ConCare: Personalized Clinical Feature Embedding via Capturing the Healthcare Context

11/27/2019
by   Liantao Ma, et al.
35

Predicting the patient's clinical outcome from the historical electronic medical records (EMR) is a fundamental research problem in medical informatics. Most deep learning-based solutions for EMR analysis concentrate on learning the clinical visit embedding and exploring the relations between visits. Although those works have shown superior performances in healthcare prediction, they fail to explore the personal characteristics during the clinical visits thoroughly. Moreover, existing works usually assume that the more recent record weights more in the prediction, but this assumption is not suitable for all conditions. In this paper, we propose ConCare to handle the irregular EMR data and extract feature interrelationship to perform individualized healthcare prediction. Our solution can embed the feature sequences separately by modeling the time-aware distribution. ConCare further improves the multi-head self-attention via the cross-head decorrelation, so that the inter-dependencies among dynamic features and static baseline information can be effectively captured to form the personal health context. Experimental results on two real-world EMR datasets demonstrate the effectiveness of ConCare. The medical findings extracted by ConCare are also empirically confirmed by human experts and medical literature.

READ FULL TEXT

page 1

page 2

page 3

page 4

page 5

page 6

page 7

page 8

research
03/20/2019

Learning Hierarchical Representations of Electronic Health Records for Clinical Outcome Prediction

Clinical outcome prediction based on the Electronic Health Record (EHR) ...
research
12/09/2021

Context-aware Health Event Prediction via Transition Functions on Dynamic Disease Graphs

With the wide application of electronic health records (EHR) in healthca...
research
11/11/2022

Integrated Convolutional and Recurrent Neural Networks for Health Risk Prediction using Patient Journey Data with Many Missing Values

Predicting the health risks of patients using Electronic Health Records ...
research
11/24/2020

Improving Clinical Outcome Predictions Using Convolution over Medical Entities with Multimodal Learning

Early prediction of mortality and length of stay(LOS) of a patient is vi...
research
09/07/2021

Sequential Diagnosis Prediction with Transformer and Ontological Representation

Sequential diagnosis prediction on the Electronic Health Record (EHR) ha...
research
11/06/2018

CarePre: An Intelligent Clinical Decision Assistance System

Clinical decision support systems (CDSS) are widely used to assist with ...
research
07/20/2022

UniHPF : Universal Healthcare Predictive Framework with Zero Domain Knowledge

Despite the abundance of Electronic Healthcare Records (EHR), its hetero...

Please sign up or login with your details

Forgot password? Click here to reset