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Electronic health record analysis via deep poisson factor models

Publication ,  Journal Article
Henao, R; Lu, JT; Lucas, JE; Ferranti, J; Carin, L
Published in: Journal of Machine Learning Research
April 1, 2016

Electronic Health Record (EHR) phenotyping utilizes patient data captured through normal medical practice, to identify features that may represent computational medical phenotypes. These features may be used to identify at-risk patients and improve prediction of patient morbidity and mortality. We present a novel deep multi-modality architecture for EHR analysis (applicable to joint analysis of multiple forms of EHR data), based on Poisson Factor Analysis (PFA) modules. Each modality, composed of observed counts, is represented as a Poisson distribution, parameterized in terms of hidden binary units. Information from different modalities is shared via a deep hierarchy of common hidden units. Activation of these binary units occurs with probability characterized as Bernoulli-Poisson link functions, instead of more traditional logistic link functions. In addition, we demonstrate that PFA modules can be adapted to discriminative modalities. To compute model parameters, we derive efficient Markov Chain Monte Carlo (MCMC) inference that scales efficiently, with significant computational gains when compared to related models based on logistic link functions. To explore the utility of these models, we apply them to a subset of patients from the Duke-Durham patient cohort. We identified a cohort of over 16,000 patients with Type 2 Diabetes Mellitus (T2DM) based on diagnosis codes and laboratory tests out of our patient population of over 240,000. Examining the common hidden units uniting the PFA modules, we identify patient features that represent medical concepts. Experiments indicate that our learned features are better able to predict mortality and morbidity than clinical features identified previously in a large-scale clinical trial.

Duke Scholars

Published In

Journal of Machine Learning Research

EISSN

1533-7928

ISSN

1532-4435

Publication Date

April 1, 2016

Volume

17

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 4905 Statistics
  • 4611 Machine learning
  • 17 Psychology and Cognitive Sciences
  • 08 Information and Computing Sciences
 

Citation

APA
Chicago
ICMJE
MLA
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Henao, R., Lu, J. T., Lucas, J. E., Ferranti, J., & Carin, L. (2016). Electronic health record analysis via deep poisson factor models. Journal of Machine Learning Research, 17.
Henao, R., J. T. Lu, J. E. Lucas, J. Ferranti, and L. Carin. “Electronic health record analysis via deep poisson factor models.” Journal of Machine Learning Research 17 (April 1, 2016).
Henao R, Lu JT, Lucas JE, Ferranti J, Carin L. Electronic health record analysis via deep poisson factor models. Journal of Machine Learning Research. 2016 Apr 1;17.
Henao, R., et al. “Electronic health record analysis via deep poisson factor models.” Journal of Machine Learning Research, vol. 17, Apr. 2016.
Henao R, Lu JT, Lucas JE, Ferranti J, Carin L. Electronic health record analysis via deep poisson factor models. Journal of Machine Learning Research. 2016 Apr 1;17.

Published In

Journal of Machine Learning Research

EISSN

1533-7928

ISSN

1532-4435

Publication Date

April 1, 2016

Volume

17

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 4905 Statistics
  • 4611 Machine learning
  • 17 Psychology and Cognitive Sciences
  • 08 Information and Computing Sciences