NDLI: Impact of Density of Lab Data in EHR for Prediction of Potentially Preventable Events

Content Provider	IEEE Xplore Digital Library
Author	Sarkar, C. Srivastava, J.
Copyright Year	2013
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Univ. of Minnesota, Minneapolis, MN, USA (Sarkar, C.; Srivastava, J.)
Abstract	This paper presents an analysis of sparse and incomplete Electronic Health Record (EHR) data for the prediction of patients with the risk of Potentially Preventable Events (PPEs). PPEs are admissions, readmissions, complications and emergency department visits that could have been avoided if the patient had been given the appropriate interventions. Machine learning techniques have made the task of PPE detection less difficult. However, it is still a challenging task due to the sparse and incomplete nature of the EHR data. It is therefore important to investigate the factors that impact the prediction of PPE in EHR data. In this paper we define the term density for evaluating sparse and incomplete nature of the EHR data set. We analyze three important factors that impacts PPE prediction in sparse and incomplete EHR data. These factors are - 1) Effect of varying domain information in the patient records on PPE prediction, 2) Impact of a popular data mining pre-processing technique known as rank aggregation based feature selection on PPE prediction, and 3) Effect of ensemble classification on prediction of PPE. The results of the analysis indicate that the rank aggregation based feature selection technique and ensemble classification improves classification accuracy by approximately 3-4\% despite of the sparse and incomplete nature of the data. However, eliminating patient records with less domain information, in order to reduce incompleteness in the data, does not cause an enhancement in the classification accuracy. We conclude that feature selection and ensemble classification techniques are important factors that affect classification accuracy even in sparse and incomplete data sets. We conclude as well that randomly decreasing domain information by varying lab values does not assist in increasing accuracy for the prediction of PPE.
Starting Page	529
Ending Page	534
File Size	334046
Page Count	6
File Format	PDF
ISBN	9780769550893
DOI	10.1109/ICHI.2013.82
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-09
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sparse and incomplete data Accuracy Feature selection Domain information Potentially preventable events Predictive models Feature extraction Prediction algorithms Data models Ensemble classification Data mining Decision trees
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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