2 research outputs found
An Adversarial Domain Separation Framework for Septic Shock Early Prediction Across EHR Systems
Modeling patient disease progression using Electronic Health Records (EHRs)
is critical to assist clinical decision making. While most of prior work has
mainly focused on developing effective disease progression models using EHRs
collected from an individual medical system, relatively little work has
investigated building robust yet generalizable diagnosis models across
different systems. In this work, we propose a general domain adaptation (DA)
framework that tackles two categories of discrepancies in EHRs collected from
different medical systems: one is caused by heterogeneous patient populations
(covariate shift) and the other is caused by variations in data collection
procedures (systematic bias). Prior research in DA has mainly focused on
addressing covariate shift but not systematic bias. In this work, we propose an
adversarial domain separation framework that addresses both categories of
discrepancies by maintaining one globally-shared invariant latent
representation across all systems} through an adversarial learning process,
while also allocating a domain-specific model for each system to extract local
latent representations that cannot and should not be unified across systems.
Moreover, our proposed framework is based on variational recurrent neural
network (VRNN) because of its ability to capture complex temporal dependencies
and handling missing values in time-series data. We evaluate our framework for
early diagnosis of an extremely challenging condition, septic shock, using two
real-world EHRs from distinct medical systems in the U.S. The results show that
by separating globally-shared from domain-specific representations, our
framework significantly improves septic shock early prediction performance in
both EHRs and outperforms the current state-of-the-art DA models.Comment: to be published in 2020 IEEE International Conference on Big Dat
Bayesian Learning of LF-MMI Trained Time Delay Neural Networks for Speech Recognition
Discriminative training techniques define state-of-the-art performance for
automatic speech recognition systems. However, they are inherently prone to
overfitting, leading to poor generalization performance when using limited
training data. In order to address this issue, this paper presents a full
Bayesian framework to account for model uncertainty in sequence discriminative
training of factored TDNN acoustic models. Several Bayesian learning based TDNN
variant systems are proposed to model the uncertainty over weight parameters
and choices of hidden activation functions, or the hidden layer outputs.
Efficient variational inference approaches using a few as one single parameter
sample ensure their computational cost in both training and evaluation time
comparable to that of the baseline TDNN systems. Statistically significant word
error rate (WER) reductions of 0.4%-1.8% absolute (5%-11% relative) were
obtained over a state-of-the-art 900 hour speed perturbed Switchboard corpus
trained baseline LF-MMI factored TDNN system using multiple regularization
methods including F-smoothing, L2 norm penalty, natural gradient, model
averaging and dropout, in addition to i-Vector plus learning hidden unit
contribution (LHUC) based speaker adaptation and RNNLM rescoring. Consistent
performance improvements were also obtained on a 450 hour HKUST conversational
Mandarin telephone speech recognition task. On a third cross domain adaptation
task requiring rapidly porting a 1000 hour LibriSpeech data trained system to a
small DementiaBank elderly speech corpus, the proposed Bayesian TDNN LF-MMI
systems outperformed the baseline system using direct weight fine-tuning by up
to 2.5\% absolute WER reduction.Comment: submitted to TASL