Effective sparse imputation of patient conditions in electronic medical records for emergency risk predictions

Effective sparse imputation of patient conditions in electronic medical records for emergency... Electronic medical records (EMRs) are being increasingly used for “risk” prediction. By “risks,” we denote outcomes such as emergency presentation, readmission, and the length of hospitalizations. However, EMR data analysis is complicated by missing entries. There are two reasons—the “primary reason for admission” is included in EMR, but the comorbidities (other chronic diseases) are left uncoded, and many zero values in the data are accurate, reflecting that a patient has not accessed medical facilities. A key challenge is to deal with the peculiarities of this data—unlike many other datasets, EMR is sparse, reflecting the fact that patients have some but not all diseases. We propose a novel model to fill-in these missing values and use the new representation for prediction of key hospital events. To “fill-in” missing values, we represent the feature-patient matrix as a product of two low-rank factors, preserving the sparsity property in the product. Intuitively, the product regularization allows sparse imputation of patient conditions reflecting common comorbidities across patients. We develop a scalable optimization algorithm based on Block coordinate descent method to find an optimal solution. We evaluate the proposed framework on two real-world EMR cohorts: Cancer (7000 admissions) and Acute Myocardial Infarction (2652 admissions). Our result shows that the AUC for 3-month emergency presentation prediction is improved significantly from (0.729 to 0.741) for Cancer data and (0.699 to 0.723) for AMI data. Similarly, AUC for 3-month emergency admission prediction from (0.730 to 0.752) for Cancer data and (0.682 to 0.724) for AMI data. We also extend the proposed method to a supervised model for predicting multiple related risk outcomes (e.g., emergency presentations and admissions in hospital over 3, 6, and 12 months period) in an integrated framework. The supervised model consistently outperforms state-of-the-art baseline methods. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Knowledge and Information Systems Springer Journals

Effective sparse imputation of patient conditions in electronic medical records for emergency risk predictions

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Publisher
Springer London
Copyright
Copyright © 2017 by Springer-Verlag London
Subject
Computer Science; Information Systems and Communication Service; IT in Business
ISSN
0219-1377
eISSN
0219-3116
D.O.I.
10.1007/s10115-017-1038-0
Publisher site
See Article on Publisher Site

Abstract

Electronic medical records (EMRs) are being increasingly used for “risk” prediction. By “risks,” we denote outcomes such as emergency presentation, readmission, and the length of hospitalizations. However, EMR data analysis is complicated by missing entries. There are two reasons—the “primary reason for admission” is included in EMR, but the comorbidities (other chronic diseases) are left uncoded, and many zero values in the data are accurate, reflecting that a patient has not accessed medical facilities. A key challenge is to deal with the peculiarities of this data—unlike many other datasets, EMR is sparse, reflecting the fact that patients have some but not all diseases. We propose a novel model to fill-in these missing values and use the new representation for prediction of key hospital events. To “fill-in” missing values, we represent the feature-patient matrix as a product of two low-rank factors, preserving the sparsity property in the product. Intuitively, the product regularization allows sparse imputation of patient conditions reflecting common comorbidities across patients. We develop a scalable optimization algorithm based on Block coordinate descent method to find an optimal solution. We evaluate the proposed framework on two real-world EMR cohorts: Cancer (7000 admissions) and Acute Myocardial Infarction (2652 admissions). Our result shows that the AUC for 3-month emergency presentation prediction is improved significantly from (0.729 to 0.741) for Cancer data and (0.699 to 0.723) for AMI data. Similarly, AUC for 3-month emergency admission prediction from (0.730 to 0.752) for Cancer data and (0.682 to 0.724) for AMI data. We also extend the proposed method to a supervised model for predicting multiple related risk outcomes (e.g., emergency presentations and admissions in hospital over 3, 6, and 12 months period) in an integrated framework. The supervised model consistently outperforms state-of-the-art baseline methods.

Journal

Knowledge and Information SystemsSpringer Journals

Published: Mar 18, 2017

References

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