MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health Records

by   Xi Sheryl Zhang, et al.

In recent years, increasingly augmentation of health data, such as patient Electronic Health Records (EHR), are becoming readily available. This provides an unprecedented opportunity for knowledge discovery and data mining algorithms to dig insights from them, which can, later on, be helpful to the improvement of the quality of care delivery. Predictive modeling of clinical risk, including in-hospital mortality, hospital readmission, chronic disease onset, condition exacerbation, etc., from patient EHR, is one of the health data analytic problems that attract most of the interests. The reason is not only because the problem is important in clinical settings, but also there are challenges working with EHR such as sparsity, irregularity, temporality, etc. Different from applications in other domains such as computer vision and natural language processing, the labeled data samples in medicine (patients) are relatively limited, which creates lots of troubles for effective predictive model learning, especially for complicated models such as deep learning. In this paper, we propose MetaPred, a meta-learning for clinical risk prediction from longitudinal patient EHRs. In particular, in order to predict the target risk where there are limited data samples, we train a meta-learner from a set of related risk prediction tasks which learns how a good predictor is learned. The meta-learned can then be directly used in target risk prediction, and the limited available samples can be used for further fine-tuning the model performance. The effectiveness of MetaPred is tested on a real patient EHR repository from Oregon Health & Science University. We are able to demonstrate that with CNN and RNN as base predictors, MetaPred can achieve much better performance for predicting target risk with low resources comparing with the predictor trained on the limited samples available for this risk.


Time Associated Meta Learning for Clinical Prediction

Rich Electronic Health Records (EHR), have created opportunities to impr...

Short-term Mortality Prediction for Elderly Patients Using Medicare Claims Data

Risk prediction is central to both clinical medicine and public health. ...

Analysis of Risk Factor Domains in Psychosis Patient Health Records

Readmission after discharge from a hospital is disruptive and costly, re...

Multimodal spatiotemporal graph neural networks for improved prediction of 30-day all-cause hospital readmission

Measures to predict 30-day readmission are considered an important quali...

Precisely Predicting Acute Kidney Injury with Convolutional Neural Network Based on Electronic Health Record Data

The incidence of Acute Kidney Injury (AKI) commonly happens in the Inten...

Explainable Health Risk Predictor with Transformer-based Medicare Claim Encoder

In 2019, The Centers for Medicare and Medicaid Services (CMS) launched a...

Learning to Prescribe Interventions for Tuberculosis Patients using Digital Adherence Data

Digital Adherence Technologies (DATs) are an increasingly popular method...

Please sign up or login with your details

Forgot password? Click here to reset