An Adversarial Domain Separation Framework for Septic Shock Early Prediction Across EHR Systems

10/26/2020
by   Farzaneh Khoshnevisan, et al.
0

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.

READ FULL TEXT
research
10/10/2021

Toward a multimodal multitask model for neurodegenerative diseases diagnosis and progression prediction

Recent studies on modelling the progression of Alzheimer's disease use a...
research
11/30/2020

Heuristic Domain Adaptation

In visual domain adaptation (DA), separating the domain-specific charact...
research
08/14/2020

A Dynamic Deep Neural Network For Multimodal Clinical Data Analysis

Clinical data from electronic medical records, registries or trials prov...
research
06/05/2018

Mixed Effect Composite RNN-GP: A Personalized and Reliable Prediction Model for Healthcare

We present a personalized and reliable prediction model for healthcare, ...
research
07/16/2020

Transferable Calibration with Lower Bias and Variance in Domain Adaptation

Domain Adaptation (DA) enables transferring a learning machine from a la...
research
05/06/2020

Deep Recurrent Disease Progression Model for Conversion-Time Prediction of Alzheimer's Disease

Alzheimer's disease (AD) is known as one of the major causes of dementia...
research
01/11/2022

DANNTe: a case study of a turbo-machinery sensor virtualization under domain shift

We propose an adversarial learning method to tackle a Domain Adaptation ...

Please sign up or login with your details

Forgot password? Click here to reset