communications and Networking 4/2 2020-04-06
2017 Francisco J. R. Ruiz; Michalis K. Titsias; David M. Blei
Scalable Large-Scale Classification with Latent Variable Augmentation
published pages: , ISSN: , DOI:
NIPS Workshop on Advances in Approximate Bayesian Inference 2020-04-06
2018 Susan Athey, David Blei, Robert Donnelly, Francisco Ruiz, Tobias Schmidt
Estimating Heterogeneous Consumer Preferences for Restaurants and Travel Time Using Mobile Location Data
published pages: 64-67, ISSN: 0000-0000, DOI: 10.1257/pandp.20181031
AEA Papers and Proceedings 108 2020-04-06
2018 Ruiz, Francisco J. R.; Titsias, Michalis K.; Dieng, Adji B.; Blei, David M.
Augment and Reduce: Stochastic Inference for Large Categorical Distributions
published pages: , ISSN: , DOI:
International Conference on Machine Learning 2020-04-06
2017 Liping Liu; Francisco J. R. Ruiz; Susan Athey; David M. Blei
Context Selection for Embedding Models
published pages: , ISSN: , DOI:
Advances in Neural Information Processing Systems 2020-04-06
2019 Michalis K. Titsias, Francisco J. R. Ruiz
Unbiased Implicit Variational Inference
published pages: , ISSN: , DOI:
International Conference on Artificial Intelligence and Statistics 2020-04-06
2019 Hanna Mendes Levitin, Jinzhou Yuan, Yim Ling Cheng, Francisco JR Ruiz, Erin C Bush, Jeffrey N Bruce, Peter Canoll, Antonio Iavarone, Anna Lasorella, David M Blei, Peter A Sims
De novo gene signature identification from single‐cell RNA‐seq with hierarchical Poisson factorization
published pages: e8557, ISSN: 1744-4292, DOI: 10.15252/msb.20188557
Molecular Systems Biology 15/2 2020-04-06
2016 Maja Rudolph, Francisco J. R. Ruiz, Stephan Mandt, David M. Blei
Exponential Family Embeddings
published pages: , ISSN: , DOI:
Advances in Neural Information Processing Systems 2020-04-06
2017 Rudolph, Maja; Ruiz, Francisco; Athey, Susan; Blei, David
Structured Embedding Models for Grouped Data
published pages: , ISSN: , DOI:
Advances in Neural Information Processing Systems 2020-04-06
2016 Francisco J. R. Ruiz, Michalis K. Titsias, David M. Blei
The Generalized Reparameterization Gradient
published pages: , ISSN: , DOI:
Advances in Neural Information Processing Systems 2020-04-06
2017 Christian A. Naesseth, Francisco J. R. Ruiz, Scott W. Linderman, and David M. Blei
Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms
published pages: 489-498, ISSN: , DOI:
International Conference on Artificial Intelligence and Statistics 54 2020-04-06

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